diff --git a/src/data/financial_loader.py b/src/data/financial_loader.py
index 8e2fd71..4b63be1 100644
--- a/src/data/financial_loader.py
+++ b/src/data/financial_loader.py
@@ -128,6 +128,10 @@ class FinancialLoader:
             strategy="backward",
         )
 
+        # 删除 f_ann_date_right 列（如果有），避免多次 asof join 时列名冲突
+        if "f_ann_date_right" in merged.columns:
+            merged = merged.drop("f_ann_date_right")
+
         return merged
 
     def _load_from_db(
diff --git a/src/experiment/regression.ipynb b/src/experiment/regression.ipynb
index 84506b4..5f5cfde 100644
--- a/src/experiment/regression.ipynb
+++ b/src/experiment/regression.ipynb
@@ -1,16 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# LightGBM 回归训练示例 - 使用因子字符串表达式\n",
-    "\n",
-    "使用字符串表达式定义因子，训练 LightGBM 回归模型预测未来5日收益率。\n",
-    "\n",
-    "**Label**: return_5 = (ts_delay(close, -5) / close) - 1  # 未来5日收益率"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -22,8 +11,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:05:52.792500Z",
-     "start_time": "2026-03-07T17:05:52.789046Z"
+     "end_time": "2026-03-08T03:32:40.563481Z",
+     "start_time": "2026-03-08T03:32:39.994884Z"
     }
    },
    "source": [
@@ -48,7 +37,7 @@
     "from src.training.config import TrainingConfig"
    ],
    "outputs": [],
-   "execution_count": 19
+   "execution_count": 1
   },
   {
    "cell_type": "markdown",
@@ -60,8 +49,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:05:52.800138Z",
-     "start_time": "2026-03-07T17:05:52.795957Z"
+     "end_time": "2026-03-08T03:32:40.574743Z",
+     "start_time": "2026-03-08T03:32:40.571165Z"
     }
    },
    "cell_type": "code",
@@ -121,7 +110,7 @@
     "    return data"
    ],
    "outputs": [],
-   "execution_count": 20
+   "execution_count": 2
   },
   {
    "cell_type": "markdown",
@@ -135,8 +124,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:05:52.808283Z",
-     "start_time": "2026-03-07T17:05:52.803432Z"
+     "end_time": "2026-03-08T03:32:40.585729Z",
+     "start_time": "2026-03-08T03:32:40.579837Z"
     }
    },
    "cell_type": "code",
@@ -165,7 +154,21 @@
     "    \"market_cap_rank\": \"cs_rank(total_mv)\",\n",
     "    # 7. 价格位置因子\n",
     "    \"high_low_ratio\": \"(close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\",\n",
-    "    \"n_income\": \"cs_rank(n_income)\",\n",
+    "    \"n_income_rank\": \"cs_rank(n_income)\",  # 净利润截面排名\n",
+    "    # 8. 财务数据因子（来自利润表 financial_income）\n",
+    "    \"operate_profit_rank\": \"cs_rank(operate_profit)\",  # 营业利润截面排名\n",
+    "    \"total_profit_rank\": \"cs_rank(total_profit)\",  # 利润总额截面排名\n",
+    "    \"ebit_rank\": \"cs_rank(ebit)\",  # 息税前利润截面排名\n",
+    "    \"ebitda_rank\": \"cs_rank(ebitda)\",  # 息税折旧摊销前利润截面排名\n",
+    "    # 9. 财务数据因子（来自资产负债表 financial_balance）\n",
+    "    \"total_liab_rank\": \"cs_rank(total_liab)\",  # 总负债截面排名\n",
+    "    \"money_cap_rank\": \"cs_rank(money_cap)\",  # 货币资金截面排名\n",
+    "    # 10. 财务数据因子（来自现金流量表 financial_cashflow）\n",
+    "    \"n_cashflow_act_rank\": \"cs_rank(n_cashflow_act)\",  # 经营活动现金流净额截面排名\n",
+    "    # 11. 财务估值因子\n",
+    "    \"profit_to_market_cap\": \"n_income / (total_mv + 1e-8)\",  # 净利润率（净利润/市值）\n",
+    "    \"cashflow_to_market_cap\": \"n_cashflow_act / (total_mv + 1e-8)\",  # 经营现金流/市值\n",
+    "    \"operate_profit_to_market_cap\": \"operate_profit / (total_mv + 1e-8)\",  # 营业利润/市值\n",
     "}\n",
     "\n",
     "# Label 因子定义（不参与训练，用于计算目标）\n",
@@ -174,7 +177,7 @@
     "}"
    ],
    "outputs": [],
-   "execution_count": 21
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -187,8 +190,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:05:52.816594Z",
-     "start_time": "2026-03-07T17:05:52.811772Z"
+     "end_time": "2026-03-08T03:32:40.593766Z",
+     "start_time": "2026-03-08T03:32:40.590001Z"
     }
    },
    "source": [
@@ -244,10 +247,13 @@
     "# 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"
+    "PERSIST_MODEL = False\n",
+    "\n",
+    "# Top N 配置：每日推荐股票数量\n",
+    "TOP_N = 2  # 可调整为 10, 20 等"
    ],
    "outputs": [],
-   "execution_count": 22
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",
@@ -262,8 +268,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:05:57.952733Z",
-     "start_time": "2026-03-07T17:05:52.822302Z"
+     "end_time": "2026-03-08T03:32:46.956583Z",
+     "start_time": "2026-03-08T03:32:40.597894Z"
     }
    },
    "source": [
@@ -374,14 +380,24 @@
       "  - vol_ma20: ts_mean(vol, 20)\n",
       "  - market_cap_rank: cs_rank(total_mv)\n",
       "  - high_low_ratio: (close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\n",
-      "  - n_income: cs_rank(n_income)\n",
+      "  - n_income_rank: cs_rank(n_income)\n",
+      "  - operate_profit_rank: cs_rank(operate_profit)\n",
+      "  - total_profit_rank: cs_rank(total_profit)\n",
+      "  - ebit_rank: cs_rank(ebit)\n",
+      "  - ebitda_rank: cs_rank(ebitda)\n",
+      "  - total_liab_rank: cs_rank(total_liab)\n",
+      "  - money_cap_rank: cs_rank(money_cap)\n",
+      "  - n_cashflow_act_rank: cs_rank(n_cashflow_act)\n",
+      "  - profit_to_market_cap: n_income / (total_mv + 1e-8)\n",
+      "  - cashflow_to_market_cap: n_cashflow_act / (total_mv + 1e-8)\n",
+      "  - operate_profit_to_market_cap: operate_profit / (total_mv + 1e-8)\n",
       "\n",
       "注册 Label 因子:\n",
       "  - return_5: (ts_delay(close, -5) / close) - 1\n",
       "\n",
-      "特征因子数: 15\n",
+      "特征因子数: 25\n",
       "Label: return_5\n",
-      "已注册因子总数: 16\n",
+      "已注册因子总数: 26\n",
       "\n",
       "[3] 准备数据\n",
       "\n",
@@ -404,33 +420,40 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "数据形状: (7044952, 24)\n",
-      "数据列: ['ts_code', 'trade_date', 'close', 'vol', 'high', 'low', 'total_mv', 'f_ann_date', 'n_income', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'return_5']\n",
+      "数据形状: (7044952, 42)\n",
+      "数据列: ['ts_code', 'trade_date', 'vol', 'close', 'low', 'high', 'total_mv', 'f_ann_date', 'n_income', 'ebitda', 'total_profit', 'ebit', 'operate_profit', 'money_cap', 'total_liab', 'n_cashflow_act', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'n_income_rank', 'operate_profit_rank', 'total_profit_rank', 'ebit_rank', 'ebitda_rank', 'total_liab_rank', 'money_cap_rank', 'n_cashflow_act_rank', 'profit_to_market_cap', 'cashflow_to_market_cap', 'operate_profit_to_market_cap', 'return_5']\n",
       "\n",
       "前5行预览:\n",
-      "shape: (5, 24)\n",
-      "┌───────────┬────────────┬─────────┬───────────┬───┬──────────┬────────────┬───────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ close   ┆ vol       ┆ … ┆ vol_ma20 ┆ market_cap ┆ high_low_ ┆ return_5  │\n",
-      "│ ---       ┆ ---        ┆ ---     ┆ ---       ┆   ┆ ---      ┆ _rank      ┆ ratio     ┆ ---       │\n",
-      "│ str       ┆ str        ┆ f64     ┆ f64       ┆   ┆ f64      ┆ ---        ┆ ---       ┆ f64       │\n",
-      "│           ┆            ┆         ┆           ┆   ┆          ┆ f64        ┆ f64       ┆           │\n",
-      "╞═══════════╪════════════╪═════════╪═══════════╪═══╪══════════╪════════════╪═══════════╪═══════════╡\n",
-      "│ 000001.SZ ┆ 20200102   ┆ 1841.69 ┆ 1.5302e6  ┆ … ┆ null     ┆ 0.993583   ┆ null      ┆ -0.004746 │\n",
-      "│ 000001.SZ ┆ 20200103   ┆ 1875.53 ┆ 1.1162e6  ┆ … ┆ null     ┆ 0.993585   ┆ null      ┆ -0.02852  │\n",
-      "│ 000001.SZ ┆ 20200106   ┆ 1863.52 ┆ 862083.5  ┆ … ┆ null     ┆ 0.993588   ┆ null      ┆ -0.004685 │\n",
-      "│ 000001.SZ ┆ 20200107   ┆ 1872.26 ┆ 728607.56 ┆ … ┆ null     ┆ 0.993588   ┆ null      ┆ -0.022743 │\n",
-      "│ 000001.SZ ┆ 20200108   ┆ 1818.76 ┆ 847824.12 ┆ … ┆ null     ┆ 0.993586   ┆ null      ┆ -0.008401 │\n",
-      "└───────────┴────────────┴─────────┴───────────┴───┴──────────┴────────────┴───────────┴───────────┘\n",
+      "shape: (5, 42)\n",
+      "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
+      "│ ts_code   ┆ trade_date ┆ vol       ┆ close   ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5  │\n",
+      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---       │\n",
+      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64       │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆           │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
+      "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
+      "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+      "│ 000001.SZ ┆ 20200102   ┆ 1.5302e6  ┆ 1841.69 ┆ … ┆ 721.52104 ┆ 2580.5045 ┆ 938.15146 ┆ -0.004746 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 1         ┆ 33        ┆ 4         ┆           │\n",
+      "│ 000001.SZ ┆ 20200103   ┆ 1.1162e6  ┆ 1875.53 ┆ … ┆ 708.50174 ┆ 2533.9412 ┆ 921.22323 ┆ -0.02852  │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 4         ┆ 96        ┆ 7         ┆           │\n",
+      "│ 000001.SZ ┆ 20200106   ┆ 862083.5  ┆ 1863.52 ┆ … ┆ 713.06736 ┆ 2550.2701 ┆ 927.15964 ┆ -0.004685 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 8         ┆ 5         ┆ 9         ┆           │\n",
+      "│ 000001.SZ ┆ 20200107   ┆ 728607.56 ┆ 1872.26 ┆ … ┆ 709.74110 ┆ 2538.3738 ┆ 922.83470 ┆ -0.022743 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 6         ┆ 46        ┆ 6         ┆           │\n",
+      "│ 000001.SZ ┆ 20200108   ┆ 847824.12 ┆ 1818.76 ┆ … ┆ 730.61584 ┆ 2613.0319 ┆ 949.97690 ┆ -0.008401 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 4         ┆ 01        ┆ 3         ┆           │\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 - 20251231\n",
-      "[配置] 特征数: 15\n",
+      "[配置] 特征数: 25\n",
       "[配置] 目标变量: return_5\n"
      ]
     }
    ],
-   "execution_count": 23
+   "execution_count": 5
   },
   {
    "cell_type": "markdown",
@@ -443,8 +466,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:05.023337Z",
-     "start_time": "2026-03-07T17:05:57.962781Z"
+     "end_time": "2026-03-08T03:32:54.939339Z",
+     "start_time": "2026-03-08T03:32:46.966714Z"
     }
    },
    "source": [
@@ -480,22 +503,22 @@
       "[步骤 1/6] 股票池筛选\n",
       "------------------------------------------------------------\n",
       "  执行每日独立筛选股票池...\n",
-      "  筛选前数据规模: (7044952, 24)\n",
-      "  筛选后数据规模: (4532198, 24)\n",
+      "  筛选前数据规模: (7044952, 42)\n",
+      "  筛选后数据规模: (4532198, 42)\n",
       "  筛选前股票数: 5678\n",
       "  筛选后股票数: 3359\n",
       "  删除记录数: 2512754\n"
      ]
     }
    ],
-   "execution_count": 24
+   "execution_count": 6
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:05.994624Z",
-     "start_time": "2026-03-07T17:06:05.030217Z"
+     "end_time": "2026-03-08T03:32:56.279010Z",
+     "start_time": "2026-03-08T03:32:54.952714Z"
     }
    },
    "source": [
@@ -540,9 +563,9 @@
       "\n",
       "[步骤 2/6] 划分训练集、验证集和测试集\n",
       "------------------------------------------------------------\n",
-      "  训练集数据规模: (2991506, 24)\n",
-      "  验证集数据规模: (769485, 24)\n",
-      "  测试集数据规模: (771207, 24)\n",
+      "  训练集数据规模: (2991506, 42)\n",
+      "  验证集数据规模: (769485, 42)\n",
+      "  测试集数据规模: (771207, 42)\n",
       "  训练集股票数: 3297\n",
       "  验证集股票数: 3220\n",
       "  测试集股票数: 3215\n",
@@ -551,67 +574,80 @@
       "  测试集日期范围: 20250102 - 20251231\n",
       "\n",
       "  训练集前5行预览:\n",
-      "shape: (5, 24)\n",
-      "┌───────────┬────────────┬─────────┬───────────┬───┬──────────┬────────────┬───────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ close   ┆ vol       ┆ … ┆ vol_ma20 ┆ market_cap ┆ high_low_ ┆ return_5  │\n",
-      "│ ---       ┆ ---        ┆ ---     ┆ ---       ┆   ┆ ---      ┆ _rank      ┆ ratio     ┆ ---       │\n",
-      "│ str       ┆ str        ┆ f64     ┆ f64       ┆   ┆ f64      ┆ ---        ┆ ---       ┆ f64       │\n",
-      "│           ┆            ┆         ┆           ┆   ┆          ┆ f64        ┆ f64       ┆           │\n",
-      "╞═══════════╪════════════╪═════════╪═══════════╪═══╪══════════╪════════════╪═══════════╪═══════════╡\n",
-      "│ 000001.SZ ┆ 20200102   ┆ 1841.69 ┆ 1.5302e6  ┆ … ┆ null     ┆ 0.993583   ┆ null      ┆ -0.004746 │\n",
-      "│ 000002.SZ ┆ 20200102   ┆ 4832.29 ┆ 1012130.4 ┆ … ┆ null     ┆ 0.99492    ┆ null      ┆ -0.011057 │\n",
-      "│ 000004.SZ ┆ 20200102   ┆ 90.75   ┆ 17853.2   ┆ … ┆ null     ┆ 0.057219   ┆ null      ┆ -0.000441 │\n",
-      "│ 000005.SZ ┆ 20200102   ┆ 29.1    ┆ 104134.12 ┆ … ┆ null     ┆ 0.28984    ┆ null      ┆ 0.022337  │\n",
-      "│ 000006.SZ ┆ 20200102   ┆ 191.3   ┆ 124751.76 ┆ … ┆ null     ┆ 0.631551   ┆ null      ┆ 0.012964  │\n",
-      "└───────────┴────────────┴─────────┴───────────┴───┴──────────┴────────────┴───────────┴───────────┘\n",
+      "shape: (5, 42)\n",
+      "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
+      "│ ts_code   ┆ trade_date ┆ vol       ┆ close   ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5  │\n",
+      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---       │\n",
+      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64       │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆           │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
+      "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
+      "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+      "│ 000001.SZ ┆ 20200102   ┆ 1.5302e6  ┆ 1841.69 ┆ … ┆ 721.52104 ┆ 2580.5045 ┆ 938.15146 ┆ -0.004746 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 1         ┆ 33        ┆ 4         ┆           │\n",
+      "│ 000002.SZ ┆ 20200102   ┆ 1012130.4 ┆ 4832.29 ┆ … ┆ 776.91820 ┆ 47.131053 ┆ 1140.2493 ┆ -0.011057 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 1         ┆           ┆ 95        ┆           │\n",
+      "│ 000004.SZ ┆ 20200102   ┆ 17853.2   ┆ 90.75   ┆ … ┆ -69.58089 ┆ -52.61755 ┆ -24.82135 ┆ -0.000441 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 5         ┆ 4         ┆ 9         ┆           │\n",
+      "│ 000005.SZ ┆ 20200102   ┆ 104134.12 ┆ 29.1    ┆ … ┆ 142.55925 ┆ 385.57490 ┆ 208.12520 ┆ 0.022337  │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 6         ┆ 4         ┆ 2         ┆           │\n",
+      "│ 000006.SZ ┆ 20200102   ┆ 124751.76 ┆ 191.3   ┆ … ┆ 633.27582 ┆ 650.95370 ┆ 819.10495 ┆ 0.012964  │\n",
+      "│           ┆            ┆           ┆         ┆   ┆           ┆ 3         ┆ 5         ┆           │\n",
+      "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
       "\n",
       "  验证集前5行预览:\n",
-      "shape: (5, 24)\n",
-      "┌───────────┬────────────┬─────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ close   ┆ vol       ┆ … ┆ vol_ma20  ┆ market_ca ┆ high_low_ ┆ return_5  │\n",
-      "│ ---       ┆ ---        ┆ ---     ┆ ---       ┆   ┆ ---       ┆ p_rank    ┆ ratio     ┆ ---       │\n",
-      "│ str       ┆ str        ┆ f64     ┆ f64       ┆   ┆ f64       ┆ ---       ┆ ---       ┆ f64       │\n",
-      "│           ┆            ┆         ┆           ┆   ┆           ┆ f64       ┆ f64       ┆           │\n",
-      "╞═══════════╪════════════╪═════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-      "│ 000001.SZ ┆ 20240102   ┆ 1074.93 ┆ 1.1584e6  ┆ … ┆ 861841.07 ┆ 0.990616  ┆ 0.349245  ┆ -0.003256 │\n",
-      "│           ┆            ┆         ┆           ┆   ┆ 5         ┆           ┆           ┆           │\n",
-      "│ 000002.SZ ┆ 20240102   ┆ 1844.3  ┆ 811106.29 ┆ … ┆ 636017.52 ┆ 0.981982  ┆ 0.056113  ┆ -0.026601 │\n",
-      "│           ┆            ┆         ┆           ┆   ┆ 9         ┆           ┆           ┆           │\n",
-      "│ 000004.SZ ┆ 20240102   ┆ 65.59   ┆ 28867.0   ┆ … ┆ 45538.156 ┆ 0.07789   ┆ 0.297557  ┆ -0.014789 │\n",
-      "│ 000005.SZ ┆ 20240102   ┆ 10.38   ┆ 63028.0   ┆ … ┆ 146027.22 ┆ 0.017643  ┆ 0.301508  ┆ -0.05395  │\n",
-      "│           ┆            ┆         ┆           ┆   ┆ 7         ┆           ┆           ┆           │\n",
-      "│ 000006.SZ ┆ 20240102   ┆ 177.64  ┆ 261947.19 ┆ … ┆ 313782.29 ┆ 0.54955   ┆ 0.215557  ┆ -0.013454 │\n",
-      "│           ┆            ┆         ┆           ┆   ┆ 25        ┆           ┆           ┆           │\n",
-      "└───────────┴────────────┴─────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
+      "shape: (5, 42)\n",
+      "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
+      "│ ts_code   ┆ trade_date ┆ vol       ┆ close   ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5  │\n",
+      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---       │\n",
+      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64       │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆           │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
+      "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
+      "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+      "│ 000001.SZ ┆ 20240102   ┆ 1.1584e6  ┆ 1074.93 ┆ … ┆ 2217.6093 ┆ 6486.3743 ┆ 2744.2180 ┆ -0.003256 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 09        ┆ 45        ┆ 84        ┆           │\n",
+      "│ 000002.SZ ┆ 20240102   ┆ 811106.29 ┆ 1844.3  ┆ … ┆ 1736.4093 ┆ 19.432701 ┆ 2329.7434 ┆ -0.026601 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 99        ┆           ┆ 1         ┆           │\n",
+      "│ 000004.SZ ┆ 20240102   ┆ 28867.0   ┆ 65.59   ┆ … ┆ -168.7552 ┆ -184.4013 ┆ -192.7135 ┆ -0.014789 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 72        ┆ 85        ┆ 84        ┆           │\n",
+      "│ 000005.SZ ┆ 20240102   ┆ 63028.0   ┆ 10.38   ┆ … ┆ -96.94997 ┆ -295.0388 ┆ -46.06373 ┆ -0.05395  │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 7         ┆ 72        ┆ 6         ┆           │\n",
+      "│ 000006.SZ ┆ 20240102   ┆ 261947.19 ┆ 177.64  ┆ … ┆ -6.971845 ┆ -51.5536  ┆ -5.32671  ┆ -0.013454 │\n",
+      "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
       "\n",
       "  测试集前5行预览:\n",
-      "shape: (5, 24)\n",
-      "┌───────────┬────────────┬─────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ close   ┆ vol       ┆ … ┆ vol_ma20  ┆ market_ca ┆ high_low_ ┆ return_5  │\n",
-      "│ ---       ┆ ---        ┆ ---     ┆ ---       ┆   ┆ ---       ┆ p_rank    ┆ ratio     ┆ ---       │\n",
-      "│ str       ┆ str        ┆ f64     ┆ f64       ┆   ┆ f64       ┆ ---       ┆ ---       ┆ f64       │\n",
-      "│           ┆            ┆         ┆           ┆   ┆           ┆ f64       ┆ f64       ┆           │\n",
-      "╞═══════════╪════════════╪═════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-      "│ 000001.SZ ┆ 20250102   ┆ 1460.57 ┆ 1.8196e6  ┆ … ┆ 1.2151e6  ┆ 0.991617  ┆ 0.063478  ┆ -0.002622 │\n",
-      "│ 000002.SZ ┆ 20250102   ┆ 1291.92 ┆ 1.1827e6  ┆ … ┆ 1.2839e6  ┆ 0.970007  ┆ 0.020839  ┆ -0.022509 │\n",
-      "│ 000004.SZ ┆ 20250102   ┆ 57.63   ┆ 119760.37 ┆ … ┆ 159807.91 ┆ 0.099106  ┆ 0.131858  ┆ -0.064897 │\n",
-      "│           ┆            ┆         ┆           ┆   ┆ 8         ┆           ┆           ┆           │\n",
-      "│ 000006.SZ ┆ 20250102   ┆ 288.12  ┆ 307195.1  ┆ … ┆ 404264.21 ┆ 0.72392   ┆ 0.028423  ┆ -0.048278 │\n",
-      "│           ┆            ┆         ┆           ┆   ┆ 1         ┆           ┆           ┆           │\n",
-      "│ 000007.SZ ┆ 20250102   ┆ 58.15   ┆ 68219.01  ┆ … ┆ 88380.284 ┆ 0.183495  ┆ 0.391829  ┆ 0.015649  │\n",
-      "│           ┆            ┆         ┆           ┆   ┆ 5         ┆           ┆           ┆           │\n",
-      "└───────────┴────────────┴─────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘\n"
+      "shape: (5, 42)\n",
+      "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
+      "│ ts_code   ┆ trade_date ┆ vol       ┆ close   ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5  │\n",
+      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---       │\n",
+      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64       │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆           │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
+      "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
+      "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+      "│ 000001.SZ ┆ 20250102   ┆ 1.8196e6  ┆ 1460.57 ┆ … ┆ 1791.1304 ┆ 6183.5904 ┆ 2158.1117 ┆ -0.002622 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 08        ┆ 38        ┆ 45        ┆           │\n",
+      "│ 000002.SZ ┆ 20250102   ┆ 1.1827e6  ┆ 1291.92 ┆ … ┆ -1933.116 ┆ -1110.658 ┆ -1729.069 ┆ -0.022509 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 105       ┆ 303       ┆ 737       ┆           │\n",
+      "│ 000004.SZ ┆ 20250102   ┆ 119760.37 ┆ 57.63   ┆ … ┆ -199.1144 ┆ -126.8907 ┆ -197.3308 ┆ -0.064897 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 31        ┆ 63        ┆ 47        ┆           │\n",
+      "│ 000006.SZ ┆ 20250102   ┆ 307195.1  ┆ 288.12  ┆ … ┆ -646.1294 ┆ 74.343232 ┆ -637.5489 ┆ -0.048278 │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ 33        ┆           ┆ 17        ┆           │\n",
+      "│ 000007.SZ ┆ 20250102   ┆ 68219.01  ┆ 58.15   ┆ … ┆ 6.740918  ┆ 108.91759 ┆ 22.556002 ┆ 0.015649  │\n",
+      "│           ┆            ┆           ┆         ┆   ┆           ┆ 8         ┆           ┆           │\n",
+      "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n"
      ]
     }
    ],
-   "execution_count": 25
+   "execution_count": 7
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:06.818724Z",
-     "start_time": "2026-03-07T17:06:06.002551Z"
+     "end_time": "2026-03-08T03:32:57.302228Z",
+     "start_time": "2026-03-08T03:32:56.290367Z"
     }
    },
    "source": [
@@ -665,22 +701,27 @@
       "      处理后记录数: 2991506\n",
       "\n",
       "  训练集处理后前5行预览:\n",
-      "shape: (5, 24)\n",
-      "┌───────────┬───────────┬───────────┬───────────┬───┬──────────┬───────────┬───────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_dat ┆ close     ┆ vol       ┆ … ┆ vol_ma20 ┆ market_ca ┆ high_low_ ┆ return_5  │\n",
-      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ ---      ┆ p_rank    ┆ ratio     ┆ ---       │\n",
-      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ f64      ┆ ---       ┆ ---       ┆ f64       │\n",
-      "│           ┆ str       ┆           ┆           ┆   ┆          ┆ f64       ┆ f64       ┆           │\n",
-      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪══════════╪═══════════╪═══════════╪═══════════╡\n",
-      "│ 000001.SZ ┆ 20200102  ┆ 7.139221  ┆ 4.749919  ┆ … ┆ null     ┆ 1.59168   ┆ null      ┆ -0.004746 │\n",
-      "│ 000002.SZ ┆ 20200102  ┆ 7.139221  ┆ 2.92576   ┆ … ┆ null     ┆ 1.59168   ┆ null      ┆ -0.011057 │\n",
-      "│ 000004.SZ ┆ 20200102  ┆ 0.095339  ┆ -0.574944 ┆ … ┆ null     ┆ -1.639529 ┆ null      ┆ -0.000441 │\n",
-      "│ 000005.SZ ┆ 20200102  ┆ -0.301277 ┆ -0.271162 ┆ … ┆ null     ┆ -0.835937 ┆ null      ┆ 0.022337  │\n",
-      "│ 000006.SZ ┆ 20200102  ┆ 0.742213  ┆ -0.19857  ┆ … ┆ null     ┆ 0.344511  ┆ null      ┆ 0.012964  │\n",
-      "└───────────┴───────────┴───────────┴───────────┴───┴──────────┴───────────┴───────────┴───────────┘\n",
+      "shape: (5, 42)\n",
+      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
+      "│ ts_code   ┆ trade_dat ┆ vol       ┆ close     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5 │\n",
+      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---      │\n",
+      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64      │\n",
+      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ ---       ┆ p         ┆          │\n",
+      "│           ┆           ┆           ┆           ┆   ┆ f64       ┆ f64       ┆ ---       ┆          │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆ f64       ┆          │\n",
+      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
+      "│ 000001.SZ ┆ 20200102  ┆ 4.749919  ┆ 7.139221  ┆ … ┆ 1.228197  ┆ 2.569741  ┆ 1.426297  ┆ -0.00474 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 6        │\n",
+      "│ 000002.SZ ┆ 20200102  ┆ 2.92576   ┆ 7.139221  ┆ … ┆ 1.353457  ┆ -0.145372 ┆ 1.83338   ┆ -0.01105 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 7        │\n",
+      "│ 000004.SZ ┆ 20200102  ┆ -0.574944 ┆ 0.095339  ┆ … ┆ -0.560579 ┆ -0.252276 ┆ -0.513404 ┆ -0.00044 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 1        │\n",
+      "│ 000005.SZ ┆ 20200102  ┆ -0.271162 ┆ -0.301277 ┆ … ┆ -0.080905 ┆ 0.217351  ┆ -0.044183 ┆ 0.022337 │\n",
+      "│ 000006.SZ ┆ 20200102  ┆ -0.19857  ┆ 0.742213  ┆ … ┆ 1.028664  ┆ 0.501768  ┆ 1.186504  ┆ 0.012964 │\n",
+      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n",
       "\n",
       "  训练集特征统计:\n",
-      "    特征数: 15\n",
+      "    特征数: 25\n",
       "    样本数: 2991506\n",
       "    缺失值统计:\n",
       "      ma5: 11541 (0.39%)\n",
@@ -691,14 +732,14 @@
      ]
     }
    ],
-   "execution_count": 26
+   "execution_count": 8
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:20.427156Z",
-     "start_time": "2026-03-07T17:06:06.824957Z"
+     "end_time": "2026-03-08T03:33:14.426213Z",
+     "start_time": "2026-03-08T03:32:57.306610Z"
     }
    },
    "source": [
@@ -734,7 +775,7 @@
       "------------------------------------------------------------\n",
       "  模型类型: LightGBM\n",
       "  训练样本数: 2991506\n",
-      "  特征数: 15\n",
+      "  特征数: 25\n",
       "  目标变量: return_5\n",
       "\n",
       "  目标变量统计:\n",
@@ -749,14 +790,14 @@
      ]
     }
    ],
-   "execution_count": 27
+   "execution_count": 9
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:20.555487Z",
-     "start_time": "2026-03-07T17:06:20.431885Z"
+     "end_time": "2026-03-08T03:33:14.547157Z",
+     "start_time": "2026-03-08T03:33:14.431622Z"
     }
    },
    "source": [
@@ -796,14 +837,14 @@
      ]
     }
    ],
-   "execution_count": 28
+   "execution_count": 10
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:21.654690Z",
-     "start_time": "2026-03-07T17:06:20.570580Z"
+     "end_time": "2026-03-08T03:33:15.644580Z",
+     "start_time": "2026-03-08T03:33:14.556580Z"
     }
    },
    "source": [
@@ -838,14 +879,14 @@
       "  预测完成！\n",
       "\n",
       "  预测结果统计:\n",
-      "    均值: 0.000689\n",
-      "    标准差: 0.006131\n",
-      "    最小值: -0.114917\n",
-      "    最大值: 0.122962\n"
+      "    均值: -0.002611\n",
+      "    标准差: 0.006952\n",
+      "    最小值: -0.104909\n",
+      "    最大值: 0.093437\n"
      ]
     }
    ],
-   "execution_count": 29
+   "execution_count": 11
   },
   {
    "cell_type": "markdown",
@@ -858,8 +899,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:27.993171Z",
-     "start_time": "2026-03-07T17:06:21.659478Z"
+     "end_time": "2026-03-08T03:33:20.917840Z",
+     "start_time": "2026-03-08T03:33:15.648621Z"
     }
    },
    "source": [
@@ -938,30 +979,30 @@
       "重新训练模型以收集训练指标...\n",
       "Training until validation scores don't improve for 100 rounds\n",
       "Early stopping, best iteration is:\n",
-      "[264]\ttrain's l1: 0.0425269\tval's l1: 0.0537147\n",
+      "[175]\ttrain's l1: 0.0422897\tval's l1: 0.0535436\n",
       "训练完成，指标已收集\n",
       "\n",
       "评估指标: l1\n",
       "\n",
       "[早停信息]\n",
       "  配置的最大轮数: 1000\n",
-      "  实际训练轮数: 364\n",
+      "  实际训练轮数: 275\n",
       "  早停状态: 已触发（连续100轮验证指标未改善）\n",
       "\n",
       "最终指标:\n",
-      "  训练 l1: 0.042479\n",
-      "  验证 l1: 0.053722\n"
+      "  训练 l1: 0.042114\n",
+      "  验证 l1: 0.053595\n"
      ]
     }
    ],
-   "execution_count": 30
+   "execution_count": 12
   },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:28.093700Z",
-     "start_time": "2026-03-07T17:06:27.999580Z"
+     "end_time": "2026-03-08T03:33:21.167778Z",
+     "start_time": "2026-03-08T03:33:20.923061Z"
     }
    },
    "source": [
@@ -1009,7 +1050,7 @@
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ],
-      "image/png": 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      },
      "metadata": {},
      "output_type": "display_data",
@@ -1023,15 +1064,15 @@
      "text": [
       "\n",
       "[指标分析]\n",
-      "  最佳验证 l1: 0.053715\n",
-      "  最佳迭代轮数: 264\n",
-      "  早停建议: 如果验证指标连续10轮不下降，建议在第 264 轮停止训练\n",
+      "  最佳验证 l1: 0.053544\n",
+      "  最佳迭代轮数: 175\n",
+      "  早停建议: 如果验证指标连续10轮不下降，建议在第 175 轮停止训练\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": 31
+   "execution_count": 13
   },
   {
    "cell_type": "markdown",
@@ -1044,8 +1085,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:28.129848Z",
-     "start_time": "2026-03-07T17:06:28.109731Z"
+     "end_time": "2026-03-08T03:33:21.213471Z",
+     "start_time": "2026-03-08T03:33:21.183426Z"
     }
    },
    "source": [
@@ -1084,45 +1125,51 @@
       "训练结果\n",
       "================================================================================\n",
       "\n",
-      "结果数据形状: (771207, 25)\n",
-      "结果列: ['ts_code', 'trade_date', 'close', 'vol', 'high', 'low', 'total_mv', 'f_ann_date', 'n_income', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'return_5', 'prediction']\n",
+      "结果数据形状: (771207, 43)\n",
+      "结果列: ['ts_code', 'trade_date', 'vol', 'close', 'low', 'high', 'total_mv', 'f_ann_date', 'n_income', 'ebitda', 'total_profit', 'ebit', 'operate_profit', 'money_cap', 'total_liab', 'n_cashflow_act', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'n_income_rank', 'operate_profit_rank', 'total_profit_rank', 'ebit_rank', 'ebitda_rank', 'total_liab_rank', 'money_cap_rank', 'n_cashflow_act_rank', 'profit_to_market_cap', 'cashflow_to_market_cap', 'operate_profit_to_market_cap', 'return_5', 'prediction']\n",
       "\n",
       "结果前10行预览:\n",
-      "shape: (10, 25)\n",
+      "shape: (10, 43)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-      "│ ts_code   ┆ trade_dat ┆ close     ┆ vol       ┆ … ┆ market_ca ┆ high_low_ ┆ return_5  ┆ predicti │\n",
-      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ p_rank    ┆ ratio     ┆ ---       ┆ on       │\n",
-      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ ---       ┆ ---       ┆ f64       ┆ ---      │\n",
-      "│           ┆ str       ┆           ┆           ┆   ┆ f64       ┆ f64       ┆           ┆ f64      │\n",
+      "│ ts_code   ┆ trade_dat ┆ vol       ┆ close     ┆ … ┆ cashflow_ ┆ operate_p ┆ return_5  ┆ predicti │\n",
+      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ to_market ┆ rofit_to_ ┆ ---       ┆ on       │\n",
+      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ _cap      ┆ market_ca ┆ f64       ┆ ---      │\n",
+      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ p         ┆           ┆ f64      │\n",
+      "│           ┆           ┆           ┆           ┆   ┆ f64       ┆ ---       ┆           ┆          │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆ f64       ┆           ┆          │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-      "│ 000001.SZ ┆ 20250102  ┆ 7.139221  ┆ 5.587779  ┆ … ┆ 1.588367  ┆ -1.34697  ┆ -0.002622 ┆ -0.00449 │\n",
-      "│ 000002.SZ ┆ 20250102  ┆ 7.139221  ┆ 3.526191  ┆ … ┆ 1.513717  ┆ -1.494739 ┆ -0.022509 ┆ 0.00046  │\n",
-      "│ 000004.SZ ┆ 20250102  ┆ -0.117733 ┆ -0.216144 ┆ … ┆ -1.494831 ┆ -1.109999 ┆ -0.064897 ┆ 0.01386  │\n",
-      "│ 000006.SZ ┆ 20250102  ┆ 1.365091  ┆ 0.443786  ┆ … ┆ 0.663601  ┆ -1.468456 ┆ -0.048278 ┆ 0.007108 │\n",
-      "│ 000007.SZ ┆ 20250102  ┆ -0.114388 ┆ -0.397614 ┆ … ┆ -1.203308 ┆ -0.209058 ┆ 0.015649  ┆ -0.00067 │\n",
+      "│ 000001.SZ ┆ 20250102  ┆ 5.587779  ┆ 7.139221  ┆ … ┆ 4.533499  ┆ 3.606645  ┆ -0.002622 ┆ -0.00794 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 7        │\n",
+      "│ 000002.SZ ┆ 20250102  ┆ 3.526191  ┆ 7.139221  ┆ … ┆ -1.386218 ┆ -3.946245 ┆ -0.022509 ┆ -0.00197 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 2        │\n",
+      "│ 000004.SZ ┆ 20250102  ┆ -0.216144 ┆ -0.117733 ┆ … ┆ -0.331877 ┆ -0.860887 ┆ -0.064897 ┆ 0.006379 │\n",
+      "│ 000006.SZ ┆ 20250102  ┆ 0.443786  ┆ 1.365091  ┆ … ┆ -0.116207 ┆ -1.747611 ┆ -0.048278 ┆ 0.002845 │\n",
+      "│ 000007.SZ ┆ 20250102  ┆ -0.397614 ┆ -0.114388 ┆ … ┆ -0.079153 ┆ -0.417972 ┆ 0.015649  ┆ -0.00445 │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
-      "│ 000008.SZ ┆ 20250102  ┆ -0.079069 ┆ 3.219998  ┆ … ┆ 0.429996  ┆ -1.485343 ┆ -0.066939 ┆ -0.00880 │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
-      "│ 000009.SZ ┆ 20250102  ┆ 0.025538  ┆ 0.204049  ┆ … ┆ 1.21254   ┆ -1.357979 ┆ -0.036045 ┆ 0.001033 │\n",
-      "│ 000010.SZ ┆ 20250102  ┆ -0.300634 ┆ 0.584268  ┆ … ┆ -0.8455   ┆ -1.382219 ┆ 0.092123  ┆ -0.00428 │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 5        │\n",
-      "│ 000011.SZ ┆ 20250102  ┆ -0.243891 ┆ -0.488782 ┆ … ┆ -0.075826 ┆ -1.409893 ┆ -0.022094 ┆ 0.002294 │\n",
-      "│ 000012.SZ ┆ 20250102  ┆ 0.501156  ┆ -0.060654 ┆ … ┆ 1.022696  ┆ -1.411396 ┆ -0.029188 ┆ 0.000017 │\n",
+      "│ 000008.SZ ┆ 20250102  ┆ 3.219998  ┆ -0.079069 ┆ … ┆ -0.385751 ┆ -1.051162 ┆ -0.066939 ┆ -0.00372 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 9        │\n",
+      "│ 000009.SZ ┆ 20250102  ┆ 0.204049  ┆ 0.025538  ┆ … ┆ 0.245489  ┆ 0.579808  ┆ -0.036045 ┆ 0.003052 │\n",
+      "│ 000010.SZ ┆ 20250102  ┆ 0.584268  ┆ -0.300634 ┆ … ┆ -0.781057 ┆ -1.022311 ┆ 0.092123  ┆ -0.01003 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 7        │\n",
+      "│ 000011.SZ ┆ 20250102  ┆ -0.488782 ┆ -0.243891 ┆ … ┆ -1.733032 ┆ -0.464004 ┆ -0.022094 ┆ 0.000949 │\n",
+      "│ 000012.SZ ┆ 20250102  ┆ -0.060654 ┆ 0.501156  ┆ … ┆ 0.722727  ┆ 0.567525  ┆ -0.029188 ┆ 0.001858 │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n",
       "\n",
       "结果后5行预览:\n",
-      "shape: (5, 25)\n",
+      "shape: (5, 43)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬──────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_dat ┆ close     ┆ vol       ┆ … ┆ market_ca ┆ high_low_ ┆ return_5 ┆ predictio │\n",
-      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ p_rank    ┆ ratio     ┆ ---      ┆ n         │\n",
-      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ ---       ┆ ---       ┆ f64      ┆ ---       │\n",
-      "│           ┆ str       ┆           ┆           ┆   ┆ f64       ┆ f64       ┆          ┆ f64       │\n",
+      "│ ts_code   ┆ trade_dat ┆ vol       ┆ close     ┆ … ┆ cashflow_ ┆ operate_p ┆ return_5 ┆ predictio │\n",
+      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ to_market ┆ rofit_to_ ┆ ---      ┆ n         │\n",
+      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ _cap      ┆ market_ca ┆ f64      ┆ ---       │\n",
+      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ p         ┆          ┆ f64       │\n",
+      "│           ┆           ┆           ┆           ┆   ┆ f64       ┆ ---       ┆          ┆           │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆ f64       ┆          ┆           │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪══════════╪═══════════╡\n",
-      "│ 605588.SH ┆ 20251231  ┆ -0.161094 ┆ -0.601605 ┆ … ┆ -1.013122 ┆ 0.084104  ┆ null     ┆ 0.003068  │\n",
-      "│ 605589.SH ┆ 20251231  ┆ -0.293364 ┆ -0.152478 ┆ … ┆ 1.098326  ┆ 0.587703  ┆ null     ┆ 0.002339  │\n",
-      "│ 605598.SH ┆ 20251231  ┆ 0.149122  ┆ -0.248806 ┆ … ┆ 0.88313   ┆ 1.46838   ┆ null     ┆ -0.002429 │\n",
-      "│ 605599.SH ┆ 20251231  ┆ -0.364967 ┆ -0.482522 ┆ … ┆ 0.641985  ┆ 1.561653  ┆ null     ┆ 0.004799  │\n",
-      "│ 689009.SH ┆ 20251231  ┆ -0.119084 ┆ -0.481066 ┆ … ┆ 1.319218  ┆ -1.339489 ┆ null     ┆ -0.000833 │\n",
+      "│ 605588.SH ┆ 20251231  ┆ -0.601605 ┆ -0.161094 ┆ … ┆ 0.196523  ┆ -0.667804 ┆ null     ┆ 0.000246  │\n",
+      "│ 605589.SH ┆ 20251231  ┆ -0.152478 ┆ -0.293364 ┆ … ┆ -0.343887 ┆ 0.27582   ┆ null     ┆ -0.001414 │\n",
+      "│ 605598.SH ┆ 20251231  ┆ -0.248806 ┆ 0.149122  ┆ … ┆ -0.191229 ┆ -0.349198 ┆ null     ┆ -0.011185 │\n",
+      "│ 605599.SH ┆ 20251231  ┆ -0.482522 ┆ -0.364967 ┆ … ┆ 1.283712  ┆ 0.931037  ┆ null     ┆ 0.00215   │\n",
+      "│ 689009.SH ┆ 20251231  ┆ -0.481066 ┆ -0.119084 ┆ … ┆ 1.09954   ┆ 0.730255  ┆ null     ┆ -0.001826 │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴──────────┴───────────┘\n",
       "\n",
       "每日预测样本数统计:\n",
@@ -1137,21 +1184,21 @@
       "│ ---       ┆ ---        ┆ ---       ┆ ---        │\n",
       "│ str       ┆ str        ┆ f64       ┆ f64        │\n",
       "╞═══════════╪════════════╪═══════════╪════════════╡\n",
-      "│ 000001.SZ ┆ 20250102   ┆ -0.002622 ┆ -0.00449   │\n",
-      "│ 000002.SZ ┆ 20250102   ┆ -0.022509 ┆ 0.00046    │\n",
-      "│ 000004.SZ ┆ 20250102   ┆ -0.064897 ┆ 0.01386    │\n",
-      "│ 000006.SZ ┆ 20250102   ┆ -0.048278 ┆ 0.007108   │\n",
-      "│ 000007.SZ ┆ 20250102   ┆ 0.015649  ┆ -0.000674  │\n",
-      "│ 000008.SZ ┆ 20250102   ┆ -0.066939 ┆ -0.008804  │\n",
-      "│ 000009.SZ ┆ 20250102   ┆ -0.036045 ┆ 0.001033   │\n",
-      "│ 000010.SZ ┆ 20250102   ┆ 0.092123  ┆ -0.004285  │\n",
-      "│ 000011.SZ ┆ 20250102   ┆ -0.022094 ┆ 0.002294   │\n",
-      "│ 000012.SZ ┆ 20250102   ┆ -0.029188 ┆ 0.000017   │\n",
+      "│ 000001.SZ ┆ 20250102   ┆ -0.002622 ┆ -0.007947  │\n",
+      "│ 000002.SZ ┆ 20250102   ┆ -0.022509 ┆ -0.001972  │\n",
+      "│ 000004.SZ ┆ 20250102   ┆ -0.064897 ┆ 0.006379   │\n",
+      "│ 000006.SZ ┆ 20250102   ┆ -0.048278 ┆ 0.002845   │\n",
+      "│ 000007.SZ ┆ 20250102   ┆ 0.015649  ┆ -0.004454  │\n",
+      "│ 000008.SZ ┆ 20250102   ┆ -0.066939 ┆ -0.003729  │\n",
+      "│ 000009.SZ ┆ 20250102   ┆ -0.036045 ┆ 0.003052   │\n",
+      "│ 000010.SZ ┆ 20250102   ┆ 0.092123  ┆ -0.010037  │\n",
+      "│ 000011.SZ ┆ 20250102   ┆ -0.022094 ┆ 0.000949   │\n",
+      "│ 000012.SZ ┆ 20250102   ┆ -0.029188 ┆ 0.001858   │\n",
       "└───────────┴────────────┴───────────┴────────────┘\n"
      ]
     }
    ],
-   "execution_count": 32
+   "execution_count": 14
   },
   {
    "cell_type": "markdown",
@@ -1163,8 +1210,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:28.442742Z",
-     "start_time": "2026-03-07T17:06:28.133253Z"
+     "end_time": "2026-03-08T03:35:07.142015Z",
+     "start_time": "2026-03-08T03:35:06.791043Z"
     }
    },
    "cell_type": "code",
@@ -1181,24 +1228,24 @@
     "end_dt = datetime.strptime(TEST_END, \"%Y%m%d\")\n",
     "date_str = f\"{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}\"\n",
     "\n",
-    "# 保存每日 Top5\n",
-    "print(\"\\n[1/1] 保存每日 Top5 股票...\")\n",
-    "top5_output_path = os.path.join(OUTPUT_DIR, f\"top5_{date_str}.csv\")\n",
+    "# 保存每日 Top N\n",
+    "print(f\"\\n[1/1] 保存每日 Top {TOP_N} 股票...\")\n",
+    "topn_output_path = os.path.join(OUTPUT_DIR, f\"regression_output.csv\")\n",
     "\n",
-    "# 按日期分组，取每日 top5\n",
-    "top5_by_date = []\n",
+    "# 按日期分组，取每日 top N\n",
+    "topn_by_date = []\n",
     "unique_dates = results[\"trade_date\"].unique().sort()\n",
     "for date in unique_dates:\n",
     "    day_data = results.filter(results[\"trade_date\"] == date)\n",
-    "    # 按 prediction 降序排序，取前5\n",
-    "    top5 = day_data.sort(\"prediction\", descending=True).head(5)\n",
-    "    top5_by_date.append(top5)\n",
+    "    # 按 prediction 降序排序，取前 N\n",
+    "    topn = day_data.sort(\"prediction\", descending=True).head(TOP_N)\n",
+    "    topn_by_date.append(topn)\n",
     "\n",
-    "# 合并所有日期的 top5\n",
-    "top5_results = pl.concat(top5_by_date)\n",
+    "# 合并所有日期的 top N\n",
+    "topn_results = pl.concat(topn_by_date)\n",
     "\n",
     "# 格式化日期并调整列顺序：日期、分数、股票\n",
-    "top5_to_save = top5_results.select(\n",
+    "topn_to_save = topn_results.select(\n",
     "    [\n",
     "        pl.col(\"trade_date\").str.slice(0, 4)\n",
     "        + \"-\"\n",
@@ -1209,11 +1256,11 @@
     "        pl.col(\"ts_code\"),\n",
     "    ]\n",
     ")\n",
-    "# top5_to_save.write_csv(top5_output_path, include_header=True)\n",
-    "print(f\"  保存路径: {top5_output_path}\")\n",
-    "print(f\"  保存行数: {len(top5_to_save)}（{len(unique_dates)}个交易日 × 每日top5）\")\n",
+    "topn_to_save.write_csv(topn_output_path, include_header=True)\n",
+    "print(f\"  保存路径: {topn_output_path}\")\n",
+    "print(f\"  保存行数: {len(topn_to_save)}（{len(unique_dates)}个交易日 × 每日top{TOP_N}）\")\n",
     "print(f\"\\n  预览（前15行）:\")\n",
-    "print(top5_to_save.head(15))"
+    "print(topn_to_save.head(15))"
    ],
    "outputs": [
     {
@@ -1225,9 +1272,9 @@
       "保存预测结果\n",
       "================================================================================\n",
       "\n",
-      "[1/1] 保存每日 Top5 股票...\n",
-      "  保存路径: output\\top5_20250101_20251231.csv\n",
-      "  保存行数: 1215（243个交易日 × 每日top5）\n",
+      "[1/1] 保存每日 Top 2 股票...\n",
+      "  保存路径: output\\regression_output.csv\n",
+      "  保存行数: 486（243个交易日 × 每日top2）\n",
       "\n",
       "  预览（前15行）:\n",
       "shape: (15, 3)\n",
@@ -1236,22 +1283,22 @@
       "│ ---        ┆ ---      ┆ ---       │\n",
       "│ str        ┆ f64      ┆ str       │\n",
       "╞════════════╪══════════╪═══════════╡\n",
-      "│ 2025-01-02 ┆ 0.112045 ┆ 603007.SH │\n",
-      "│ 2025-01-02 ┆ 0.085536 ┆ 603559.SH │\n",
-      "│ 2025-01-02 ┆ 0.02923  ┆ 603959.SH │\n",
-      "│ 2025-01-02 ┆ 0.027056 ┆ 002356.SZ │\n",
-      "│ 2025-01-02 ┆ 0.025893 ┆ 002693.SZ │\n",
+      "│ 2025-01-02 ┆ 0.078766 ┆ 603007.SH │\n",
+      "│ 2025-01-02 ┆ 0.065013 ┆ 603559.SH │\n",
+      "│ 2025-01-03 ┆ 0.087979 ┆ 603007.SH │\n",
+      "│ 2025-01-03 ┆ 0.067351 ┆ 603559.SH │\n",
+      "│ 2025-01-06 ┆ 0.087962 ┆ 603007.SH │\n",
       "│ …          ┆ …        ┆ …         │\n",
-      "│ 2025-01-06 ┆ 0.122962 ┆ 603007.SH │\n",
-      "│ 2025-01-06 ┆ 0.05708  ┆ 603959.SH │\n",
-      "│ 2025-01-06 ┆ 0.031928 ┆ 603106.SH │\n",
-      "│ 2025-01-06 ┆ 0.029876 ┆ 603536.SH │\n",
-      "│ 2025-01-06 ┆ 0.02902  ┆ 000856.SZ │\n",
+      "│ 2025-01-09 ┆ 0.036131 ┆ 603848.SH │\n",
+      "│ 2025-01-09 ┆ 0.027876 ┆ 603977.SH │\n",
+      "│ 2025-01-10 ┆ 0.027436 ┆ 002952.SZ │\n",
+      "│ 2025-01-10 ┆ 0.024641 ┆ 603848.SH │\n",
+      "│ 2025-01-13 ┆ 0.024852 ┆ 603848.SH │\n",
       "└────────────┴──────────┴───────────┘\n"
      ]
     }
    ],
-   "execution_count": 33
+   "execution_count": 23
   },
   {
    "cell_type": "markdown",
@@ -1264,8 +1311,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:28.449645Z",
-     "start_time": "2026-03-07T17:06:28.446303Z"
+     "end_time": "2026-03-08T03:35:07.151989Z",
+     "start_time": "2026-03-08T03:35:07.148773Z"
     }
    },
    "source": [
@@ -1285,21 +1332,31 @@
      "text": [
       "\n",
       "特征重要性:\n",
-      "return_10          2508.074001\n",
-      "return_20          1599.133273\n",
-      "vol_ratio          1443.186734\n",
-      "ma_ratio           1258.536915\n",
-      "volatility_5       1168.921020\n",
-      "high_low_ratio     1021.852749\n",
-      "return_diff         726.928342\n",
-      "vol_ma20            595.716762\n",
-      "vol_ma5             551.588497\n",
-      "market_cap_rank     542.557658\n",
-      "ma20                432.252425\n",
-      "n_income            346.770895\n",
-      "ma5                 342.071273\n",
-      "ma10                328.521408\n",
-      "volatility_20       136.785447\n",
+      "ebitda_rank                     14616.823972\n",
+      "return_10                        2170.157647\n",
+      "return_20                        1597.996920\n",
+      "ma_ratio                         1346.640091\n",
+      "vol_ratio                        1307.361400\n",
+      "high_low_ratio                    918.782296\n",
+      "vol_ma20                          828.398926\n",
+      "ma20                              635.353193\n",
+      "volatility_5                      631.979581\n",
+      "return_diff                       617.399685\n",
+      "vol_ma5                           491.980673\n",
+      "market_cap_rank                   415.972949\n",
+      "volatility_20                     276.516502\n",
+      "profit_to_market_cap              256.683738\n",
+      "ma5                               236.319446\n",
+      "ma10                              173.155782\n",
+      "ebit_rank                         142.423690\n",
+      "operate_profit_to_market_cap      127.127625\n",
+      "total_liab_rank                    97.979562\n",
+      "n_income_rank                      77.705741\n",
+      "operate_profit_rank                75.762452\n",
+      "cashflow_to_market_cap             68.135267\n",
+      "money_cap_rank                     58.562898\n",
+      "n_cashflow_act_rank                47.001317\n",
+      "total_profit_rank                  36.941945\n",
       "dtype: float64\n",
       "\n",
       "================================================================================\n",
@@ -1308,7 +1365,7 @@
      ]
     }
    ],
-   "execution_count": 34
+   "execution_count": 24
   },
   {
    "cell_type": "markdown",
@@ -1325,8 +1382,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:28.464210Z",
-     "start_time": "2026-03-07T17:06:28.457241Z"
+     "end_time": "2026-03-08T03:35:07.163848Z",
+     "start_time": "2026-03-08T03:35:07.158216Z"
     }
    },
    "source": [
@@ -1346,11 +1403,11 @@
      "output_type": "stream",
      "text": [
       "模型类型: <class 'lightgbm.basic.Booster'>\n",
-      "特征数量: 15\n"
+      "特征数量: 25\n"
      ]
     }
    ],
-   "execution_count": 35
+   "execution_count": 25
   },
   {
    "cell_type": "markdown",
@@ -1367,8 +1424,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T17:06:28.553758Z",
-     "start_time": "2026-03-07T17:06:28.467408Z"
+     "end_time": "2026-03-08T03:35:07.289782Z",
+     "start_time": "2026-03-08T03:35:07.170460Z"
     }
    },
    "cell_type": "code",
@@ -1418,7 +1475,7 @@
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data",
@@ -1432,28 +1489,38 @@
      "text": [
       "\n",
       "[特征重要性排名 - Gain]\n",
-      "return_10          2508.074001\n",
-      "return_20          1599.133273\n",
-      "vol_ratio          1443.186734\n",
-      "ma_ratio           1258.536915\n",
-      "volatility_5       1168.921020\n",
-      "high_low_ratio     1021.852749\n",
-      "return_diff         726.928342\n",
-      "vol_ma20            595.716762\n",
-      "vol_ma5             551.588497\n",
-      "market_cap_rank     542.557658\n",
-      "ma20                432.252425\n",
-      "n_income            346.770895\n",
-      "ma5                 342.071273\n",
-      "ma10                328.521408\n",
-      "volatility_20       136.785447\n",
+      "ebitda_rank                     14616.823972\n",
+      "return_10                        2170.157647\n",
+      "return_20                        1597.996920\n",
+      "ma_ratio                         1346.640091\n",
+      "vol_ratio                        1307.361400\n",
+      "high_low_ratio                    918.782296\n",
+      "vol_ma20                          828.398926\n",
+      "ma20                              635.353193\n",
+      "volatility_5                      631.979581\n",
+      "return_diff                       617.399685\n",
+      "vol_ma5                           491.980673\n",
+      "market_cap_rank                   415.972949\n",
+      "volatility_20                     276.516502\n",
+      "profit_to_market_cap              256.683738\n",
+      "ma5                               236.319446\n",
+      "ma10                              173.155782\n",
+      "ebit_rank                         142.423690\n",
+      "operate_profit_to_market_cap      127.127625\n",
+      "total_liab_rank                    97.979562\n",
+      "n_income_rank                      77.705741\n",
+      "operate_profit_rank                75.762452\n",
+      "cashflow_to_market_cap             68.135267\n",
+      "money_cap_rank                     58.562898\n",
+      "n_cashflow_act_rank                47.001317\n",
+      "total_profit_rank                  36.941945\n",
       "dtype: float64\n",
       "\n",
       "所有特征都有一定重要性\n"
      ]
     }
    ],
-   "execution_count": 36
+   "execution_count": 26
   }
  ],
  "metadata": {
diff --git a/src/experiment/regression.py b/src/experiment/regression.py
deleted file mode 100644
index 92620cc..0000000
--- a/src/experiment/regression.py
+++ /dev/null
@@ -1,484 +0,0 @@
-"""LightGBM 回归训练示例 - 使用因子字符串表达式
-
-使用字符串表达式定义因子，训练 LightGBM 回归模型预测未来5日收益率。
-Label: return_5 = (ts_delay(close, -5) / close) - 1  # 未来5日收益率
-"""
-
-import os
-from datetime import datetime
-from typing import List, Tuple
-
-import polars as pl
-
-from src.factors import FactorEngine
-from src.training import (
-    DateSplitter,
-    LightGBMModel,
-    STFilter,
-    StandardScaler,
-    StockFilterConfig,
-    StockPoolManager,
-    Trainer,
-    Winsorizer,
-    NullFiller,
-)
-from src.training.config import TrainingConfig
-
-# =============================================================================
-# 因子定义（集中在此，方便修改）
-# =============================================================================
-
-# 特征因子定义字典：新增因子只需在此处添加一行
-FACTOR_DEFINITIONS = {
-    # 1. 价格动量因子
-    "ma5": "ts_mean(close, 5)",
-    "ma10": "ts_mean(close, 10)",
-    "ma20": "ts_mean(close, 20)",
-    "ma_ratio": "ts_mean(close, 5) / ts_mean(close, 20) - 1",
-    # 2. 波动率因子
-    "volatility_5": "ts_std(close, 5)",
-    "volatility_20": "ts_std(close, 20)",
-    "vol_ratio": "ts_std(close, 5) / (ts_std(close, 20) + 1e-8)",
-    # 3. 收益率动量因子
-    "return_10": "(close / ts_delay(close, 10)) - 1",
-    "return_20": "(close / ts_delay(close, 20)) - 1",
-    # 4. 收益率变化因子
-    "return_diff": "(close / ts_delay(close, 5)) - 1 - ((close / ts_delay(close, 10)) - 1)",
-    # 5. 成交量因子
-    "vol_ma5": "ts_mean(vol, 5)",
-    "vol_ma20": "ts_mean(vol, 20)",
-    "vol_ratio": "ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)",
-    # 6. 市值因子（截面排名）
-    "market_cap_rank": "cs_rank(total_mv)",
-    # 7. 价格位置因子
-    "high_low_ratio": "(close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)",
-    "n_income": "n_income",
-}
-
-# Label 因子定义（不参与训练，用于计算目标）
-LABEL_FACTOR = {
-    "return_5": "(ts_delay(close, -5) / close) - 1",  # 未来5日收益率
-}
-
-# =============================================================================
-# 训练参数配置（集中在此，方便修改）
-# =============================================================================
-
-# 日期范围配置
-TRAIN_START = "20200101"
-TRAIN_END = "20241231"
-TEST_START = "20250101"
-TEST_END = "20251231"
-
-# 模型参数配置
-MODEL_PARAMS = {
-    "objective": "regression",
-    "metric": "mae",  # 改为 MAE，对异常值更稳健
-    # 树结构控制（防过拟合核心）
-    "num_leaves": 20,  # 从31降为20，降低模型复杂度
-    "max_depth": 4,  # 显式限制深度，防止过度拟合噪声
-    "min_child_samples": 50,  # 叶子最小样本数，防止学习极端样本
-    "min_child_weight": 0.001,
-    # 学习参数
-    "learning_rate": 0.01,  # 降低学习率，配合更多树
-    "n_estimators": 1000,  # 增加树数量，配合早停
-    # 采样策略（关键防过拟合）
-    "subsample": 0.8,  # 每棵树随机采样80%数据（行采样）
-    "subsample_freq": 5,  # 每5轮迭代进行一次 subsample
-    "colsample_bytree": 0.8,  # 每棵树随机选择80%特征（列采样）
-    # 正则化
-    "reg_alpha": 0.1,  # L1正则，增加稀疏性
-    "reg_lambda": 1.0,  # L2正则，平滑权重
-    # 数值稳定性
-    "verbose": -1,
-    "random_state": 42,
-}
-
-# 数据处理器配置
-PROCESSOR_CONFIGS = [
-    {"name": "winsorizer", "params": {"lower": 0.01, "upper": 0.99}},
-    {"name": "cs_standard_scaler", "params": {}},
-]
-
-# 股票池筛选配置
-STOCK_FILTER_CONFIG = {
-    "exclude_cyb": True,  # 排除创业板
-    "exclude_kcb": True,  # 排除科创板
-    "exclude_bj": True,  # 排除北交所
-    "exclude_st": True,  # 排除ST股票
-}
-
-# 输出配置（相对于本文件所在目录）
-OUTPUT_DIR = "output"
-SAVE_PREDICTIONS = True
-PERSIST_MODEL = False
-
-
-def create_factors_with_strings(engine: FactorEngine) -> List[str]:
-    """使用字符串表达式定义因子
-
-    Args:
-        engine: FactorEngine 实例
-
-    Returns:
-        特征因子名称列表（不包含 label）
-    """
-    print("=" * 80)
-    print("使用字符串表达式定义因子")
-    print("=" * 80)
-
-    # 使用模块级别的因子定义
-    # 注册所有特征因子
-    print("\n注册特征因子:")
-    for name, expr in FACTOR_DEFINITIONS.items():
-        engine.add_factor(name, expr)
-        print(f"  - {name}: {expr}")
-
-    # 注册 label 因子
-    print("\n注册 Label 因子:")
-    for name, expr in LABEL_FACTOR.items():
-        engine.add_factor(name, expr)
-        print(f"  - {name}: {expr}")
-
-    # 从字典自动获取特征列
-    feature_cols = list(FACTOR_DEFINITIONS.keys())
-
-    print(f"\n特征因子数: {len(feature_cols)}")
-    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:
-    """准备数据：计算因子
-
-    Args:
-        engine: FactorEngine 实例
-        feature_cols: 特征列名列表
-        start_date: 开始日期
-        end_date: 结束日期
-
-    Returns:
-        因子数据 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 + ["return_5"]  # 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
-
-
-def train_regression_model():
-    """训练 LightGBM 回归模型"""
-    print("\n" + "=" * 80)
-    print("LightGBM 回归模型训练")
-    print("=" * 80)
-
-    # 1. 创建 FactorEngine
-    print("\n[1] 创建 FactorEngine")
-    engine = FactorEngine()
-
-    # 2. 使用字符串表达式定义因子
-    print("\n[2] 定义因子（字符串表达式）")
-    feature_cols = create_factors_with_strings(engine)
-    target_col = "return_5"
-
-    # 3. 准备数据（使用模块级别的日期配置）
-    print("\n[3] 准备数据")
-
-    data = prepare_data(
-        engine=engine,
-        feature_cols=feature_cols,
-        start_date=TRAIN_START,
-        end_date=TEST_END,
-    )
-
-    # 4. 打印配置信息（使用模块级别的配置常量）
-    print(f"\n[配置] 训练期: {TRAIN_START} - {TRAIN_END}")
-    print(f"[配置] 测试期: {TEST_START} - {TEST_END}")
-    print(f"[配置] 特征数: {len(feature_cols)}")
-    print(f"[配置] 目标变量: {target_col}")
-
-    # 5. 创建模型（使用模块级别的模型参数）
-    model = LightGBMModel(params=MODEL_PARAMS)
-
-    # 6. 创建数据处理器（从 PROCESSOR_CONFIGS 解析）
-    processors = [
-        NullFiller(strategy="mean"),
-        Winsorizer(**PROCESSOR_CONFIGS[0]["params"]),  # type: ignore[arg-type]
-        StandardScaler(exclude_cols=["ts_code", "trade_date", target_col]),  # type: ignore[call-arg]
-    ]
-
-    # 7. 创建数据划分器（使用模块级别的日期配置）
-    splitter = DateSplitter(
-        train_start=TRAIN_START,
-        train_end=TRAIN_END,
-        test_start=TEST_START,
-        test_end=TEST_END,
-    )
-
-    # 8. 创建股票池管理器（使用模块级别的筛选配置）
-    pool_manager = StockPoolManager(
-        filter_config=StockFilterConfig(**STOCK_FILTER_CONFIG),
-        selector_config=None,  # 暂时不启用市值选择
-        data_router=engine.router,  # 从 FactorEngine 获取数据路由器
-    )
-
-    # 8.5 创建 ST 股票过滤器（在股票池筛选之前执行）
-    st_filter = STFilter(
-        data_router=engine.router,
-    )
-
-    # 9. 创建训练器
-    trainer = Trainer(
-        model=model,
-        pool_manager=pool_manager,
-        processors=processors,
-        filters=[st_filter],  # 在股票池筛选之前过滤 ST 股票
-        splitter=splitter,
-        target_col=target_col,
-        feature_cols=feature_cols,
-        persist_model=PERSIST_MODEL,
-    )
-
-    # 10. 手动执行训练流程（增加详细打印）
-    print("\n" + "=" * 80)
-    print("开始训练")
-    print("=" * 80)
-
-    # 10.1 股票池筛选
-    print("\n[步骤 1/6] 股票池筛选")
-    print("-" * 60)
-    if pool_manager:
-        print("  执行每日独立筛选股票池...")
-        filtered_data = pool_manager.filter_and_select_daily(data)
-        print(f"  筛选前数据规模: {data.shape}")
-        print(f"  筛选后数据规模: {filtered_data.shape}")
-        print(f"  筛选前股票数: {data['ts_code'].n_unique()}")
-        print(f"  筛选后股票数: {filtered_data['ts_code'].n_unique()}")
-        print(f"  删除记录数: {len(data) - len(filtered_data)}")
-    else:
-        filtered_data = data
-        print("  未配置股票池管理器，跳过筛选")
-
-    # 10.2 划分训练/测试集
-    print("\n[步骤 2/6] 划分训练集和测试集")
-    print("-" * 60)
-    if splitter:
-        train_data, test_data = splitter.split(filtered_data)
-        print(f"  训练集数据规模: {train_data.shape}")
-        print(f"  测试集数据规模: {test_data.shape}")
-        print(f"  训练集股票数: {train_data['ts_code'].n_unique()}")
-        print(f"  测试集股票数: {test_data['ts_code'].n_unique()}")
-        print(
-            f"  训练集日期范围: {train_data['trade_date'].min()} - {train_data['trade_date'].max()}"
-        )
-        print(
-            f"  测试集日期范围: {test_data['trade_date'].min()} - {test_data['trade_date'].max()}"
-        )
-
-        print("\n  训练集前5行预览:")
-        print(train_data.head())
-        print("\n  测试集前5行预览:")
-        print(test_data.head())
-    else:
-        train_data = filtered_data
-        test_data = filtered_data
-        print("  未配置划分器，全部作为训练集")
-
-    # 10.3 训练集数据处理
-    print("\n[步骤 3/6] 训练集数据处理")
-    print("-" * 60)
-    fitted_processors = []
-    if processors:
-        for i, processor in enumerate(processors, 1):
-            print(
-                f"  [{i}/{len(processors)}] 应用处理器: {processor.__class__.__name__}"
-            )
-            train_data_before = len(train_data)
-            train_data = processor.fit_transform(train_data)
-            train_data_after = len(train_data)
-            fitted_processors.append(processor)
-            print(f"      处理前记录数: {train_data_before}")
-            print(f"      处理后记录数: {train_data_after}")
-            if train_data_before != train_data_after:
-                print(f"      删除记录数: {train_data_before - train_data_after}")
-
-    print("\n  训练集处理后前5行预览:")
-    print(train_data.head())
-    print(f"\n  训练集特征统计:")
-    print(f"    特征数: {len(feature_cols)}")
-    print(f"    样本数: {len(train_data)}")
-    print(f"    缺失值统计:")
-    for col in feature_cols[:5]:  # 只显示前5个特征的缺失值
-        null_count = train_data[col].null_count()
-        if null_count > 0:
-            print(
-                f"      {col}: {null_count} ({null_count / len(train_data) * 100:.2f}%)"
-            )
-
-    # 10.4 训练模型
-    print("\n[步骤 4/6] 训练模型")
-    print("-" * 60)
-    print(f"  模型类型: LightGBM")
-    print(f"  训练样本数: {len(train_data)}")
-    print(f"  特征数: {len(feature_cols)}")
-    print(f"  目标变量: {target_col}")
-
-    X_train = train_data.select(feature_cols)
-    y_train = train_data.select(target_col).to_series()
-
-    print(f"\n  目标变量统计:")
-    print(f"    均值: {y_train.mean():.6f}")
-    print(f"    标准差: {y_train.std():.6f}")
-    print(f"    最小值: {y_train.min():.6f}")
-    print(f"    最大值: {y_train.max():.6f}")
-    print(f"    缺失值: {y_train.null_count()}")
-
-    print("\n  开始训练...")
-    model.fit(X_train, y_train)
-    print("  训练完成！")
-
-    # 10.5 测试集数据处理
-    print("\n[步骤 5/6] 测试集数据处理")
-    print("-" * 60)
-    if processors and test_data is not train_data:
-        for i, processor in enumerate(fitted_processors, 1):
-            print(
-                f"  [{i}/{len(fitted_processors)}] 应用处理器: {processor.__class__.__name__}"
-            )
-            test_data_before = len(test_data)
-            test_data = processor.transform(test_data)
-            test_data_after = len(test_data)
-            print(f"      处理前记录数: {test_data_before}")
-            print(f"      处理后记录数: {test_data_after}")
-    else:
-        print("  跳过测试集处理")
-
-    # 10.6 预测
-    print("\n[步骤 6/6] 生成预测")
-    print("-" * 60)
-    X_test = test_data.select(feature_cols)
-    print(f"  测试样本数: {len(X_test)}")
-    print(f"  预测中...")
-    predictions = model.predict(X_test)
-    print(f"  预测完成！")
-
-    print(f"\n  预测结果统计:")
-    print(f"    均值: {predictions.mean():.6f}")
-    print(f"    标准差: {predictions.std():.6f}")
-    print(f"    最小值: {predictions.min():.6f}")
-    print(f"    最大值: {predictions.max():.6f}")
-
-    # 保存结果到 trainer
-    trainer.results = test_data.with_columns([pl.Series("prediction", predictions)])
-
-    # 11. 获取结果
-    print("\n" + "=" * 80)
-    print("训练结果")
-    print("=" * 80)
-
-    results = trainer.results
-
-    print(f"\n结果数据形状: {results.shape}")
-    print(f"结果列: {results.columns}")
-    print(f"\n结果前10行预览:")
-    print(results.head(10))
-    print(f"\n结果后5行预览:")
-    print(results.tail())
-
-    print(f"\n每日预测样本数统计:")
-    daily_counts = results.group_by("trade_date").agg(pl.count()).sort("trade_date")
-    print(f"  最小: {daily_counts['count'].min()}")
-    print(f"  最大: {daily_counts['count'].max()}")
-    print(f"  平均: {daily_counts['count'].mean():.2f}")
-
-    # 展示某一天的前10个预测结果
-    sample_date = results["trade_date"][0]
-    sample_data = results.filter(results["trade_date"] == sample_date).head(10)
-    print(f"\n示例日期 {sample_date} 的前10条预测:")
-    print(sample_data.select(["ts_code", "trade_date", target_col, "prediction"]))
-
-    # 12. 保存结果
-    print("\n" + "=" * 80)
-    print("保存预测结果")
-    print("=" * 80)
-
-    # 确保输出目录存在
-    os.makedirs(OUTPUT_DIR, exist_ok=True)
-
-    # 生成时间戳
-    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')}"
-
-    # 12.1 保存每日 Top5
-    print("\n[1/1] 保存每日 Top5 股票...")
-    top5_output_path = os.path.join(OUTPUT_DIR, f"top5_{date_str}.csv")
-
-    # 按日期分组，取每日 top5
-    top5_by_date = []
-    unique_dates = results["trade_date"].unique().sort()
-    for date in unique_dates:
-        day_data = results.filter(results["trade_date"] == date)
-        # 按 prediction 降序排序，取前5
-        top5 = day_data.sort("prediction", descending=True).head(5)
-        top5_by_date.append(top5)
-
-    # 合并所有日期的 top5
-    top5_results = pl.concat(top5_by_date)
-
-    # 格式化日期并调整列顺序：日期、分数、股票
-    top5_to_save = top5_results.select(
-        [
-            pl.col("trade_date").str.slice(0, 4)
-            + "-"
-            + pl.col("trade_date").str.slice(4, 2)
-            + "-"
-            + pl.col("trade_date").str.slice(6, 2).alias("date"),
-            pl.col("prediction").alias("score"),
-            pl.col("ts_code"),
-        ]
-    )
-    top5_to_save.write_csv(top5_output_path, include_header=True)
-    print(f"  保存路径: {top5_output_path}")
-    print(f"  保存行数: {len(top5_to_save)}（{len(unique_dates)}个交易日 × 每日top5）")
-    print(f"\n  预览（前15行）:")
-    print(top5_to_save.head(15))
-
-    # 13. 特征重要性
-    importance = model.feature_importance()
-    if importance is not None:
-        print("\n特征重要性:")
-        print(importance.sort_values(ascending=False))
-
-    print("\n" + "=" * 80)
-    print("训练完成！")
-    print("=" * 80)
-
-    return trainer, results
-
-
-if __name__ == "__main__":
-    trainer, results = train_regression_model()