diff --git a/src/experiment/common.py b/src/experiment/common.py
new file mode 100644
index 0000000..621a16f
--- /dev/null
+++ b/src/experiment/common.py
@@ -0,0 +1,278 @@
+"""实验脚本的共用配置和辅助函数。
+
+此模块包含 regression.py 和 learn_to_rank.py 共用的代码，
+避免重复维护两份相同的配置和函数。
+"""
+
+from datetime import datetime
+from typing import List
+
+import polars as pl
+
+from src.factors import FactorEngine
+
+
+# =============================================================================
+# 日期范围配置（正确的 train/val/test 三分法）
+# =============================================================================
+TRAIN_START = "20200101"
+TRAIN_END = "20231231"
+VAL_START = "20240101"
+VAL_END = "20241231"
+TEST_START = "20250101"
+TEST_END = "20261231"
+
+
+# =============================================================================
+# 因子配置
+# =============================================================================
+# 当前选择的因子列表（从 FACTOR_DEFINITIONS 中选择要使用的因子）
+SELECTED_FACTORS = [
+    # ================= 1. 价格、趋势与路径依赖 =================
+    "ma_5",
+    "ma_20",
+    "ma_ratio_5_20",
+    "bias_10",
+    "high_low_ratio",
+    "bbi_ratio",
+    "return_5",
+    "return_20",
+    "kaufman_ER_20",
+    "mom_acceleration_10_20",
+    "drawdown_from_high_60",
+    "up_days_ratio_20",
+    # ================= 2. 波动率、风险调整与高阶矩 =================
+    "volatility_5",
+    "volatility_20",
+    "volatility_ratio",
+    "std_return_20",
+    "sharpe_ratio_20",
+    "min_ret_20",
+    "volatility_squeeze_5_60",
+    # ================= 3. 日内微观结构与异象 =================
+    "overnight_intraday_diff",
+    "upper_shadow_ratio",
+    "capital_retention_20",
+    "max_ret_20",
+    # ================= 4. 量能、流动性与量价背离 =================
+    "volume_ratio_5_20",
+    "turnover_rate_mean_5",
+    "turnover_deviation",
+    "amihud_illiq_20",
+    "turnover_cv_20",
+    "pv_corr_20",
+    "close_vwap_deviation",
+    # ================= 5. 基本面财务特征 =================
+    "roe",
+    "roa",
+    "profit_margin",
+    "debt_to_equity",
+    "current_ratio",
+    "net_profit_yoy",
+    "revenue_yoy",
+    "healthy_expansion_velocity",
+    # ================= 6. 基本面估值与截面动量共振 =================
+    "EP",
+    "BP",
+    "CP",
+    "market_cap_rank",
+    "turnover_rank",
+    "return_5_rank",
+    "EP_rank",
+    "pe_expansion_trend",
+    "value_price_divergence",
+    "active_market_cap",
+    "ebit_rank",
+]
+
+# 因子定义字典（完整因子库，用于存放尚未注册到metadata的因子）
+FACTOR_DEFINITIONS = {}
+
+
+def get_label_factor(label_name: str) -> dict:
+    """获取Label因子定义字典。
+
+    Args:
+        label_name: label因子名称
+
+    Returns:
+        Label因子定义字典
+    """
+    return {
+        label_name: "(ts_delay(close, -5) / ts_delay(open, -1)) - 1",
+    }
+
+
+# =============================================================================
+# 辅助函数
+# =============================================================================
+def register_factors(
+    engine: FactorEngine,
+    selected_factors: List[str],
+    factor_definitions: dict,
+    label_factor: dict,
+) -> List[str]:
+    """注册因子。
+
+    selected_factors 从 metadata 查询，factor_definitions 用 DSL 表达式注册。
+
+    Args:
+        engine: FactorEngine实例
+        selected_factors: 从metadata中选择的因子名称列表
+        factor_definitions: 通过表达式定义的因子字典
+        label_factor: label因子定义字典
+
+    Returns:
+        特征列名称列表
+    """
+    print("=" * 80)
+    print("注册因子")
+    print("=" * 80)
+
+    # 注册 SELECTED_FACTORS 中的因子（已在 metadata 中）
+    print("\n注册特征因子（从 metadata）:")
+    for name in selected_factors:
+        engine.add_factor(name)
+        print(f"  - {name}")
+
+    # 注册 FACTOR_DEFINITIONS 中的因子（通过表达式，尚未在 metadata 中）
+    print("\n注册特征因子（表达式）:")
+    for name, expr in factor_definitions.items():
+        engine.add_factor(name, expr)
+        print(f"  - {name}: {expr}")
+
+    # 注册 label 因子（通过表达式）
+    print("\n注册 Label 因子（表达式）:")
+    for name, expr in label_factor.items():
+        engine.add_factor(name, expr)
+        print(f"  - {name}: {expr}")
+
+    # 特征列 = SELECTED_FACTORS + FACTOR_DEFINITIONS 的 keys
+    feature_cols = selected_factors + list(factor_definitions.keys())
+
+    print(f"\n特征因子数: {len(feature_cols)}")
+    print(f"  - 来自 metadata: {len(selected_factors)}")
+    print(f"  - 来自表达式: {len(factor_definitions)}")
+    print(f"Label: {list(label_factor.keys())[0]}")
+    print(f"已注册因子总数: {len(engine.list_registered())}")
+
+    return feature_cols
+
+
+def prepare_data(
+    engine: FactorEngine,
+    feature_cols: List[str],
+    start_date: str,
+    end_date: str,
+    label_name: str,
+) -> pl.DataFrame:
+    """准备数据。
+
+    计算因子并返回包含特征和label的数据框。
+
+    Args:
+        engine: FactorEngine实例
+        feature_cols: 特征列名称列表
+        start_date: 开始日期 (YYYYMMDD)
+        end_date: 结束日期 (YYYYMMDD)
+        label_name: label列名称
+
+    Returns:
+        包含因子计算结果的数据框
+    """
+    print("\n" + "=" * 80)
+    print("准备数据")
+    print("=" * 80)
+
+    # 计算因子（全市场数据）
+    print(f"\n计算因子: {start_date} - {end_date}")
+    factor_names = feature_cols + [label_name]  # 包含 label
+
+    data = engine.compute(
+        factor_names=factor_names,
+        start_date=start_date,
+        end_date=end_date,
+    )
+
+    print(f"数据形状: {data.shape}")
+    print(f"数据列: {data.columns}")
+    print(f"\n前5行预览:")
+    print(data.head())
+
+    return data
+
+
+# =============================================================================
+# 股票池筛选配置
+# =============================================================================
+def stock_pool_filter(df: pl.DataFrame) -> pl.Series:
+    """股票池筛选函数（单日数据）。
+
+    筛选条件：
+    1. 排除创业板（代码以 300 开头）
+    2. 排除科创板（代码以 688 开头）
+    3. 排除北交所（代码以 8、9 或 4 开头）
+    4. 选取当日市值最小的500只股票
+
+    Args:
+        df: 单日数据框
+
+    Returns:
+        布尔Series，表示哪些股票被选中
+    """
+    # 代码筛选（排除创业板、科创板、北交所）
+    code_filter = (
+        ~df["ts_code"].str.starts_with("30")  # 排除创业板
+        & ~df["ts_code"].str.starts_with("68")  # 排除科创板
+        & ~df["ts_code"].str.starts_with("8")  # 排除北交所
+        & ~df["ts_code"].str.starts_with("9")  # 排除北交所
+        & ~df["ts_code"].str.starts_with("4")  # 排除北交所
+    )
+
+    # 在已筛选的股票中，选取市值最小的500只
+    valid_df = df.filter(code_filter)
+    n = min(500, len(valid_df))
+    small_cap_codes = valid_df.sort("total_mv").head(n)["ts_code"]
+
+    # 返回布尔 Series：是否在被选中的股票中
+    return df["ts_code"].is_in(small_cap_codes)
+
+
+# 定义筛选所需的基础列
+STOCK_FILTER_REQUIRED_COLUMNS = ["total_mv"]
+
+
+# =============================================================================
+# 输出配置
+# =============================================================================
+OUTPUT_DIR = "output"
+SAVE_PREDICTIONS = True
+PERSIST_MODEL = False
+
+# Top N 配置：每日推荐股票数量
+TOP_N = 5  # 可调整为 10, 20 等
+
+
+def get_output_path(model_type: str, test_start: str, test_end: str) -> str:
+    """生成输出文件路径。
+
+    Args:
+        model_type: 模型类型（"regression" 或 "rank"）
+        test_start: 测试开始日期
+        test_end: 测试结束日期
+
+    Returns:
+        输出文件路径
+    """
+    import os
+
+    # 确保输出目录存在
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+    # 生成文件名
+    start_dt = datetime.strptime(test_start, "%Y%m%d")
+    end_dt = datetime.strptime(test_end, "%Y%m%d")
+    date_str = f"{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}"
+
+    filename = f"{model_type}_output.csv"
+    return os.path.join(OUTPUT_DIR, filename)
diff --git a/src/experiment/learn_to_rank.ipynb b/src/experiment/learn_to_rank.ipynb
index a7c7616..ccb2375 100644
--- a/src/experiment/learn_to_rank.ipynb
+++ b/src/experiment/learn_to_rank.ipynb
@@ -22,7 +22,12 @@
    "source": "## 1. 导入依赖"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:08.941539Z",
+     "start_time": "2026-03-14T15:09:08.938469Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "import os\n",
@@ -48,88 +53,47 @@
     ")\n",
     "from src.training.components.models import LightGBMLambdaRankModel\n",
     "from src.training.config import TrainingConfig\n",
+    "\n",
+    "# 从 common 模块导入共用配置和函数\n",
+    "from src.experiment.common import (\n",
+    "    SELECTED_FACTORS,\n",
+    "    FACTOR_DEFINITIONS,\n",
+    "    get_label_factor,\n",
+    "    register_factors,\n",
+    "    prepare_data,\n",
+    "    TRAIN_START,\n",
+    "    TRAIN_END,\n",
+    "    VAL_START,\n",
+    "    VAL_END,\n",
+    "    TEST_START,\n",
+    "    TEST_END,\n",
+    "    stock_pool_filter,\n",
+    "    STOCK_FILTER_REQUIRED_COLUMNS,\n",
+    "    OUTPUT_DIR,\n",
+    "    SAVE_PREDICTIONS,\n",
+    "    PERSIST_MODEL,\n",
+    "    TOP_N,\n",
+    ")\n",
     "\n"
    ],
    "outputs": [],
-   "execution_count": null
+   "execution_count": 13
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## 2. 辅助函数"
+   "source": "## 2. 本地辅助函数"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:08.987446Z",
+     "start_time": "2026-03-14T15:09:08.981368Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "def register_factors(\n",
-    "    engine: FactorEngine,\n",
-    "    selected_factors: List[str],\n",
-    "    factor_definitions: dict,\n",
-    "    label_factor: dict,\n",
-    ") -> List[str]:\n",
-    "    \"\"\"注册因子（selected_factors 从 metadata 查询，factor_definitions 用 DSL 表达式注册）\"\"\"\n",
-    "    print(\"=\" * 80)\n",
-    "    print(\"注册因子\")\n",
-    "    print(\"=\" * 80)\n",
-    "\n",
-    "    # 注册 SELECTED_FACTORS 中的因子（已在 metadata 中）\n",
-    "    print(\"\\n注册特征因子（从 metadata）:\")\n",
-    "    for name in selected_factors:\n",
-    "        engine.add_factor(name)\n",
-    "        print(f\"  - {name}\")\n",
-    "\n",
-    "    # 注册 FACTOR_DEFINITIONS 中的因子（通过表达式，尚未在 metadata 中）\n",
-    "    print(\"\\n注册特征因子（表达式）:\")\n",
-    "    for name, expr in factor_definitions.items():\n",
-    "        engine.add_factor(name, expr)\n",
-    "        print(f\"  - {name}: {expr}\")\n",
-    "\n",
-    "    # 注册 label 因子（通过表达式）\n",
-    "    print(\"\\n注册 Label 因子（表达式）:\")\n",
-    "    for name, expr in label_factor.items():\n",
-    "        engine.add_factor(name, expr)\n",
-    "        print(f\"  - {name}: {expr}\")\n",
-    "\n",
-    "    # 特征列 = SELECTED_FACTORS + FACTOR_DEFINITIONS 的 keys\n",
-    "    feature_cols = selected_factors + list(factor_definitions.keys())\n",
-    "\n",
-    "    print(f\"\\n特征因子数: {len(feature_cols)}\")\n",
-    "    print(f\"  - 来自 metadata: {len(selected_factors)}\")\n",
-    "    print(f\"  - 来自表达式: {len(factor_definitions)}\")\n",
-    "    print(f\"Label: {list(label_factor.keys())[0]}\")\n",
-    "    print(f\"已注册因子总数: {len(engine.list_registered())}\")\n",
-    "\n",
-    "    return feature_cols\n",
-    "\n",
-    "\n",
-    "def prepare_data(\n",
-    "    engine: FactorEngine,\n",
-    "    feature_cols: List[str],\n",
-    "    start_date: str,\n",
-    "    end_date: str,\n",
-    ") -> pl.DataFrame:\n",
-    "    \"\"\"准备数据\"\"\"\n",
-    "    print(\"\\n\" + \"=\" * 80)\n",
-    "    print(\"准备数据\")\n",
-    "    print(\"=\" * 80)\n",
-    "\n",
-    "    # 计算因子（全市场数据）\n",
-    "    print(f\"\\n计算因子: {start_date} - {end_date}\")\n",
-    "    factor_names = feature_cols + [LABEL_NAME]  # 包含 label\n",
-    "\n",
-    "    data = engine.compute(\n",
-    "        factor_names=factor_names,\n",
-    "        start_date=start_date,\n",
-    "        end_date=end_date,\n",
-    "    )\n",
-    "\n",
-    "    print(f\"数据形状: {data.shape}\")\n",
-    "    print(f\"数据列: {data.columns}\")\n",
-    "    print(f\"\\n前5行预览:\")\n",
-    "    print(data.head())\n",
-    "\n",
-    "    return data\n",
+    "# 注意：register_factors 和 prepare_data 已从 common 模块导入\n",
     "\n",
     "\n",
     "def prepare_ranking_data(\n",
@@ -263,7 +227,7 @@
     "\n"
    ],
    "outputs": [],
-   "execution_count": null
+   "execution_count": 14
   },
   {
    "metadata": {},
@@ -271,104 +235,29 @@
    "source": [
     "## 3. 配置参数\n",
     "#\n",
-    "### 3.1 因子定义"
+    "### 3.1 因子与日期配置"
    ]
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:08.996882Z",
+     "start_time": "2026-03-14T15:09:08.993154Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "# 特征因子定义字典（复用 regression.ipynb 的因子定义）\n",
-    "LABEL_NAME = \"future_return_5_rank\"\n",
+    "# 注意：SELECTED_FACTORS, FACTOR_DEFINITIONS, 日期配置等已从 common 模块导入\n",
+    "# 本脚本特有的配置：\n",
     "\n",
-    "# 当前选择的因子列表（从 FACTOR_DEFINITIONS 中选择要使用的因子）\n",
-    "SELECTED_FACTORS = [\n",
-    "    # ================= 1. 价格、趋势与路径依赖 =================\n",
-    "    \"ma_5\",\n",
-    "    \"ma_20\",\n",
-    "    \"ma_ratio_5_20\",\n",
-    "    \"bias_10\",\n",
-    "    \"high_low_ratio\",\n",
-    "    \"bbi_ratio\",\n",
-    "    \"return_5\",\n",
-    "    \"return_20\",\n",
-    "    \"kaufman_ER_20\",\n",
-    "    \"mom_acceleration_10_20\",\n",
-    "    \"drawdown_from_high_60\",\n",
-    "    \"up_days_ratio_20\",\n",
-    "    # ================= 2. 波动率、风险调整与高阶矩 =================\n",
-    "    \"volatility_5\",\n",
-    "    \"volatility_20\",\n",
-    "    \"volatility_ratio\",\n",
-    "    \"std_return_20\",\n",
-    "    \"sharpe_ratio_20\",\n",
-    "    \"min_ret_20\",\n",
-    "    \"volatility_squeeze_5_60\",\n",
-    "    # ================= 3. 日内微观结构与异象 =================\n",
-    "    \"overnight_intraday_diff\",\n",
-    "    \"upper_shadow_ratio\",\n",
-    "    \"capital_retention_20\",\n",
-    "    \"max_ret_20\",\n",
-    "    # ================= 4. 量能、流动性与量价背离 =================\n",
-    "    \"volume_ratio_5_20\",\n",
-    "    \"turnover_rate_mean_5\",\n",
-    "    \"turnover_deviation\",\n",
-    "    \"amihud_illiq_20\",\n",
-    "    \"turnover_cv_20\",\n",
-    "    \"pv_corr_20\",\n",
-    "    \"close_vwap_deviation\",\n",
-    "    # ================= 5. 基本面财务特征 =================\n",
-    "    \"roe\",\n",
-    "    \"roa\",\n",
-    "    \"profit_margin\",\n",
-    "    \"debt_to_equity\",\n",
-    "    \"current_ratio\",\n",
-    "    \"net_profit_yoy\",\n",
-    "    \"revenue_yoy\",\n",
-    "    \"healthy_expansion_velocity\",\n",
-    "    \"ebit_rank\",\n",
-    "    # ================= 6. 基本面估值与截面动量共振 =================\n",
-    "    \"EP\",\n",
-    "    \"BP\",\n",
-    "    \"CP\",\n",
-    "    \"market_cap_rank\",\n",
-    "    \"turnover_rank\",\n",
-    "    \"return_5_rank\",\n",
-    "    \"EP_rank\",\n",
-    "    \"pe_expansion_trend\",\n",
-    "    \"value_price_divergence\",\n",
-    "    \"active_market_cap\",\n",
-    "]\n",
+    "# Label 名称（排序学习使用原始收益率，会后续转换为分位数标签）\n",
+    "LABEL_NAME = \"future_return_5\"\n",
     "\n",
-    "# 因子定义字典（完整因子库）\n",
-    "FACTOR_DEFINITIONS = {\n",
-    "    # \"turnover_rate_volatility\": \"ts_std(log(turnover_rate), 20)\"\n",
-    "}\n",
+    "# 获取 Label 因子定义\n",
+    "LABEL_FACTOR = get_label_factor(LABEL_NAME)\n",
     "\n",
-    "# Label 因子定义（不参与训练，用于计算目标）\n",
-    "LABEL_FACTOR = {\n",
-    "    LABEL_NAME: \"(ts_delay(close, -5) / ts_delay(open, -1)) - 1\",\n",
-    "}"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### 3.2 训练参数配置"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "# 日期范围配置（正确的 train/val/test 三分法）\n",
-    "TRAIN_START = \"20200101\"\n",
-    "TRAIN_END = \"20231231\"\n",
-    "VAL_START = \"20240101\"\n",
-    "VAL_END = \"20241231\"\n",
-    "TEST_START = \"20250101\"\n",
-    "TEST_END = \"20251231\"\n",
+    "# 分位数配置\n",
+    "N_QUANTILES = 20  # 将 label 分为 20 组\n",
     "\n",
     "\n",
     "# 分位数配置\n",
@@ -378,8 +267,8 @@
     "MODEL_PARAMS = {\n",
     "    \"objective\": \"lambdarank\",\n",
     "    \"metric\": \"ndcg\",\n",
-    "    \"ndcg_at\": 10,  # 评估 NDCG@k\n",
-    "    \"learning_rate\": 0.01,\n",
+    "    \"ndcg_at\": 5,  # 评估 NDCG@k\n",
+    "    \"learning_rate\": 0.05,\n",
     "    \"num_leaves\": 31,\n",
     "    \"max_depth\": 4,\n",
     "    \"min_data_in_leaf\": 20,\n",
@@ -391,48 +280,15 @@
     "    \"reg_lambda\": 1.0,\n",
     "    \"verbose\": -1,\n",
     "    \"random_state\": 42,\n",
-    "    \"lambdarank_truncation_level\": 10,\n",
+    "    \"lambdarank_truncation_level\": 5,\n",
     "    \"label_gain\": [i for i in range(1, N_QUANTILES + 1)],\n",
     "}\n",
     "\n",
-    "\n",
-    "# 股票池筛选函数\n",
-    "def stock_pool_filter(df: pl.DataFrame) -> pl.Series:\n",
-    "    \"\"\"股票池筛选函数（单日数据）\n",
-    "\n",
-    "    筛选条件：\n",
-    "    1. 排除创业板（代码以 300 开头）\n",
-    "    2. 排除科创板（代码以 688 开头）\n",
-    "    3. 排除北交所（代码以 8、9 或 4 开头）\n",
-    "    4. 选取当日市值最小的500只股票\n",
-    "    \"\"\"\n",
-    "    code_filter = (\n",
-    "        ~df[\"ts_code\"].str.starts_with(\"30\")\n",
-    "        & ~df[\"ts_code\"].str.starts_with(\"68\")\n",
-    "        & ~df[\"ts_code\"].str.starts_with(\"8\")\n",
-    "        & ~df[\"ts_code\"].str.starts_with(\"9\")\n",
-    "        & ~df[\"ts_code\"].str.starts_with(\"4\")\n",
-    "    )\n",
-    "\n",
-    "    valid_df = df.filter(code_filter)\n",
-    "    n = min(500, len(valid_df))\n",
-    "    small_cap_codes = valid_df.sort(\"total_mv\").head(n)[\"ts_code\"]\n",
-    "\n",
-    "    return df[\"ts_code\"].is_in(small_cap_codes)\n",
-    "\n",
-    "\n",
-    "STOCK_FILTER_REQUIRED_COLUMNS = [\"total_mv\"]\n",
-    "\n",
-    "# 输出配置\n",
-    "OUTPUT_DIR = \"output\"\n",
-    "SAVE_PREDICTIONS = True\n",
-    "PERSIST_MODEL = False\n",
-    "\n",
-    "# Top N 配置：每日推荐股票数量\n",
-    "TOP_N = 5  # 可调整为 10, 20 等"
+    "# 注意：stock_pool_filter, STOCK_FILTER_REQUIRED_COLUMNS, OUTPUT_DIR 等配置\n",
+    "# 已从 common 模块导入"
    ],
    "outputs": [],
-   "execution_count": null
+   "execution_count": 15
   },
   {
    "metadata": {},
@@ -440,7 +296,12 @@
    "source": "## 4. 训练流程"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:21.575611Z",
+     "start_time": "2026-03-14T15:09:09.003108Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -464,6 +325,7 @@
     "    feature_cols=feature_cols,\n",
     "    start_date=TRAIN_START,\n",
     "    end_date=TEST_END,\n",
+    "    label_name=LABEL_NAME,\n",
     ")\n",
     "\n",
     "# 4. 转换为排序学习格式（分位数标签）\n",
@@ -523,8 +385,223 @@
     "    persist_model=PERSIST_MODEL,\n",
     ")"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "LightGBM LambdaRank 排序学习训练\n",
+      "================================================================================\n",
+      "\n",
+      "[1] 创建 FactorEngine\n",
+      "\n",
+      "[2] 定义因子（从 metadata 注册）\n",
+      "================================================================================\n",
+      "注册因子\n",
+      "================================================================================\n",
+      "\n",
+      "注册特征因子（从 metadata）:\n",
+      "  - ma_5\n",
+      "  - ma_20\n",
+      "  - ma_ratio_5_20\n",
+      "  - bias_10\n",
+      "  - high_low_ratio\n",
+      "  - bbi_ratio\n",
+      "  - return_5\n",
+      "  - return_20\n",
+      "  - kaufman_ER_20\n",
+      "  - mom_acceleration_10_20\n",
+      "  - drawdown_from_high_60\n",
+      "  - up_days_ratio_20\n",
+      "  - volatility_5\n",
+      "  - volatility_20\n",
+      "  - volatility_ratio\n",
+      "  - std_return_20\n",
+      "  - sharpe_ratio_20\n",
+      "  - min_ret_20\n",
+      "  - volatility_squeeze_5_60\n",
+      "  - overnight_intraday_diff\n",
+      "  - upper_shadow_ratio\n",
+      "  - capital_retention_20\n",
+      "  - max_ret_20\n",
+      "  - volume_ratio_5_20\n",
+      "  - turnover_rate_mean_5\n",
+      "  - turnover_deviation\n",
+      "  - amihud_illiq_20\n",
+      "  - turnover_cv_20\n",
+      "  - pv_corr_20\n",
+      "  - close_vwap_deviation\n",
+      "  - roe\n",
+      "  - roa\n",
+      "  - profit_margin\n",
+      "  - debt_to_equity\n",
+      "  - current_ratio\n",
+      "  - net_profit_yoy\n",
+      "  - revenue_yoy\n",
+      "  - healthy_expansion_velocity\n",
+      "  - EP\n",
+      "  - BP\n",
+      "  - CP\n",
+      "  - market_cap_rank\n",
+      "  - turnover_rank\n",
+      "  - return_5_rank\n",
+      "  - EP_rank\n",
+      "  - pe_expansion_trend\n",
+      "  - value_price_divergence\n",
+      "  - active_market_cap\n",
+      "  - ebit_rank\n",
+      "\n",
+      "注册特征因子（表达式）:\n",
+      "\n",
+      "注册 Label 因子（表达式）:\n",
+      "  - future_return_5: (ts_delay(close, -5) / ts_delay(open, -1)) - 1\n",
+      "\n",
+      "特征因子数: 49\n",
+      "  - 来自 metadata: 49\n",
+      "  - 来自表达式: 0\n",
+      "Label: future_return_5\n",
+      "已注册因子总数: 63\n",
+      "\n",
+      "[3] 准备数据\n",
+      "\n",
+      "================================================================================\n",
+      "准备数据\n",
+      "================================================================================\n",
+      "\n",
+      "计算因子: 20200101 - 20261231\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\PyProject\\ProStock\\src\\data\\financial_loader.py:148: UserWarning: Sortedness of columns cannot be checked when 'by' groups provided\n",
+      "  merged = df_price.join_asof(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "数据形状: (7255513, 70)\n",
+      "数据列: ['ts_code', 'trade_date', 'amount', 'low', 'turnover_rate', 'vol', 'open', 'close', 'high', 'total_assets', 'total_mv', 'f_ann_date', 'total_hldr_eqy_exc_min_int', 'total_liab', 'total_cur_liab', 'total_cur_assets', 'ebit', 'n_income', 'revenue', 'n_cashflow_act', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'high_low_ratio', 'bbi_ratio', 'return_5', 'return_20', 'kaufman_ER_20', 'mom_acceleration_10_20', 'drawdown_from_high_60', 'up_days_ratio_20', 'volatility_5', 'volatility_20', 'volatility_ratio', 'std_return_20', 'sharpe_ratio_20', 'min_ret_20', 'volatility_squeeze_5_60', 'overnight_intraday_diff', 'upper_shadow_ratio', 'capital_retention_20', 'max_ret_20', 'volume_ratio_5_20', 'turnover_rate_mean_5', 'turnover_deviation', 'amihud_illiq_20', 'turnover_cv_20', 'pv_corr_20', 'close_vwap_deviation', 'roe', 'roa', 'profit_margin', 'debt_to_equity', 'current_ratio', 'net_profit_yoy', 'revenue_yoy', 'healthy_expansion_velocity', 'EP', 'BP', 'CP', 'market_cap_rank', 'turnover_rank', 'return_5_rank', 'EP_rank', 'pe_expansion_trend', 'value_price_divergence', 'active_market_cap', 'ebit_rank', 'future_return_5']\n",
+      "\n",
+      "前5行预览:\n",
+      "shape: (5, 70)\n",
+      "┌───────────┬────────────┬──────────┬─────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
+      "│ ts_code   ┆ trade_date ┆ amount   ┆ low     ┆ … ┆ value_pric ┆ active_ma ┆ ebit_rank ┆ future_re │\n",
+      "│ ---       ┆ ---        ┆ ---      ┆ ---     ┆   ┆ e_divergen ┆ rket_cap  ┆ ---       ┆ turn_5    │\n",
+      "│ str       ┆ str        ┆ f64      ┆ f64     ┆   ┆ ce         ┆ ---       ┆ f64       ┆ ---       │\n",
+      "│           ┆            ┆          ┆         ┆   ┆ ---        ┆ f64       ┆           ┆ f64       │\n",
+      "│           ┆            ┆          ┆         ┆   ┆ f64        ┆           ┆           ┆           │\n",
+      "╞═══════════╪════════════╪══════════╪═════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
+      "│ 000001.SZ ┆ 20200102   ┆ 2.5712e6 ┆ 1806.75 ┆ … ┆ null       ┆ null      ┆ null      ┆ -0.008857 │\n",
+      "│ 000001.SZ ┆ 20200103   ┆ 1.9145e6 ┆ 1847.15 ┆ … ┆ null       ┆ null      ┆ null      ┆ -0.01881  │\n",
+      "│ 000001.SZ ┆ 20200106   ┆ 1.4779e6 ┆ 1846.05 ┆ … ┆ null       ┆ null      ┆ null      ┆ -0.008171 │\n",
+      "│ 000001.SZ ┆ 20200107   ┆ 1.2470e6 ┆ 1850.42 ┆ … ┆ null       ┆ null      ┆ null      ┆ -0.014117 │\n",
+      "│ 000001.SZ ┆ 20200108   ┆ 1.4236e6 ┆ 1815.49 ┆ … ┆ null       ┆ null      ┆ null      ┆ -0.017252 │\n",
+      "└───────────┴────────────┴──────────┴─────────┴───┴────────────┴───────────┴───────────┴───────────┘\n",
+      "\n",
+      "[4] 转换为排序学习格式\n",
+      "\n",
+      "================================================================================\n",
+      "准备排序学习数据（将 future_return_5 转换为 20 分位数标签）\n",
+      "================================================================================\n",
+      "\n",
+      "原始 future_return_5 统计:\n",
+      "shape: (9, 2)\n",
+      "┌────────────┬────────────┐\n",
+      "│ statistic  ┆ value      │\n",
+      "│ ---        ┆ ---        │\n",
+      "│ str        ┆ f64        │\n",
+      "╞════════════╪════════════╡\n",
+      "│ count      ┆ 7.227054e6 │\n",
+      "│ null_count ┆ 28459.0    │\n",
+      "│ mean       ┆ 0.003978   │\n",
+      "│ std        ┆ 0.073204   │\n",
+      "│ min        ┆ -0.969459  │\n",
+      "│ 25%        ┆ -0.032998  │\n",
+      "│ 50%        ┆ -0.001278  │\n",
+      "│ 75%        ┆ 0.032666   │\n",
+      "│ max        ┆ 10.361925  │\n",
+      "└────────────┴────────────┘\n",
+      "\n",
+      "转换后 future_return_5_rank 统计:\n",
+      "shape: (9, 2)\n",
+      "┌────────────┬────────────┐\n",
+      "│ statistic  ┆ value      │\n",
+      "│ ---        ┆ ---        │\n",
+      "│ str        ┆ f64        │\n",
+      "╞════════════╪════════════╡\n",
+      "│ count      ┆ 7.227054e6 │\n",
+      "│ null_count ┆ 28459.0    │\n",
+      "│ mean       ┆ 9.493551   │\n",
+      "│ std        ┆ 5.765628   │\n",
+      "│ min        ┆ 0.0        │\n",
+      "│ 25%        ┆ 4.0        │\n",
+      "│ 50%        ┆ 9.0        │\n",
+      "│ 75%        ┆ 14.0       │\n",
+      "│ max        ┆ 19.0       │\n",
+      "└────────────┴────────────┘\n",
+      "\n",
+      "每日样本数统计:\n",
+      "shape: (9, 2)\n",
+      "┌────────────┬─────────────┐\n",
+      "│ statistic  ┆ value       │\n",
+      "│ ---        ┆ ---         │\n",
+      "│ str        ┆ f64         │\n",
+      "╞════════════╪═════════════╡\n",
+      "│ count      ┆ 1494.0      │\n",
+      "│ null_count ┆ 0.0         │\n",
+      "│ mean       ┆ 4856.434404 │\n",
+      "│ std        ┆ 564.521537  │\n",
+      "│ min        ┆ 2885.0      │\n",
+      "│ 25%        ┆ 4382.0      │\n",
+      "│ 50%        ┆ 5069.0      │\n",
+      "│ 75%        ┆ 5347.0      │\n",
+      "│ max        ┆ 5476.0      │\n",
+      "└────────────┴─────────────┘\n",
+      "\n",
+      "分位数标签分布:\n",
+      "shape: (21, 2)\n",
+      "┌──────────────────────┬────────┐\n",
+      "│ future_return_5_rank ┆ count  │\n",
+      "│ ---                  ┆ ---    │\n",
+      "│ i64                  ┆ u32    │\n",
+      "╞══════════════════════╪════════╡\n",
+      "│ null                 ┆ 28459  │\n",
+      "│ 0                    ┆ 362270 │\n",
+      "│ 1                    ┆ 361546 │\n",
+      "│ 2                    ┆ 361599 │\n",
+      "│ 3                    ┆ 361755 │\n",
+      "│ …                    ┆ …      │\n",
+      "│ 15                   ┆ 361289 │\n",
+      "│ 16                   ┆ 361218 │\n",
+      "│ 17                   ┆ 361227 │\n",
+      "│ 18                   ┆ 361252 │\n",
+      "│ 19                   ┆ 359483 │\n",
+      "└──────────────────────┴────────┘\n",
+      "\n",
+      "[配置] 训练期: 20200101 - 20231231\n",
+      "[配置] 验证期: 20240101 - 20241231\n",
+      "[配置] 测试期: 20250101 - 20261231\n",
+      "[配置] 特征数: 49\n",
+      "[配置] 目标变量: future_return_5_rank（20分位数）\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_28476\\562285170.py:58: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n",
+      "(Deprecated in version 0.20.5)\n",
+      "  daily_counts = df_ranked.group_by(date_col).agg(pl.count().alias(\"count\"))\n"
+     ]
+    }
+   ],
+   "execution_count": 16
   },
   {
    "metadata": {},
@@ -532,7 +609,12 @@
    "source": "### 4.1 股票池筛选"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:45.297379Z",
+     "start_time": "2026-03-14T15:09:21.587998Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -557,8 +639,29 @@
     "    filtered_data = data\n",
     "    print(\"  未配置股票池管理器，跳过筛选\")"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "股票池筛选\n",
+      "================================================================================\n",
+      "\n",
+      "[过滤] 应用 ST 过滤器...\n",
+      "  ST 过滤后数据规模: (7027678, 71)\n",
+      "\n",
+      "执行每日独立筛选股票池...\n",
+      "  筛选前数据规模: (7027678, 71)\n",
+      "  筛选后数据规模: (747000, 71)\n",
+      "  筛选前股票数: 5694\n",
+      "  筛选后股票数: 1439\n",
+      "  删除记录数: 6280678\n"
+     ]
+    }
+   ],
+   "execution_count": 17
   },
   {
    "metadata": {},
@@ -566,7 +669,12 @@
    "source": "### 4.2 数据划分"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:45.376100Z",
+     "start_time": "2026-03-14T15:09:45.307622Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -593,8 +701,39 @@
     "else:\n",
     "    raise ValueError(\"必须配置数据划分器\")"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "数据划分\n",
+      "================================================================================\n",
+      "\n",
+      "训练集数据规模: (485000, 71)\n",
+      "验证集数据规模: (121000, 71)\n",
+      "测试集数据规模: (141000, 71)\n",
+      "\n",
+      "训练集 group 数量: 970\n",
+      "验证集 group 数量: 242\n",
+      "测试集 group 数量: 282\n",
+      "训练集日均样本数: 500.0\n",
+      "验证集日均样本数: 500.0\n",
+      "测试集日均样本数: 500.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_28476\\562285170.py:82: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n",
+      "(Deprecated in version 0.20.5)\n",
+      "  pl.count().alias(\"count\")\n"
+     ]
+    }
+   ],
+   "execution_count": 18
   },
   {
    "metadata": {},
@@ -602,7 +741,12 @@
    "source": "### 4.3 数据质量检查"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:47.637521Z",
+     "start_time": "2026-03-14T15:09:45.382468Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -620,8 +764,52 @@
     "\n",
     "print(\"[成功] 数据质量检查通过，未发现异常\")\n"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "数据质量检查（必须在预处理之前）\n",
+      "================================================================================\n",
+      "\n",
+      "检查训练集...\n",
+      "\n",
+      "================================================================================\n",
+      "数据质量检查报告\n",
+      "================================================================================\n",
+      "\n",
+      "[严重] 发现 1638 个全空因子:\n",
+      "  (某天的某个因子所有值都是 null，可能是数据缺失或计算错误)\n",
+      "    - 日期 20200824: roa (样本数: 500)\n",
+      "    - 日期 20200824: net_profit_yoy (样本数: 500)\n",
+      "    - 日期 20200824: revenue_yoy (样本数: 500)\n",
+      "    - 日期 20200824: healthy_expansion_velocity (样本数: 500)\n",
+      "    - 日期 20200824: value_price_divergence (样本数: 500)\n",
+      "    - 日期 20200115: ma_20 (样本数: 500)\n",
+      "    - 日期 20200115: ma_ratio_5_20 (样本数: 500)\n",
+      "    - 日期 20200115: high_low_ratio (样本数: 500)\n",
+      "    - 日期 20200115: bbi_ratio (样本数: 500)\n",
+      "    - 日期 20200115: return_20 (样本数: 500)\n",
+      "    ... 还有 1628 个\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "建议处理方式:\n",
+      "  1. 检查因子定义和数据源，确认计算逻辑是否正确\n",
+      "  2. 如果是预期内的缺失（如新股无历史数据），考虑调整因子计算窗口\n",
+      "  3. 如果是数据同步问题，重新同步相关数据\n",
+      "  4. 可以使用 filter 排除问题日期或因子\n",
+      "================================================================================\n",
+      "\n",
+      "检查验证集...\n",
+      "\n",
+      "检查测试集...\n",
+      "[成功] 数据质量检查通过，未发现异常\n"
+     ]
+    }
+   ],
+   "execution_count": 19
   },
   {
    "metadata": {},
@@ -629,7 +817,12 @@
    "source": "### 4.4 数据预处理"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:47.934178Z",
+     "start_time": "2026-03-14T15:09:47.643592Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -656,8 +849,32 @@
     "print(f\"处理后验证集形状: {val_data.shape}\")\n",
     "print(f\"处理后测试集形状: {test_data.shape}\")"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "数据预处理\n",
+      "================================================================================\n",
+      "\n",
+      "训练集处理...\n",
+      "  [1/3] NullFiller\n",
+      "  [2/3] Winsorizer\n",
+      "  [3/3] StandardScaler\n",
+      "\n",
+      "验证集处理...\n",
+      "\n",
+      "测试集处理...\n",
+      "\n",
+      "处理后训练集形状: (485000, 71)\n",
+      "处理后验证集形状: (121000, 71)\n",
+      "处理后测试集形状: (141000, 71)\n"
+     ]
+    }
+   ],
+   "execution_count": 20
   },
   {
    "metadata": {},
@@ -665,7 +882,12 @@
    "source": "### 4.4 训练 LambdaRank 模型"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:48.844310Z",
+     "start_time": "2026-03-14T15:09:47.939155Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -696,8 +918,48 @@
     ")\n",
     "print(\"训练完成！\")"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "训练 LambdaRank 模型\n",
+      "================================================================================\n",
+      "\n",
+      "训练样本数: 485000\n",
+      "验证样本数: 121000\n",
+      "特征数: 49\n",
+      "目标变量: future_return_5_rank\n",
+      "\n",
+      "目标变量统计（训练集）:\n",
+      "shape: (9, 2)\n",
+      "┌────────────┬──────────┐\n",
+      "│ statistic  ┆ value    │\n",
+      "│ ---        ┆ ---      │\n",
+      "│ str        ┆ f64      │\n",
+      "╞════════════╪══════════╡\n",
+      "│ count      ┆ 484665.0 │\n",
+      "│ null_count ┆ 335.0    │\n",
+      "│ mean       ┆ 9.988943 │\n",
+      "│ std        ┆ 5.224762 │\n",
+      "│ min        ┆ 0.0      │\n",
+      "│ 25%        ┆ 6.0      │\n",
+      "│ 50%        ┆ 10.0     │\n",
+      "│ 75%        ┆ 14.0     │\n",
+      "│ max        ┆ 19.0     │\n",
+      "└────────────┴──────────┘\n",
+      "\n",
+      "开始训练...\n",
+      "Training until validation scores don't improve for 100 rounds\n",
+      "Early stopping, best iteration is:\n",
+      "[5]\ttrain's ndcg@5: 0.610972\tval's ndcg@5: 0.558883\n",
+      "训练完成！\n"
+     ]
+    }
+   ],
+   "execution_count": 21
   },
   {
    "metadata": {},
@@ -705,7 +967,12 @@
    "source": "### 4.5 训练指标曲线"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:48.923721Z",
+     "start_time": "2026-03-14T15:09:48.850317Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -758,8 +1025,57 @@
     "        print(f\"    {metric}: {best_val:.4f} (迭代 {best_iter_metric + 1})\")\n",
     "    print(f\"\\n[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\")"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "训练指标曲线\n",
+      "================================================================================\n",
+      "[成功] 已从模型获取训练评估结果\n",
+      "\n",
+      "评估的 NDCG 指标: ['ndcg@5']\n",
+      "\n",
+      "[早停信息]\n",
+      "  配置的最大轮数: 2000\n",
+      "  实际训练轮数: 105\n",
+      "  早停状态: 已触发（最佳迭代: 5）\n",
+      "\n",
+      "最终 NDCG 指标:\n",
+      "  ndcg@5: 训练集=0.7120, 验证集=0.5334\n",
+      "\n",
+      "[绘图] 使用 LightGBM 原生接口绘制训练曲线...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x1000 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[指标分析]\n",
+      "  各NDCG指标在验证集上的最佳值:\n",
+      "    ndcg@5: 0.5589 (迭代 5)\n",
+      "\n",
+      "[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\n"
+     ]
+    }
+   ],
+   "execution_count": 22
   },
   {
    "metadata": {},
@@ -767,7 +1083,12 @@
    "source": "### 4.6 模型评估"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:49.191132Z",
+     "start_time": "2026-03-14T15:09:48.936532Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -808,11 +1129,61 @@
     "    for i, (feature, score) in enumerate(top_features.items(), 1):\n",
     "        print(f\"  {i:2d}. {feature:30s} {score:10.2f}\")"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "模型评估\n",
+      "================================================================================\n",
+      "\n",
+      "生成预测...\n",
+      "\n",
+      "计算 NDCG 指标...\n",
+      "\n",
+      "NDCG 评估结果:\n",
+      "----------------------------------------\n",
+      "  ndcg@1: 0.5390\n",
+      "  ndcg@5: 0.5280\n",
+      "  ndcg@10: 0.5261\n",
+      "  ndcg@20: 0.5281\n",
+      "\n",
+      "特征重要性（Top 20）:\n",
+      "----------------------------------------\n",
+      "   1. max_ret_20                         178.55\n",
+      "   2. ma_ratio_5_20                      175.30\n",
+      "   3. ma_5                               161.37\n",
+      "   4. market_cap_rank                    144.83\n",
+      "   5. CP                                 130.89\n",
+      "   6. roa                                109.85\n",
+      "   7. healthy_expansion_velocity         108.79\n",
+      "   8. roe                                107.79\n",
+      "   9. ebit_rank                          103.48\n",
+      "  10. close_vwap_deviation                96.07\n",
+      "  11. std_return_20                       94.57\n",
+      "  12. revenue_yoy                         93.39\n",
+      "  13. ma_20                               90.67\n",
+      "  14. turnover_rank                       78.58\n",
+      "  15. pv_corr_20                          75.06\n",
+      "  16. amihud_illiq_20                     71.67\n",
+      "  17. EP_rank                             60.11\n",
+      "  18. return_20                           46.65\n",
+      "  19. min_ret_20                          46.34\n",
+      "  20. volume_ratio_5_20                   45.85\n"
+     ]
+    }
+   ],
+   "execution_count": 23
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T15:09:49.453399Z",
+     "start_time": "2026-03-14T15:09:49.213145Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "# 确保输出目录存在\n",
@@ -861,8 +1232,80 @@
     "\n",
     "print(\"\\n训练流程完成！\")"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[1/1] 保存每日 Top 5 股票...\n",
+      "  保存路径: output\\rank_output.csv\n",
+      "  保存行数: 1410（282个交易日 x 每日top5）\n",
+      "\n",
+      "  预览（前15行）:\n",
+      "shape: (15, 3)\n",
+      "┌────────────┬──────────┬───────────┐\n",
+      "│ trade_date ┆ score    ┆ ts_code   │\n",
+      "│ ---        ┆ ---      ┆ ---       │\n",
+      "│ str        ┆ f64      ┆ str       │\n",
+      "╞════════════╪══════════╪═══════════╡\n",
+      "│ 2025-01-02 ┆ 0.092165 ┆ 002816.SZ │\n",
+      "│ 2025-01-02 ┆ 0.067764 ┆ 002634.SZ │\n",
+      "│ 2025-01-02 ┆ 0.066779 ┆ 002836.SZ │\n",
+      "│ 2025-01-02 ┆ 0.054118 ┆ 000004.SZ │\n",
+      "│ 2025-01-02 ┆ 0.046321 ┆ 000691.SZ │\n",
+      "│ …          ┆ …        ┆ …         │\n",
+      "│ 2025-01-06 ┆ 0.092165 ┆ 002816.SZ │\n",
+      "│ 2025-01-06 ┆ 0.066779 ┆ 002836.SZ │\n",
+      "│ 2025-01-06 ┆ 0.05733  ┆ 002634.SZ │\n",
+      "│ 2025-01-06 ┆ 0.054118 ┆ 000004.SZ │\n",
+      "│ 2025-01-06 ┆ 0.052639 ┆ 600857.SH │\n",
+      "└────────────┴──────────┴───────────┘\n",
+      "\n",
+      "训练流程完成！\n"
+     ]
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## 5. 总结\n",
+    "#\n",
+    "本 Notebook 实现了完整的 Learn-to-Rank 训练流程：\n",
+    "#\n",
+    "### 核心步骤\n",
+    "#\n",
+    "1. **数据准备**: 计算 49 个特征因子，将 `future_return_5` 转换为 20 分位数标签\n",
+    "2. **模型训练**: 使用 LightGBM LambdaRank 学习每日股票排序\n",
+    "3. **模型评估**: 使用 NDCG@1/5/10/20 评估排序质量\n",
+    "4. **策略分析**: 基于排序分数构建 Top-k 选股策略\n",
+    "#\n",
+    "### 关键参数\n",
+    "#\n",
+    "- **Objective**: lambdarank\n",
+    "- **Metric**: ndcg\n",
+    "- **Learning Rate**: 0.05\n",
+    "- **Num Leaves**: 31\n",
+    "- **N Quantiles**: 20\n",
+    "#\n",
+    "### 输出结果\n",
+    "#\n",
+    "- rank_output.csv: 每日Top-N推荐股票（格式：date, score, ts_code）\n",
+    "- 特征重要性排名\n",
+    "- Top-k 策略统计和图表\n",
+    "- NDCG训练指标曲线\n",
+    "#\n",
+    "### 后续优化方向\n",
+    "#\n",
+    "1. **特征工程**: 尝试更多因子组合\n",
+    "2. **超参数调优**: 使用网格搜索优化 LambdaRank 参数\n",
+    "3. **模型集成**: 结合多个排序模型的预测\n",
+    "4. **更复杂的分组**: 考虑按行业分组排序\n",
+    "#\n"
+   ]
   },
   {
    "metadata": {},
diff --git a/src/experiment/learn_to_rank.py b/src/experiment/learn_to_rank.py
index bf85985..4205141 100644
--- a/src/experiment/learn_to_rank.py
+++ b/src/experiment/learn_to_rank.py
@@ -1,4 +1,4 @@
-#%% md
+# %% md
 # # Learn-to-Rank 排序学习训练流程
 # #
 # 本 Notebook 实现基于 LightGBM LambdaRank 的排序学习训练，用于股票排序任务。
@@ -9,9 +9,9 @@
 # 2. **排序学习**: 使用 LambdaRank 目标函数，学习每日股票排序
 # 3. **NDCG 评估**: 使用 NDCG@1/5/10/20 评估排序质量
 # 4. **策略回测**: 基于排序分数构建 Top-k 选股策略
-#%% md
+# %% md
 # ## 1. 导入依赖
-#%%
+# %%
 import os
 from datetime import datetime
 from typing import List, Tuple, Optional
@@ -36,78 +36,32 @@ from src.training import (
 from src.training.components.models import LightGBMLambdaRankModel
 from src.training.config import TrainingConfig
 
-
-#%% md
-# ## 2. 辅助函数
-#%%
-def register_factors(
-    engine: FactorEngine,
-    selected_factors: List[str],
-    factor_definitions: dict,
-    label_factor: dict,
-) -> List[str]:
-    """注册因子（selected_factors 从 metadata 查询，factor_definitions 用 DSL 表达式注册）"""
-    print("=" * 80)
-    print("注册因子")
-    print("=" * 80)
-
-    # 注册 SELECTED_FACTORS 中的因子（已在 metadata 中）
-    print("\n注册特征因子（从 metadata）:")
-    for name in selected_factors:
-        engine.add_factor(name)
-        print(f"  - {name}")
-
-    # 注册 FACTOR_DEFINITIONS 中的因子（通过表达式，尚未在 metadata 中）
-    print("\n注册特征因子（表达式）:")
-    for name, expr in factor_definitions.items():
-        engine.add_factor(name, expr)
-        print(f"  - {name}: {expr}")
-
-    # 注册 label 因子（通过表达式）
-    print("\n注册 Label 因子（表达式）:")
-    for name, expr in label_factor.items():
-        engine.add_factor(name, expr)
-        print(f"  - {name}: {expr}")
-
-    # 特征列 = SELECTED_FACTORS + FACTOR_DEFINITIONS 的 keys
-    feature_cols = selected_factors + list(factor_definitions.keys())
-
-    print(f"\n特征因子数: {len(feature_cols)}")
-    print(f"  - 来自 metadata: {len(selected_factors)}")
-    print(f"  - 来自表达式: {len(factor_definitions)}")
-    print(f"Label: {list(label_factor.keys())[0]}")
-    print(f"已注册因子总数: {len(engine.list_registered())}")
-
-    return feature_cols
+# 从 common 模块导入共用配置和函数
+from src.experiment.common import (
+    SELECTED_FACTORS,
+    FACTOR_DEFINITIONS,
+    get_label_factor,
+    register_factors,
+    prepare_data,
+    TRAIN_START,
+    TRAIN_END,
+    VAL_START,
+    VAL_END,
+    TEST_START,
+    TEST_END,
+    stock_pool_filter,
+    STOCK_FILTER_REQUIRED_COLUMNS,
+    OUTPUT_DIR,
+    SAVE_PREDICTIONS,
+    PERSIST_MODEL,
+    TOP_N,
+)
 
 
-def prepare_data(
-    engine: FactorEngine,
-    feature_cols: List[str],
-    start_date: str,
-    end_date: str,
-) -> pl.DataFrame:
-    """准备数据"""
-    print("\n" + "=" * 80)
-    print("准备数据")
-    print("=" * 80)
-
-    # 计算因子（全市场数据）
-    print(f"\n计算因子: {start_date} - {end_date}")
-    factor_names = feature_cols + [LABEL_NAME]  # 包含 label
-
-    data = engine.compute(
-        factor_names=factor_names,
-        start_date=start_date,
-        end_date=end_date,
-    )
-
-    print(f"数据形状: {data.shape}")
-    print(f"数据列: {data.columns}")
-    print(f"\n前5行预览:")
-    print(data.head())
-
-    return data
+# %% md
+# ## 2. 本地辅助函数
+# %%
+# 注意：register_factors 和 prepare_data 已从 common 模块导入
 
 
 def prepare_ranking_data(
@@ -240,92 +194,22 @@ def evaluate_ndcg_at_k(
     return results
 
 
-#%% md
+# %% md
 # ## 3. 配置参数
 # #
-# ### 3.1 因子定义
-#%%
-# 特征因子定义字典（复用 regression.ipynb 的因子定义）
-LABEL_NAME = "future_return_5_rank"
+# ### 3.1 因子与日期配置
+# %%
+# 注意：SELECTED_FACTORS, FACTOR_DEFINITIONS, 日期配置等已从 common 模块导入
+# 本脚本特有的配置：
 
-# 当前选择的因子列表（从 FACTOR_DEFINITIONS 中选择要使用的因子）
-SELECTED_FACTORS = [
-    # ================= 1. 价格、趋势与路径依赖 =================
-    "ma_5",
-    "ma_20",
-    "ma_ratio_5_20",
-    "bias_10",
-    "high_low_ratio",
-    "bbi_ratio",
-    "return_5",
-    "return_20",
-    "kaufman_ER_20",
-    "mom_acceleration_10_20",
-    "drawdown_from_high_60",
-    "up_days_ratio_20",
-    # ================= 2. 波动率、风险调整与高阶矩 =================
-    "volatility_5",
-    "volatility_20",
-    "volatility_ratio",
-    "std_return_20",
-    "sharpe_ratio_20",
-    "min_ret_20",
-    "volatility_squeeze_5_60",
-    # ================= 3. 日内微观结构与异象 =================
-    "overnight_intraday_diff",
-    "upper_shadow_ratio",
-    "capital_retention_20",
-    "max_ret_20",
-    # ================= 4. 量能、流动性与量价背离 =================
-    "volume_ratio_5_20",
-    "turnover_rate_mean_5",
-    "turnover_deviation",
-    "amihud_illiq_20",
-    "turnover_cv_20",
-    "pv_corr_20",
-    "close_vwap_deviation",
-    # ================= 5. 基本面财务特征 =================
-    "roe",
-    "roa",
-    "profit_margin",
-    "debt_to_equity",
-    "current_ratio",
-    "net_profit_yoy",
-    "revenue_yoy",
-    "healthy_expansion_velocity",
-    "ebit_rank",
-    # ================= 6. 基本面估值与截面动量共振 =================
-    "EP",
-    "BP",
-    "CP",
-    "market_cap_rank",
-    "turnover_rank",
-    "return_5_rank",
-    "EP_rank",
-    "pe_expansion_trend",
-    "value_price_divergence",
-    "active_market_cap",
-]
+# Label 名称（排序学习使用原始收益率，会后续转换为分位数标签）
+LABEL_NAME = "future_return_5"
 
-# 因子定义字典（完整因子库）
-FACTOR_DEFINITIONS = {
-    # "turnover_rate_volatility": "ts_std(log(turnover_rate), 20)"
-}
+# 获取 Label 因子定义
+LABEL_FACTOR = get_label_factor(LABEL_NAME)
 
-# Label 因子定义（不参与训练，用于计算目标）
-LABEL_FACTOR = {
-    LABEL_NAME: "(ts_delay(close, -5) / ts_delay(open, -1)) - 1",
-}
-#%% md
-# ### 3.2 训练参数配置
-#%%
-# 日期范围配置（正确的 train/val/test 三分法）
-TRAIN_START = "20200101"
-TRAIN_END = "20231231"
-VAL_START = "20240101"
-VAL_END = "20241231"
-TEST_START = "20250101"
-TEST_END = "20251231"
+# 分位数配置
+N_QUANTILES = 20  # 将 label 分为 20 组
 
 
 # 分位数配置
@@ -352,44 +236,11 @@ MODEL_PARAMS = {
     "label_gain": [i for i in range(1, N_QUANTILES + 1)],
 }
 
-
-# 股票池筛选函数
-def stock_pool_filter(df: pl.DataFrame) -> pl.Series:
-    """股票池筛选函数（单日数据）
-
-    筛选条件：
-    1. 排除创业板（代码以 300 开头）
-    2. 排除科创板（代码以 688 开头）
-    3. 排除北交所（代码以 8、9 或 4 开头）
-    4. 选取当日市值最小的500只股票
-    """
-    code_filter = (
-        ~df["ts_code"].str.starts_with("30")
-        & ~df["ts_code"].str.starts_with("68")
-        & ~df["ts_code"].str.starts_with("8")
-        & ~df["ts_code"].str.starts_with("9")
-        & ~df["ts_code"].str.starts_with("4")
-    )
-
-    valid_df = df.filter(code_filter)
-    n = min(500, len(valid_df))
-    small_cap_codes = valid_df.sort("total_mv").head(n)["ts_code"]
-
-    return df["ts_code"].is_in(small_cap_codes)
-
-
-STOCK_FILTER_REQUIRED_COLUMNS = ["total_mv"]
-
-# 输出配置
-OUTPUT_DIR = "output"
-SAVE_PREDICTIONS = True
-PERSIST_MODEL = False
-
-# Top N 配置：每日推荐股票数量
-TOP_N = 5  # 可调整为 10, 20 等
-#%% md
+# 注意：stock_pool_filter, STOCK_FILTER_REQUIRED_COLUMNS, OUTPUT_DIR 等配置
+# 已从 common 模块导入
+# %% md
 # ## 4. 训练流程
-#%%
+# %%
 print("\n" + "=" * 80)
 print("LightGBM LambdaRank 排序学习训练")
 print("=" * 80)
@@ -411,6 +262,7 @@ data = prepare_data(
     feature_cols=feature_cols,
     start_date=TRAIN_START,
     end_date=TEST_END,
+    label_name=LABEL_NAME,
 )
 
 # 4. 转换为排序学习格式（分位数标签）
@@ -469,9 +321,9 @@ trainer = Trainer(
     feature_cols=feature_cols,
     persist_model=PERSIST_MODEL,
 )
-#%% md
+# %% md
 # ### 4.1 股票池筛选
-#%%
+# %%
 print("\n" + "=" * 80)
 print("股票池筛选")
 print("=" * 80)
@@ -493,9 +345,9 @@ if pool_manager:
 else:
     filtered_data = data
     print("  未配置股票池管理器，跳过筛选")
-#%% md
+# %% md
 # ### 4.2 数据划分
-#%%
+# %%
 print("\n" + "=" * 80)
 print("数据划分")
 print("=" * 80)
@@ -519,9 +371,9 @@ if splitter:
     print(f"测试集日均样本数: {np.mean(test_group):.1f}")
 else:
     raise ValueError("必须配置数据划分器")
-#%% md
+# %% md
 # ### 4.3 数据质量检查
-#%%
+# %%
 print("\n" + "=" * 80)
 print("数据质量检查（必须在预处理之前）")
 print("=" * 80)
@@ -537,9 +389,9 @@ check_data_quality(test_data, feature_cols, raise_on_error=True)
 
 print("[成功] 数据质量检查通过，未发现异常")
 
-#%% md
+# %% md
 # ### 4.4 数据预处理
-#%%
+# %%
 print("\n" + "=" * 80)
 print("数据预处理")
 print("=" * 80)
@@ -563,9 +415,9 @@ if processors:
 print(f"\n处理后训练集形状: {train_data.shape}")
 print(f"处理后验证集形状: {val_data.shape}")
 print(f"处理后测试集形状: {test_data.shape}")
-#%% md
+# %% md
 # ### 4.4 训练 LambdaRank 模型
-#%%
+# %%
 print("\n" + "=" * 80)
 print("训练 LambdaRank 模型")
 print("=" * 80)
@@ -593,9 +445,9 @@ model.fit(
     eval_set=(X_val, y_val, val_group),
 )
 print("训练完成！")
-#%% md
+# %% md
 # ### 4.5 训练指标曲线
-#%%
+# %%
 print("\n" + "=" * 80)
 print("训练指标曲线")
 print("=" * 80)
@@ -645,9 +497,9 @@ else:
         best_val = max(val_metric_list)
         print(f"    {metric}: {best_val:.4f} (迭代 {best_iter_metric + 1})")
     print(f"\n[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！")
-#%% md
+# %% md
 # ### 4.6 模型评估
-#%%
+# %%
 print("\n" + "=" * 80)
 print("模型评估")
 print("=" * 80)
@@ -685,7 +537,7 @@ if importance is not None:
     top_features = importance.sort_values(ascending=False).head(20)
     for i, (feature, score) in enumerate(top_features.items(), 1):
         print(f"  {i:2d}. {feature:30s} {score:10.2f}")
-#%%
+# %%
 # 确保输出目录存在
 os.makedirs(OUTPUT_DIR, exist_ok=True)
 
@@ -731,7 +583,7 @@ print(f"\n  预览（前15行）:")
 print(topn_to_save.head(15))
 
 print("\n训练流程完成！")
-#%% md
+# %% md
 # ## 5. 总结
 # #
 # 本 Notebook 实现了完整的 Learn-to-Rank 训练流程：
@@ -764,4 +616,4 @@ print("\n训练流程完成！")
 # 2. **超参数调优**: 使用网格搜索优化 LambdaRank 参数
 # 3. **模型集成**: 结合多个排序模型的预测
 # 4. **更复杂的分组**: 考虑按行业分组排序
-# 
\ No newline at end of file
+#
diff --git a/src/experiment/regression.ipynb b/src/experiment/regression.ipynb
index 64393cf..6ead512 100644
--- a/src/experiment/regression.ipynb
+++ b/src/experiment/regression.ipynb
@@ -15,7 +15,6 @@
    "source": [
     "import os\n",
     "from datetime import datetime\n",
-    "from typing import List\n",
     "\n",
     "import polars as pl\n",
     "\n",
@@ -25,7 +24,6 @@
     "    LightGBMModel,\n",
     "    STFilter,\n",
     "    StandardScaler,\n",
-    "    # StockFilterConfig,  # 已删除，使用 StockPoolManager + filter_func 替代\n",
     "    StockPoolManager,\n",
     "    Trainer,\n",
     "    Winsorizer,\n",
@@ -33,87 +31,27 @@
     "    check_data_quality,\n",
     ")\n",
     "from src.training.config import TrainingConfig\n",
-    "\n"
-   ]
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "## 2. 定义辅助函数"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": [
-    "def register_factors(\n",
-    "    engine: FactorEngine,\n",
-    "    selected_factors: List[str],\n",
-    "    factor_definitions: dict,\n",
-    "    label_factor: dict,\n",
-    ") -> List[str]:\n",
-    "    \"\"\"注册因子（selected_factors 从 metadata 查询，factor_definitions 用 DSL 表达式注册）\"\"\"\n",
-    "    print(\"=\" * 80)\n",
-    "    print(\"注册因子\")\n",
-    "    print(\"=\" * 80)\n",
     "\n",
-    "    # 注册 SELECTED_FACTORS 中的因子（已在 metadata 中）\n",
-    "    print(\"\\n注册特征因子（从 metadata）:\")\n",
-    "    for name in selected_factors:\n",
-    "        engine.add_factor(name)\n",
-    "        print(f\"  - {name}\")\n",
-    "\n",
-    "    # 注册 FACTOR_DEFINITIONS 中的因子（通过表达式，尚未在 metadata 中）\n",
-    "    print(\"\\n注册特征因子（表达式）:\")\n",
-    "    for name, expr in factor_definitions.items():\n",
-    "        engine.add_factor(name, expr)\n",
-    "        print(f\"  - {name}: {expr}\")\n",
-    "\n",
-    "    # 注册 label 因子（通过表达式）\n",
-    "    print(\"\\n注册 Label 因子（表达式）:\")\n",
-    "    for name, expr in label_factor.items():\n",
-    "        engine.add_factor(name, expr)\n",
-    "        print(f\"  - {name}: {expr}\")\n",
-    "\n",
-    "    # 特征列 = SELECTED_FACTORS + FACTOR_DEFINITIONS 的 keys\n",
-    "    feature_cols = selected_factors + list(factor_definitions.keys())\n",
-    "\n",
-    "    print(f\"\\n特征因子数: {len(feature_cols)}\")\n",
-    "    print(f\"  - 来自 metadata: {len(selected_factors)}\")\n",
-    "    print(f\"  - 来自表达式: {len(factor_definitions)}\")\n",
-    "    print(f\"Label: {list(label_factor.keys())[0]}\")\n",
-    "    print(f\"已注册因子总数: {len(engine.list_registered())}\")\n",
-    "\n",
-    "    return feature_cols\n",
-    "\n",
-    "\n",
-    "def prepare_data(\n",
-    "    engine: FactorEngine,\n",
-    "    feature_cols: List[str],\n",
-    "    start_date: str,\n",
-    "    end_date: str,\n",
-    ") -> pl.DataFrame:\n",
-    "    print(\"\\n\" + \"=\" * 80)\n",
-    "    print(\"准备数据\")\n",
-    "    print(\"=\" * 80)\n",
-    "\n",
-    "    # 计算因子（全市场数据）\n",
-    "    print(f\"\\n计算因子: {start_date} - {end_date}\")\n",
-    "    factor_names = feature_cols + [LABEL_NAME]  # 包含 label\n",
-    "\n",
-    "    data = engine.compute(\n",
-    "        factor_names=factor_names,\n",
-    "        start_date=start_date,\n",
-    "        end_date=end_date,\n",
-    "    )\n",
-    "\n",
-    "    print(f\"数据形状: {data.shape}\")\n",
-    "    print(f\"数据列: {data.columns}\")\n",
-    "    print(f\"\\n前5行预览:\")\n",
-    "    print(data.head())\n",
-    "\n",
-    "    return data\n",
+    "# 从 common 模块导入共用配置和函数\n",
+    "from src.experiment.common import (\n",
+    "    SELECTED_FACTORS,\n",
+    "    FACTOR_DEFINITIONS,\n",
+    "    get_label_factor,\n",
+    "    register_factors,\n",
+    "    prepare_data,\n",
+    "    TRAIN_START,\n",
+    "    TRAIN_END,\n",
+    "    VAL_START,\n",
+    "    VAL_END,\n",
+    "    TEST_START,\n",
+    "    TEST_END,\n",
+    "    stock_pool_filter,\n",
+    "    STOCK_FILTER_REQUIRED_COLUMNS,\n",
+    "    OUTPUT_DIR,\n",
+    "    SAVE_PREDICTIONS,\n",
+    "    PERSIST_MODEL,\n",
+    "    TOP_N,\n",
+    ")\n",
     "\n"
    ]
   },
@@ -121,9 +59,9 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "## 3. 配置参数\n",
+    "## 2. 配置参数\n",
     "#\n",
-    "### 3.1 因子定义"
+    "### 2.1 标签定义"
    ]
   },
   {
@@ -132,177 +70,11 @@
    "outputs": [],
    "execution_count": null,
    "source": [
-    "# 特征因子定义字典：新增因子只需在此处添加一行\n",
+    "# Label 名称（回归任务使用连续收益率）\n",
     "LABEL_NAME = \"future_return_5\"\n",
     "\n",
-    "# 当前选择的因子列表（从 FACTOR_DEFINITIONS 中选择要使用的因子）\n",
-    "SELECTED_FACTORS = [\n",
-    "    # ================= 1. 价格、趋势与路径依赖 =================\n",
-    "    \"ma_5\",\n",
-    "    \"ma_20\",\n",
-    "    \"ma_ratio_5_20\",\n",
-    "    \"bias_10\",\n",
-    "    \"high_low_ratio\",\n",
-    "    \"bbi_ratio\",\n",
-    "    \"return_5\",\n",
-    "    \"return_20\",\n",
-    "    \"kaufman_ER_20\",\n",
-    "    \"mom_acceleration_10_20\",\n",
-    "    \"drawdown_from_high_60\",\n",
-    "    \"up_days_ratio_20\",\n",
-    "    # ================= 2. 波动率、风险调整与高阶矩 =================\n",
-    "    \"volatility_5\",\n",
-    "    \"volatility_20\",\n",
-    "    \"volatility_ratio\",\n",
-    "    \"std_return_20\",\n",
-    "    \"sharpe_ratio_20\",\n",
-    "    \"min_ret_20\",\n",
-    "    \"volatility_squeeze_5_60\",\n",
-    "    # ================= 3. 日内微观结构与异象 =================\n",
-    "    \"overnight_intraday_diff\",\n",
-    "    \"upper_shadow_ratio\",\n",
-    "    \"capital_retention_20\",\n",
-    "    \"max_ret_20\",\n",
-    "    # ================= 4. 量能、流动性与量价背离 =================\n",
-    "    \"volume_ratio_5_20\",\n",
-    "    \"turnover_rate_mean_5\",\n",
-    "    \"turnover_deviation\",\n",
-    "    \"amihud_illiq_20\",\n",
-    "    \"turnover_cv_20\",\n",
-    "    \"pv_corr_20\",\n",
-    "    \"close_vwap_deviation\",\n",
-    "    # ================= 5. 基本面财务特征 =================\n",
-    "    \"roe\",\n",
-    "    \"roa\",\n",
-    "    \"profit_margin\",\n",
-    "    \"debt_to_equity\",\n",
-    "    \"current_ratio\",\n",
-    "    \"net_profit_yoy\",\n",
-    "    \"revenue_yoy\",\n",
-    "    \"healthy_expansion_velocity\",\n",
-    "    # ================= 6. 基本面估值与截面动量共振 =================\n",
-    "    \"EP\",\n",
-    "    \"BP\",\n",
-    "    \"CP\",\n",
-    "    \"market_cap_rank\",\n",
-    "    \"turnover_rank\",\n",
-    "    \"return_5_rank\",\n",
-    "    \"EP_rank\",\n",
-    "    \"pe_expansion_trend\",\n",
-    "    \"value_price_divergence\",\n",
-    "    \"active_market_cap\",\n",
-    "    \"ebit_rank\",\n",
-    "]\n",
-    "\n",
-    "# 因子定义字典（完整因子库）\n",
-    "FACTOR_DEFINITIONS = {\n",
-    "    # ================= 1. 价格、趋势与路径依赖 (Trend, Momentum & Path Dependency) =================\n",
-    "    \"ma_5\": \"ts_mean(close, 5)\",\n",
-    "    \"ma_20\": \"ts_mean(close, 20)\",\n",
-    "    \"ma_ratio_5_20\": \"ts_mean(close, 5) / (ts_mean(close, 20) + 1e-8) - 1\",  # 均线发散度\n",
-    "    \"bias_10\": \"close / (ts_mean(close, 10) + 1e-8) - 1\",  # 10日乖离率\n",
-    "    \"high_low_ratio\": \"(close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\",  # 威廉指标变形\n",
-    "    \"bbi_ratio\": \"(ts_mean(close, 3) + ts_mean(close, 6) + ts_mean(close, 12) + ts_mean(close, 24)) / (4 * close + 1e-8)\",  # 多空指标比率\n",
-    "    \"return_5\": \"(close / (ts_delay(close, 5) + 1e-8)) - 1\",  # 5日动量\n",
-    "    \"return_20\": \"(close / (ts_delay(close, 20) + 1e-8)) - 1\",  # 20日动量\n",
-    "    # [高阶] Kaufman 趋势效率 (极高价值) - 衡量趋势流畅度，剔除无序震荡\n",
-    "    \"kaufman_ER_20\": \"abs(close - ts_delay(close, 20)) / (ts_sum(abs(close - ts_delay(close, 1)), 20) + 1e-8)\",\n",
-    "    # [高阶] 动量加速度 - 寻找二阶导数大于0，正在加速爆发的股票\n",
-    "    \"mom_acceleration_10_20\": \"(close / (ts_delay(close, 10) + 1e-8) - 1) - (ts_delay(close, 10) / (ts_delay(close, 20) + 1e-8) - 1)\",\n",
-    "    # [高阶] 高点距离衰减 - 衡量套牢盘压力\n",
-    "    \"drawdown_from_high_60\": \"close / (ts_max(high, 60) + 1e-8) - 1\",\n",
-    "    # [高阶] 趋势一致性 - 过去20天内收红的天数比例\n",
-    "    \"up_days_ratio_20\": \"ts_sum(close > ts_delay(close, 1), 20) / 20\",\n",
-    "    # ================= 2. 波动率、风险调整与高阶矩 (Volatility & Risk-Adjusted Returns) =================\n",
-    "    \"volatility_5\": \"ts_std(close, 5)\",\n",
-    "    \"volatility_20\": \"ts_std(close, 20)\",\n",
-    "    \"volatility_ratio\": \"ts_std(close, 5) / (ts_std(close, 20) + 1e-8)\",  # 波动率期限结构\n",
-    "    \"std_return_20\": \"ts_std((close / (ts_delay(close, 1) + 1e-8)) - 1, 20)\",  # 真实收益率波动率\n",
-    "    # [高阶] 夏普趋势比率 - 惩罚暴涨暴跌，奖励稳健爬坡\n",
-    "    \"sharpe_ratio_20\": \"ts_mean(close / (ts_delay(close, 1) + 1e-8) - 1, 20) / (ts_std(close / (ts_delay(close, 1) + 1e-8) - 1, 20) + 1e-8)\",\n",
-    "    # [高阶] 尾部崩盘风险 - 过去一个月最大单日跌幅\n",
-    "    \"min_ret_20\": \"ts_min(close / (ts_delay(close, 1) + 1e-8) - 1, 20)\",\n",
-    "    # [高阶] 波动率挤压比 - 寻找盘整到极致面临变盘的股票 (布林带收口)\n",
-    "    \"volatility_squeeze_5_60\": \"ts_std(close, 5) / (ts_std(close, 60) + 1e-8)\",\n",
-    "    # ================= 3. 日内微观结构与异象 (Intraday Microstructure & Anomalies) =================\n",
-    "    # [高阶] 隔夜与日内背离 - 差值越小说明主力越喜欢在盘中吸筹\n",
-    "    \"overnight_intraday_diff\": \"(open / (ts_delay(close, 1) + 1e-8) - 1) - (close / (open + 1e-8) - 1)\",\n",
-    "    # [高阶] 上影线抛压极值 - 冲高回落被套牢的概率\n",
-    "    \"upper_shadow_ratio\": \"(high - ((open + close + abs(open - close)) / 2)) / (high - low + 1e-8)\",\n",
-    "    # [高阶] 资金沉淀率 - 衡量主力日内高抛低吸洗盘的剧烈程度\n",
-    "    \"capital_retention_20\": \"ts_sum(abs(close - open), 20) / (ts_sum(high - low, 20) + 1e-8)\",\n",
-    "    # [高阶] MAX 彩票效应 - 反转因子，剔除近期有过妖股连板特征的标的\n",
-    "    \"max_ret_20\": \"ts_max(close / (ts_delay(close, 1) + 1e-8) - 1, 20)\",\n",
-    "    # ================= 4. 量能、流动性与量价背离 (Volume, Liquidity & Divergence) =================\n",
-    "    \"volume_ratio_5_20\": \"ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)\",  # 相对放量比\n",
-    "    \"turnover_rate_mean_5\": \"ts_mean(turnover_rate, 5)\",  # 活跃度\n",
-    "    \"turnover_deviation\": \"(turnover_rate - ts_mean(turnover_rate, 10)) / (ts_std(turnover_rate, 10) + 1e-8)\",  # 换手率偏离度\n",
-    "    # [高阶] Amihud 非流动性异象 (绝对核心) - 衡量砸盘/拉升的摩擦成本\n",
-    "    \"amihud_illiq_20\": \"ts_mean(abs(close / (ts_delay(close, 1) + 1e-8) - 1) / (amount + 1e-8), 20)\",\n",
-    "    # [高阶] 换手率惩罚因子 - 换手率忽高忽低说明游资接力，行情极不稳定\n",
-    "    \"turnover_cv_20\": \"ts_std(turnover_rate, 20) / (ts_mean(turnover_rate, 20) + 1e-8)\",\n",
-    "    # [高阶] 纯粹量价相关性 - 检验是否是\"放量上涨，缩量下跌\"的良性多头\n",
-    "    \"pv_corr_20\": \"ts_corr(close / (ts_delay(close, 1) + 1e-8) - 1, vol, 20)\",\n",
-    "    # [高阶] 收盘价与均价背离 - 专门抓尾盘突袭拉升骗线的股票\n",
-    "    \"close_vwap_deviation\": \"close / (amount / (vol * 100 + 1e-8) + 1e-8) - 1\",\n",
-    "    # ================= 5. 基本面财务特征 (Fundamental Quality & Structure) =================\n",
-    "    \"roe\": \"n_income / (total_hldr_eqy_exc_min_int + 1e-8)\",  # 净资产收益率\n",
-    "    \"roa\": \"n_income / (total_assets + 1e-8)\",  # 总资产收益率\n",
-    "    \"profit_margin\": \"n_income / (revenue + 1e-8)\",  # 销售净利率\n",
-    "    \"debt_to_equity\": \"total_liab / (total_hldr_eqy_exc_min_int + 1e-8)\",  # 杠杆率\n",
-    "    \"current_ratio\": \"total_cur_assets / (total_cur_liab + 1e-8)\",  # 短期偿债安全垫\n",
-    "    # [高阶] 利润同比增速 (日频延后252天等于去年同期)\n",
-    "    \"net_profit_yoy\": \"(n_income / (ts_delay(n_income, 252) + 1e-8)) - 1\",\n",
-    "    # [高阶] 营收同比增速\n",
-    "    \"revenue_yoy\": \"(revenue / (ts_delay(revenue, 252) + 1e-8)) - 1\",\n",
-    "    # [高阶] 资产负债表扩张斜率 - 剔除单纯靠举债扩张的公司\n",
-    "    \"healthy_expansion_velocity\": \"(total_assets / (ts_delay(total_assets, 252) + 1e-8) - 1) - (total_liab / (ts_delay(total_liab, 252) + 1e-8) - 1)\",\n",
-    "    # ================= 6. 基本面估值与截面动量共振 (Valuation & Cross-Sectional Ranking) =================\n",
-    "    # 估值水平绝对值 (Tushare 市值单位需要 * 10000 转换为元)\n",
-    "    \"EP\": \"n_income / (total_mv * 10000 + 1e-8)\",  # 盈利收益率 (1/PE)\n",
-    "    \"BP\": \"total_hldr_eqy_exc_min_int / (total_mv * 10000 + 1e-8)\",  # 账面市值比 (1/PB)\n",
-    "    \"CP\": \"n_cashflow_act / (total_mv * 10000 + 1e-8)\",  # 经营现金流收益率 (1/PCF)\n",
-    "    # 全市场截面排名因子\n",
-    "    \"market_cap_rank\": \"cs_rank(total_mv)\",  # 规模因子 (Size)\n",
-    "    \"turnover_rank\": \"cs_rank(turnover_rate)\",\n",
-    "    \"return_5_rank\": \"cs_rank((close / (ts_delay(close, 5) + 1e-8)) - 1)\",\n",
-    "    \"EP_rank\": \"cs_rank(n_income / (total_mv + 1e-8))\",  # 谁最便宜\n",
-    "    # [高阶] 戴维斯双击动量 - 估值相对上一年是否在扩张\n",
-    "    \"pe_expansion_trend\": \"(total_mv / (n_income + 1e-8)) / (ts_delay(total_mv, 60) / (ts_delay(n_income, 60) + 1e-8) + 1e-8) - 1\",\n",
-    "    # [高阶] 业绩与价格背离度 - 截面做差：利润排名全市场第一，但近20日价格排名倒数第一，捕捉被错杀的潜伏股\n",
-    "    \"value_price_divergence\": \"cs_rank((n_income - ts_delay(n_income, 252)) / (abs(ts_delay(n_income, 252)) + 1e-8)) - cs_rank(close / (ts_delay(close, 20) + 1e-8))\",\n",
-    "    # [高阶] 流动性溢价调整后市值 - 识别僵尸大盘股和极度活跃的小微盘\n",
-    "    \"active_market_cap\": \"total_mv * ts_mean(turnover_rate, 20)\",\n",
-    "    \"ebit_rank\": \"cs_rank(ebit)\",\n",
-    "}\n",
-    "\n",
-    "# Label 因子定义（不参与训练，用于计算目标）\n",
-    "LABEL_FACTOR = {\n",
-    "    LABEL_NAME: \"(ts_delay(close, -5) / ts_delay(open, -1)) - 1\",  # 未来5日收益率\n",
-    "}"
-   ]
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### 3.2 训练参数配置"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": [
-    "# 日期范围配置（正确的 train/val/test 三分法）\n",
-    "# Train: 用于训练模型参数\n",
-    "# Val: 用于验证/早停/调参（位于 train 之后，test 之前）\n",
-    "# Test: 仅用于最终评估，完全独立于训练过程\n",
-    "TRAIN_START = \"20200101\"\n",
-    "TRAIN_END = \"20231231\"\n",
-    "VAL_START = \"20240101\"\n",
-    "VAL_END = \"20241231\"\n",
-    "TEST_START = \"20250101\"\n",
-    "TEST_END = \"20261231\"\n",
+    "# 获取 Label 因子定义\n",
+    "LABEL_FACTOR = get_label_factor(LABEL_NAME)\n",
     "\n",
     "# 模型参数配置\n",
     "MODEL_PARAMS = {\n",
@@ -326,60 +98,7 @@
     "    # 数值稳定性\n",
     "    \"verbose\": -1,\n",
     "    \"random_state\": 42,\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 股票池筛选函数\n",
-    "# 使用新的 StockPoolManager API：传入自定义筛选函数和所需列/因子\n",
-    "# 筛选函数接收单日 DataFrame，返回布尔 Series\n",
-    "#\n",
-    "# 筛选逻辑（针对单日数据）：\n",
-    "# 1. 先排除创业板、科创板、北交所（ST过滤由STFilter组件处理）\n",
-    "# 2. 然后选取市值最小的500只股票\n",
-    "def stock_pool_filter(df: pl.DataFrame) -> pl.Series:\n",
-    "    \"\"\"股票池筛选函数（单日数据）\n",
-    "\n",
-    "    筛选条件：\n",
-    "    1. 排除创业板（代码以 300 开头）\n",
-    "    2. 排除科创板（代码以 688 开头）\n",
-    "    3. 排除北交所（代码以 8、9 或 4 开头）\n",
-    "    4. 选取当日市值最小的500只股票\n",
-    "    \"\"\"\n",
-    "    # 代码筛选（排除创业板、科创板、北交所）\n",
-    "    code_filter = (\n",
-    "        ~df[\"ts_code\"].str.starts_with(\"30\")  # 排除创业板\n",
-    "        & ~df[\"ts_code\"].str.starts_with(\"68\")  # 排除科创板\n",
-    "        & ~df[\"ts_code\"].str.starts_with(\"8\")  # 排除北交所\n",
-    "        & ~df[\"ts_code\"].str.starts_with(\"9\")  # 排除北交所\n",
-    "        & ~df[\"ts_code\"].str.starts_with(\"4\")  # 排除北交所\n",
-    "    )\n",
-    "\n",
-    "    # 在已筛选的股票中，选取市值最小的500只\n",
-    "    # 按市值升序排序，取前500\n",
-    "    valid_df = df.filter(code_filter)\n",
-    "    n = min(1000, len(valid_df))\n",
-    "    small_cap_codes = valid_df.sort(\"total_mv\").head(n)[\"ts_code\"]\n",
-    "\n",
-    "    # 返回布尔 Series：是否在被选中的股票中\n",
-    "    return df[\"ts_code\"].is_in(small_cap_codes)\n",
-    "\n",
-    "\n",
-    "# 定义筛选所需的基础列\n",
-    "STOCK_FILTER_REQUIRED_COLUMNS = [\"total_mv\"]  # ST过滤由STFilter组件处理\n",
-    "\n",
-    "# 可选：定义筛选所需的因子（如果需要用因子进行筛选）\n",
-    "# STOCK_FILTER_REQUIRED_FACTORS = {\n",
-    "#     \"market_cap_rank\": \"cs_rank(total_mv)\",\n",
-    "# }\n",
-    "\n",
-    "\n",
-    "# 输出配置（相对于本文件所在目录）\n",
-    "OUTPUT_DIR = \"output\"\n",
-    "SAVE_PREDICTIONS = True\n",
-    "PERSIST_MODEL = False\n",
-    "\n",
-    "# Top N 配置：每日推荐股票数量\n",
-    "TOP_N = 5  # 可调整为 10, 20 等"
+    "}"
    ]
   },
   {
@@ -420,6 +139,7 @@
     "    feature_cols=feature_cols,\n",
     "    start_date=TRAIN_START,\n",
     "    end_date=TEST_END,\n",
+    "    label_name=LABEL_NAME,\n",
     ")\n",
     "\n",
     "# 4. 打印配置信息\n",
@@ -515,8 +235,6 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "# 步骤 2: 划分训练/验证/测试集（正确的三分法）\n",
     "print(\"\\n[步骤 2/6] 划分训练集、验证集和测试集\")\n",
@@ -550,7 +268,9 @@
     "    train_data = filtered_data\n",
     "    test_data = filtered_data\n",
     "    print(\"  未配置划分器，全部作为训练集\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -579,8 +299,6 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "# 步骤 4: 训练集数据处理\n",
     "print(\"\\n[步骤 4/7] 训练集数据处理\")\n",
@@ -608,7 +326,9 @@
     "    null_count = train_data[col].null_count()\n",
     "    if null_count > 0:\n",
     "        print(f\"      {col}: {null_count} ({null_count / len(train_data) * 100:.2f}%)\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -828,8 +548,6 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
     "print(\"训练结果\")\n",
@@ -855,7 +573,9 @@
     "sample_data = results.filter(results[\"trade_date\"] == sample_date).head(10)\n",
     "print(f\"\\n示例日期 {sample_date} 的前10条预测:\")\n",
     "print(sample_data.select([\"ts_code\", \"trade_date\", target_col, \"prediction\"]))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -978,6 +698,61 @@
     "- 可以帮助理解哪些因子最有效"
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "print(\"绘制特征重要性...\")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "lgb.plot_importance(\n",
+    "    booster,\n",
+    "    max_num_features=20,\n",
+    "    importance_type=\"gain\",\n",
+    "    title=\"Feature Importance (Gain)\",\n",
+    "    ax=ax,\n",
+    ")\n",
+    "ax.set_xlabel(\"Importance (Gain)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 打印重要性排名\n",
+    "importance_gain = pd.Series(\n",
+    "    booster.feature_importance(importance_type=\"gain\"), index=feature_cols\n",
+    ").sort_values(ascending=False)\n",
+    "\n",
+    "print(\"\\n[特征重要性排名 - Gain]\")\n",
+    "print(importance_gain)\n",
+    "\n",
+    "# 识别低重要性特征\n",
+    "zero_importance = importance_gain[importance_gain == 0].index.tolist()\n",
+    "if zero_importance:\n",
+    "    print(f\"\\n[低重要性特征] 以下{len(zero_importance)}个特征重要性为0，可考虑删除:\")\n",
+    "    for feat in zero_importance:\n",
+    "        print(f\"  - {feat}\")\n",
+    "else:\n",
+    "    print(\"\\n所有特征都有一定重要性\")\n"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# 导入可视化库\n",
+    "import matplotlib.pyplot as plt\n",
+    "import lightgbm as lgb\n",
+    "import pandas as pd\n",
+    "\n",
+    "# 从封装的model中取出底层Booster\n",
+    "booster = model.model\n",
+    "print(f\"模型类型: {type(booster)}\")\n",
+    "print(f\"特征数量: {len(feature_cols)}\")"
+   ]
+  },
   {
    "metadata": {},
    "cell_type": "code",
diff --git a/src/experiment/regression.py b/src/experiment/regression.py
index 60a20db..d593cd6 100644
--- a/src/experiment/regression.py
+++ b/src/experiment/regression.py
@@ -3,7 +3,6 @@
 # %%
 import os
 from datetime import datetime
-from typing import List
 
 import polars as pl
 
@@ -13,7 +12,6 @@ from src.training import (
     LightGBMModel,
     STFilter,
     StandardScaler,
-    # StockFilterConfig,  # 已删除，使用 StockPoolManager + filter_func 替代
     StockPoolManager,
     Trainer,
     Winsorizer,
@@ -22,167 +20,38 @@ from src.training import (
 )
 from src.training.config import TrainingConfig
 
-
-# %% md
-# ## 2. 定义辅助函数
-# %%
-def register_factors(
-    engine: FactorEngine,
-    selected_factors: List[str],
-    factor_definitions: dict,
-    label_factor: dict,
-) -> List[str]:
-    """注册因子（selected_factors 从 metadata 查询，factor_definitions 用 DSL 表达式注册）"""
-    print("=" * 80)
-    print("注册因子")
-    print("=" * 80)
-
-    # 注册 SELECTED_FACTORS 中的因子（已在 metadata 中）
-    print("\n注册特征因子（从 metadata）:")
-    for name in selected_factors:
-        engine.add_factor(name)
-        print(f"  - {name}")
-
-    # 注册 FACTOR_DEFINITIONS 中的因子（通过表达式，尚未在 metadata 中）
-    print("\n注册特征因子（表达式）:")
-    for name, expr in factor_definitions.items():
-        engine.add_factor(name, expr)
-        print(f"  - {name}: {expr}")
-
-    # 注册 label 因子（通过表达式）
-    print("\n注册 Label 因子（表达式）:")
-    for name, expr in label_factor.items():
-        engine.add_factor(name, expr)
-        print(f"  - {name}: {expr}")
-
-    # 特征列 = SELECTED_FACTORS + FACTOR_DEFINITIONS 的 keys
-    feature_cols = selected_factors + list(factor_definitions.keys())
-
-    print(f"\n特征因子数: {len(feature_cols)}")
-    print(f"  - 来自 metadata: {len(selected_factors)}")
-    print(f"  - 来自表达式: {len(factor_definitions)}")
-    print(f"Label: {list(label_factor.keys())[0]}")
-    print(f"已注册因子总数: {len(engine.list_registered())}")
-
-    return feature_cols
-
-
-def prepare_data(
-    engine: FactorEngine,
-    feature_cols: List[str],
-    start_date: str,
-    end_date: str,
-) -> pl.DataFrame:
-    print("\n" + "=" * 80)
-    print("准备数据")
-    print("=" * 80)
-
-    # 计算因子（全市场数据）
-    print(f"\n计算因子: {start_date} - {end_date}")
-    factor_names = feature_cols + [LABEL_NAME]  # 包含 label
-
-    data = engine.compute(
-        factor_names=factor_names,
-        start_date=start_date,
-        end_date=end_date,
-    )
-
-    print(f"数据形状: {data.shape}")
-    print(f"数据列: {data.columns}")
-    print(f"\n前5行预览:")
-    print(data.head())
-
-    return data
+# 从 common 模块导入共用配置和函数
+from src.experiment.common import (
+    SELECTED_FACTORS,
+    FACTOR_DEFINITIONS,
+    get_label_factor,
+    register_factors,
+    prepare_data,
+    TRAIN_START,
+    TRAIN_END,
+    VAL_START,
+    VAL_END,
+    TEST_START,
+    TEST_END,
+    stock_pool_filter,
+    STOCK_FILTER_REQUIRED_COLUMNS,
+    OUTPUT_DIR,
+    SAVE_PREDICTIONS,
+    PERSIST_MODEL,
+    TOP_N,
+)
 
 
 # %% md
-# ## 3. 配置参数
+# ## 2. 配置参数
 #
-# ### 3.1 因子定义
+# ### 2.1 标签定义
 # %%
-# 特征因子定义字典：新增因子只需在此处添加一行
+# Label 名称（回归任务使用连续收益率）
 LABEL_NAME = "future_return_5"
 
-# 当前选择的因子列表（从 FACTOR_DEFINITIONS 中选择要使用的因子）
-SELECTED_FACTORS = [
-    # ================= 1. 价格、趋势与路径依赖 =================
-    "ma_5",
-    "ma_20",
-    "ma_ratio_5_20",
-    "bias_10",
-    "high_low_ratio",
-    "bbi_ratio",
-    "return_5",
-    "return_20",
-    "kaufman_ER_20",
-    "mom_acceleration_10_20",
-    "drawdown_from_high_60",
-    "up_days_ratio_20",
-    # ================= 2. 波动率、风险调整与高阶矩 =================
-    "volatility_5",
-    "volatility_20",
-    "volatility_ratio",
-    "std_return_20",
-    "sharpe_ratio_20",
-    "min_ret_20",
-    "volatility_squeeze_5_60",
-    # ================= 3. 日内微观结构与异象 =================
-    "overnight_intraday_diff",
-    "upper_shadow_ratio",
-    "capital_retention_20",
-    "max_ret_20",
-    # ================= 4. 量能、流动性与量价背离 =================
-    "volume_ratio_5_20",
-    "turnover_rate_mean_5",
-    "turnover_deviation",
-    "amihud_illiq_20",
-    "turnover_cv_20",
-    "pv_corr_20",
-    "close_vwap_deviation",
-    # ================= 5. 基本面财务特征 =================
-    "roe",
-    "roa",
-    "profit_margin",
-    "debt_to_equity",
-    "current_ratio",
-    "net_profit_yoy",
-    "revenue_yoy",
-    "healthy_expansion_velocity",
-    # ================= 6. 基本面估值与截面动量共振 =================
-    "EP",
-    "BP",
-    "CP",
-    "market_cap_rank",
-    "turnover_rank",
-    "return_5_rank",
-    "EP_rank",
-    "pe_expansion_trend",
-    "value_price_divergence",
-    "active_market_cap",
-    "ebit_rank",
-]
-
-# 因子定义字典（完整因子库）
-FACTOR_DEFINITIONS = {
-}
-
-# Label 因子定义（不参与训练，用于计算目标）
-LABEL_FACTOR = {
-    LABEL_NAME: "(ts_delay(close, -5) / ts_delay(open, -1)) - 1",  # 未来5日收益率
-}
-# %% md
-# ### 3.2 训练参数配置
-# %%
-# 日期范围配置（正确的 train/val/test 三分法）
-# Train: 用于训练模型参数
-# Val: 用于验证/早停/调参（位于 train 之后，test 之前）
-# Test: 仅用于最终评估，完全独立于训练过程
-TRAIN_START = "20200101"
-TRAIN_END = "20231231"
-VAL_START = "20240101"
-VAL_END = "20241231"
-TEST_START = "20250101"
-TEST_END = "20261231"
+# 获取 Label 因子定义
+LABEL_FACTOR = get_label_factor(LABEL_NAME)
 
 # 模型参数配置
 MODEL_PARAMS = {
@@ -207,59 +76,6 @@ MODEL_PARAMS = {
     "verbose": -1,
     "random_state": 42,
 }
-
-
-# 股票池筛选函数
-# 使用新的 StockPoolManager API：传入自定义筛选函数和所需列/因子
-# 筛选函数接收单日 DataFrame，返回布尔 Series
-#
-# 筛选逻辑（针对单日数据）：
-# 1. 先排除创业板、科创板、北交所（ST过滤由STFilter组件处理）
-# 2. 然后选取市值最小的500只股票
-def stock_pool_filter(df: pl.DataFrame) -> pl.Series:
-    """股票池筛选函数（单日数据）
-
-    筛选条件：
-    1. 排除创业板（代码以 300 开头）
-    2. 排除科创板（代码以 688 开头）
-    3. 排除北交所（代码以 8、9 或 4 开头）
-    4. 选取当日市值最小的500只股票
-    """
-    # 代码筛选（排除创业板、科创板、北交所）
-    code_filter = (
-        ~df["ts_code"].str.starts_with("30")  # 排除创业板
-        & ~df["ts_code"].str.starts_with("68")  # 排除科创板
-        & ~df["ts_code"].str.starts_with("8")  # 排除北交所
-        & ~df["ts_code"].str.starts_with("9")  # 排除北交所
-        & ~df["ts_code"].str.starts_with("4")  # 排除北交所
-    )
-
-    # 在已筛选的股票中，选取市值最小的500只
-    # 按市值升序排序，取前500
-    valid_df = df.filter(code_filter)
-    n = min(1000, len(valid_df))
-    small_cap_codes = valid_df.sort("total_mv").head(n)["ts_code"]
-
-    # 返回布尔 Series：是否在被选中的股票中
-    return df["ts_code"].is_in(small_cap_codes)
-
-
-# 定义筛选所需的基础列
-STOCK_FILTER_REQUIRED_COLUMNS = ["total_mv"]  # ST过滤由STFilter组件处理
-
-# 可选：定义筛选所需的因子（如果需要用因子进行筛选）
-# STOCK_FILTER_REQUIRED_FACTORS = {
-#     "market_cap_rank": "cs_rank(total_mv)",
-# }
-
-
-# 输出配置（相对于本文件所在目录）
-OUTPUT_DIR = "output"
-SAVE_PREDICTIONS = True
-PERSIST_MODEL = False
-
-# Top N 配置：每日推荐股票数量
-TOP_N = 5  # 可调整为 10, 20 等
 # %% md
 # ## 4. 训练流程
 #
@@ -288,6 +104,7 @@ data = prepare_data(
     feature_cols=feature_cols,
     start_date=TRAIN_START,
     end_date=TEST_END,
+    label_name=LABEL_NAME,
 )
 
 # 4. 打印配置信息