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diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json
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diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index e22d595..2422442 100644
Binary files a/catboost_info/learn/events.out.tfevents and b/catboost_info/learn/events.out.tfevents differ
diff --git a/catboost_info/learn_error.tsv b/catboost_info/learn_error.tsv
index fca1eeb..74062b4 100644
--- a/catboost_info/learn_error.tsv
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diff --git a/catboost_info/test/events.out.tfevents b/catboost_info/test/events.out.tfevents
index 74ccb32..fa56198 100644
Binary files a/catboost_info/test/events.out.tfevents and b/catboost_info/test/events.out.tfevents differ
diff --git a/catboost_info/test_error.tsv b/catboost_info/test_error.tsv
index 4b691e7..ed69dee 100644
--- a/catboost_info/test_error.tsv
+++ b/catboost_info/test_error.tsv
@@ -1,751 +1,1176 @@
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diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index 0d49225..61fddf3 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,751 +1,1176 @@
 iter	Passed	Remaining
-0	18	13515
-1	32	11980
-2	45	11445
-3	58	10965
-4	72	10847
-5	86	10711
-6	101	10762
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-9	143	10638
-10	156	10541
-11	170	10511
-12	184	10482
-13	198	10417
-14	212	10422
-15	227	10443
-16	242	10474
-17	257	10467
-18	271	10433
-19	286	10444
-20	300	10416
-21	313	10363
-22	325	10298
-23	339	10281
-24	352	10222
-25	365	10178
-26	378	10137
-27	391	10085
-28	403	10042
-29	416	9992
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-31	444	9974
-32	458	9955
-33	470	9912
-34	483	9877
-35	496	9856
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-37	528	9910
-38	546	9963
-39	562	9988
-40	581	10059
-41	596	10056
-42	608	10012
-43	622	9983
-44	635	9953
-45	648	9922
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-47	674	9869
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-50	713	9784
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+994	141337	71734
+995	141493	71599
+996	141650	71464
+997	141807	71330
+998	141966	71196
+999	142124	71062
+1000	142282	70927
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+1003	142762	70528
+1004	142923	70395
+1005	143094	70267
+1006	143252	70132
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+1008	143573	69866
+1009	143739	69734
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+1015	144679	68921
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+1156	166654	49405
+1157	166813	49266
+1158	166971	49126
+1159	167133	48987
+1160	167288	48846
+1161	167451	48707
+1162	167606	48567
+1163	167762	48426
+1164	167923	48286
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+1166	168244	48007
+1167	168408	47869
+1168	168577	47732
+1169	168738	47592
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+1173	169371	47031
+1174	169529	46891
diff --git a/main/data/daily_data.ipynb b/main/data/daily_data.ipynb
index eed24fa..3b5e29c 100644
--- a/main/data/daily_data.ipynb
+++ b/main/data/daily_data.ipynb
@@ -20,7 +20,10 @@
     }
    ],
    "source": [
-    "print('hello world')"
+    "print('hello world')\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {
@@ -59,5388 +62,149 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "成功获取 000001.SZ 的数据\n",
-      "成功获取 000002.SZ 的数据\n",
-      "成功获取 000004.SZ 的数据\n",
-      "成功获取 000006.SZ 的数据\n",
-      "成功获取 000007.SZ 的数据\n",
-      "成功获取 000008.SZ 的数据\n",
-      "成功获取 000009.SZ 的数据\n",
-      "成功获取 000010.SZ 的数据\n",
-      "成功获取 000011.SZ 的数据\n",
-      "成功获取 000012.SZ 的数据\n",
-      "成功获取 000014.SZ 的数据\n",
-      "成功获取 000016.SZ 的数据\n",
-      "成功获取 000017.SZ 的数据\n",
-      "成功获取 000019.SZ 的数据\n",
-      "成功获取 000020.SZ 的数据\n",
-      "成功获取 000021.SZ 的数据\n",
-      "成功获取 000025.SZ 的数据\n",
-      "成功获取 000026.SZ 的数据\n",
-      "成功获取 000027.SZ 的数据\n",
-      "成功获取 000028.SZ 的数据\n",
-      "成功获取 000029.SZ 的数据\n",
-      "成功获取 000030.SZ 的数据\n",
-      "成功获取 000031.SZ 的数据\n",
-      "成功获取 000032.SZ 的数据\n",
-      "成功获取 000034.SZ 的数据\n",
-      "成功获取 000035.SZ 的数据\n",
-      "成功获取 000036.SZ 的数据\n",
-      "成功获取 000037.SZ 的数据\n",
-      "成功获取 000039.SZ 的数据\n",
-      "成功获取 000040.SZ 的数据\n",
-      "成功获取 000042.SZ 的数据\n",
-      "成功获取 000045.SZ 的数据\n",
-      "成功获取 000048.SZ 的数据\n",
-      "成功获取 000049.SZ 的数据\n",
-      "成功获取 000050.SZ 的数据\n",
-      "成功获取 000055.SZ 的数据\n",
-      "成功获取 000056.SZ 的数据\n",
-      "成功获取 000058.SZ 的数据\n",
-      "成功获取 000059.SZ 的数据\n",
-      "成功获取 000060.SZ 的数据\n",
-      "成功获取 000061.SZ 的数据\n",
-      "成功获取 000062.SZ 的数据\n",
-      "成功获取 000063.SZ 的数据\n",
-      "成功获取 000065.SZ 的数据\n",
-      "成功获取 000066.SZ 的数据\n",
-      "成功获取 000068.SZ 的数据\n",
-      "成功获取 000069.SZ 的数据\n",
-      "成功获取 000070.SZ 的数据\n",
-      "成功获取 000078.SZ 的数据\n",
-      "成功获取 000088.SZ 的数据\n",
-      "成功获取 000089.SZ 的数据\n",
-      "成功获取 000090.SZ 的数据\n",
-      "成功获取 000096.SZ 的数据\n",
-      "成功获取 000099.SZ 的数据\n",
-      "成功获取 000100.SZ 的数据\n",
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-      "成功获取 000153.SZ 的数据\n",
-      "成功获取 000155.SZ 的数据\n",
-      "成功获取 000156.SZ 的数据\n",
-      "成功获取 000157.SZ 的数据\n",
-      "成功获取 000158.SZ 的数据\n",
-      "成功获取 000159.SZ 的数据\n",
-      "成功获取 000166.SZ 的数据\n",
-      "成功获取 000301.SZ 的数据\n",
-      "成功获取 000333.SZ 的数据\n",
-      "成功获取 000338.SZ 的数据\n",
-      "成功获取 000400.SZ 的数据\n",
-      "成功获取 000401.SZ 的数据\n",
-      "成功获取 000402.SZ 的数据\n",
-      "成功获取 000403.SZ 的数据\n",
-      "成功获取 000404.SZ 的数据\n",
-      "成功获取 000407.SZ 的数据\n",
-      "成功获取 000408.SZ 的数据\n",
-      "成功获取 000409.SZ 的数据\n",
-      "成功获取 000410.SZ 的数据\n",
-      "成功获取 000411.SZ 的数据\n",
-      "成功获取 000415.SZ 的数据\n",
-      "成功获取 000417.SZ 的数据\n",
-      "成功获取 000419.SZ 的数据\n",
-      "成功获取 000420.SZ 的数据\n",
-      "成功获取 000421.SZ 的数据\n",
-      "成功获取 000422.SZ 的数据\n",
-      "成功获取 000423.SZ 的数据\n",
-      "成功获取 000425.SZ 的数据\n",
-      "成功获取 000426.SZ 的数据\n",
-      "成功获取 000428.SZ 的数据\n",
-      "成功获取 000429.SZ 的数据\n",
-      "成功获取 000430.SZ 的数据\n",
-      "成功获取 000488.SZ 的数据\n",
-      "成功获取 000498.SZ 的数据\n",
-      "成功获取 000501.SZ 的数据\n",
-      "成功获取 000503.SZ 的数据\n",
-      "成功获取 000504.SZ 的数据\n",
-      "成功获取 000505.SZ 的数据\n",
-      "成功获取 000506.SZ 的数据\n",
-      "成功获取 000507.SZ 的数据\n",
-      "成功获取 000509.SZ 的数据\n",
-      "成功获取 000510.SZ 的数据\n",
-      "成功获取 000513.SZ 的数据\n",
-      "成功获取 000514.SZ 的数据\n",
-      "成功获取 000516.SZ 的数据\n",
-      "成功获取 000517.SZ 的数据\n",
-      "成功获取 000518.SZ 的数据\n",
-      "成功获取 000519.SZ 的数据\n",
-      "成功获取 000520.SZ 的数据\n",
-      "成功获取 000521.SZ 的数据\n",
-      "成功获取 000523.SZ 的数据\n",
-      "成功获取 000524.SZ 的数据\n",
-      "成功获取 000525.SZ 的数据\n",
-      "成功获取 000526.SZ 的数据\n",
-      "成功获取 000528.SZ 的数据\n",
-      "成功获取 000529.SZ 的数据\n",
-      "成功获取 000530.SZ 的数据\n",
-      "成功获取 000531.SZ 的数据\n",
-      "成功获取 000532.SZ 的数据\n",
-      "成功获取 000533.SZ 的数据\n",
-      "成功获取 000534.SZ 的数据\n",
-      "成功获取 000536.SZ 的数据\n",
-      "成功获取 000537.SZ 的数据\n",
-      "成功获取 000538.SZ 的数据\n",
-      "成功获取 000539.SZ 的数据\n",
-      "成功获取 000541.SZ 的数据\n",
-      "成功获取 000543.SZ 的数据\n",
-      "成功获取 000544.SZ 的数据\n",
-      "成功获取 000545.SZ 的数据\n",
-      "成功获取 000546.SZ 的数据\n",
-      "成功获取 000547.SZ 的数据\n",
-      "成功获取 000548.SZ 的数据\n",
-      "成功获取 000550.SZ 的数据\n",
-      "成功获取 000551.SZ 的数据\n",
-      "成功获取 000552.SZ 的数据\n",
-      "成功获取 000553.SZ 的数据\n",
-      "成功获取 000554.SZ 的数据\n",
-      "成功获取 000555.SZ 的数据\n",
-      "成功获取 000557.SZ 的数据\n",
-      "成功获取 000558.SZ 的数据\n",
-      "成功获取 000559.SZ 的数据\n",
-      "成功获取 000560.SZ 的数据\n",
-      "成功获取 000561.SZ 的数据\n",
-      "成功获取 000563.SZ 的数据\n",
-      "成功获取 000564.SZ 的数据\n",
-      "成功获取 000565.SZ 的数据\n",
-      "成功获取 000566.SZ 的数据\n",
-      "成功获取 000567.SZ 的数据\n",
-      "成功获取 000568.SZ 的数据\n",
-      "成功获取 000570.SZ 的数据\n",
-      "成功获取 000571.SZ 的数据\n",
-      "成功获取 000572.SZ 的数据\n",
-      "成功获取 000573.SZ 的数据\n",
-      "成功获取 000576.SZ 的数据\n",
-      "成功获取 000581.SZ 的数据\n",
-      "成功获取 000582.SZ 的数据\n",
-      "成功获取 000584.SZ 的数据\n",
-      "成功获取 000586.SZ 的数据\n",
-      "成功获取 000589.SZ 的数据\n",
-      "成功获取 000590.SZ 的数据\n",
-      "成功获取 000591.SZ 的数据\n",
-      "成功获取 000592.SZ 的数据\n",
-      "成功获取 000593.SZ 的数据\n",
-      "成功获取 000595.SZ 的数据\n",
-      "成功获取 000596.SZ 的数据\n",
-      "成功获取 000597.SZ 的数据\n",
-      "成功获取 000598.SZ 的数据\n",
-      "成功获取 000599.SZ 的数据\n",
-      "成功获取 000600.SZ 的数据\n",
-      "成功获取 000601.SZ 的数据\n",
-      "成功获取 000603.SZ 的数据\n",
-      "成功获取 000605.SZ 的数据\n",
-      "成功获取 000607.SZ 的数据\n",
-      "成功获取 000608.SZ 的数据\n",
-      "成功获取 000609.SZ 的数据\n",
-      "成功获取 000610.SZ 的数据\n",
-      "成功获取 000612.SZ 的数据\n",
-      "成功获取 000615.SZ 的数据\n",
-      "成功获取 000617.SZ 的数据\n",
-      "成功获取 000619.SZ 的数据\n",
-      "成功获取 000620.SZ 的数据\n",
-      "成功获取 000622.SZ 的数据\n",
-      "成功获取 000623.SZ 的数据\n",
-      "成功获取 000625.SZ 的数据\n",
-      "成功获取 000626.SZ 的数据\n",
-      "成功获取 000627.SZ 的数据\n",
-      "成功获取 000628.SZ 的数据\n",
-      "成功获取 000629.SZ 的数据\n",
-      "成功获取 000630.SZ 的数据\n",
-      "成功获取 000631.SZ 的数据\n",
-      "成功获取 000632.SZ 的数据\n",
-      "成功获取 000633.SZ 的数据\n",
-      "成功获取 000635.SZ 的数据\n",
-      "成功获取 000636.SZ 的数据\n",
-      "成功获取 000637.SZ 的数据\n",
-      "成功获取 000638.SZ 的数据\n",
-      "成功获取 000639.SZ 的数据\n",
-      "成功获取 000650.SZ 的数据\n",
-      "成功获取 000651.SZ 的数据\n",
-      "成功获取 000652.SZ 的数据\n",
-      "成功获取 000655.SZ 的数据\n",
-      "成功获取 000656.SZ 的数据\n",
-      "成功获取 000657.SZ 的数据\n",
-      "成功获取 000659.SZ 的数据\n",
-      "成功获取 000661.SZ 的数据\n",
-      "成功获取 000663.SZ 的数据\n",
-      "成功获取 000665.SZ 的数据\n",
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-      "成功获取 000669.SZ 的数据\n",
-      "成功获取 000670.SZ 的数据\n",
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-      "成功获取 000676.SZ 的数据\n",
-      "成功获取 000677.SZ 的数据\n",
-      "成功获取 000678.SZ 的数据\n",
-      "成功获取 000679.SZ 的数据\n",
-      "成功获取 000680.SZ 的数据\n",
-      "成功获取 000681.SZ 的数据\n",
-      "成功获取 000682.SZ 的数据\n",
-      "成功获取 000683.SZ 的数据\n",
-      "成功获取 000685.SZ 的数据\n",
-      "成功获取 000686.SZ 的数据\n",
-      "成功获取 000688.SZ 的数据\n",
-      "成功获取 000690.SZ 的数据\n",
-      "成功获取 000691.SZ 的数据\n",
-      "成功获取 000692.SZ 的数据\n",
-      "成功获取 000695.SZ 的数据\n",
-      "成功获取 000697.SZ 的数据\n",
-      "成功获取 000698.SZ 的数据\n",
-      "成功获取 000700.SZ 的数据\n",
-      "成功获取 000701.SZ 的数据\n",
-      "成功获取 000702.SZ 的数据\n",
-      "成功获取 000703.SZ 的数据\n",
-      "成功获取 000705.SZ 的数据\n",
-      "成功获取 000707.SZ 的数据\n",
-      "成功获取 000708.SZ 的数据\n",
-      "成功获取 000709.SZ 的数据\n",
-      "成功获取 000710.SZ 的数据\n",
-      "成功获取 000711.SZ 的数据\n",
-      "成功获取 000712.SZ 的数据\n",
-      "成功获取 000713.SZ 的数据\n",
-      "成功获取 000715.SZ 的数据\n",
-      "成功获取 000716.SZ 的数据\n",
-      "成功获取 000717.SZ 的数据\n",
-      "成功获取 000718.SZ 的数据\n",
-      "成功获取 000719.SZ 的数据\n",
-      "成功获取 000720.SZ 的数据\n",
-      "成功获取 000721.SZ 的数据\n",
-      "成功获取 000722.SZ 的数据\n",
-      "成功获取 000723.SZ 的数据\n",
-      "成功获取 000725.SZ 的数据\n",
-      "成功获取 000726.SZ 的数据\n",
-      "成功获取 000727.SZ 的数据\n",
-      "成功获取 000728.SZ 的数据\n",
-      "成功获取 000729.SZ 的数据\n",
-      "成功获取 000731.SZ 的数据\n",
-      "成功获取 000733.SZ 的数据\n",
-      "成功获取 000735.SZ 的数据\n",
-      "成功获取 000736.SZ 的数据\n",
-      "成功获取 000737.SZ 的数据\n",
-      "成功获取 000738.SZ 的数据\n",
-      "成功获取 000739.SZ 的数据\n",
-      "成功获取 000750.SZ 的数据\n",
-      "成功获取 000751.SZ 的数据\n",
-      "成功获取 000752.SZ 的数据\n",
-      "成功获取 000753.SZ 的数据\n",
-      "成功获取 000755.SZ 的数据\n",
-      "成功获取 000756.SZ 的数据\n",
-      "成功获取 000757.SZ 的数据\n",
-      "成功获取 000758.SZ 的数据\n",
-      "成功获取 000759.SZ 的数据\n",
-      "成功获取 000761.SZ 的数据\n",
-      "成功获取 000762.SZ 的数据\n",
-      "成功获取 000766.SZ 的数据\n",
-      "成功获取 000767.SZ 的数据\n",
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@@ -5448,7 +212,10 @@
     "\n",
     "\n",
     "# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）\n",
-    "stocks_df = pd.read_csv('../../stocks_list.csv', encoding='utf-8-sig')\n",
+    "from concurrent.futures import ThreadPoolExecutor, as_completed\n",
+    "\n",
+    "\n",
+    "stocks_df = pd.read_csv('/mnt/d/PyProject/NewStock/stocks_list.csv', encoding='utf-8-sig')\n",
     "\n",
     "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n",
     "daily_data_list = []\n",
@@ -5457,25 +224,33 @@
     "api_call_count = 0\n",
     "batch_start_time = time.time()\n",
     "\n",
-    "# 循环遍历每个股票代码并获取数据\n",
-    "for idx, row in stocks_df.iterrows():\n",
-    "    ts_code = row['ts_code']  # 假设股票代码列名为 ts_code\n",
-    "    try:\n",
-    "        # 调用 tushare 接口获取该股票自 2017 年以来的日线数据\n",
-    "        daily_data = ts.pro_bar(ts_code=ts_code, adj='hfq', start_date='20170101')\n",
-    "        # 如果返回数据不为空，则添加一列标识股票代码\n",
-    "        if not daily_data.empty:\n",
-    "            daily_data['ts_code'] = ts_code\n",
-    "            daily_data_list.append(daily_data)\n",
-    "        print(f\"成功获取 {ts_code} 的数据\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"获取 {ts_code} 数据时出错: {e}\")\n",
+    "def get_data(ts_code):\n",
+    "    start_time = time.time()\n",
+    "    daily_data = ts.pro_bar(ts_code=ts_code, adj='hfq', start_date='20170101')\n",
+    "    # 如果返回数据不为空，则添加一列标识股票代码\n",
+    "    if time.time() - start_time < 0.15:\n",
+    "        time.sleep(0.5)\n",
+    "    if daily_data is not None and not daily_data.empty:\n",
+    "        return daily_data\n",
     "\n",
+    "\n",
+    "# 循环遍历每个股票代码并获取数据\n",
+    "with ThreadPoolExecutor(max_workers=4) as executor:\n",
+    "    future_to_date = {executor.submit(get_data, row['ts_code']): row['ts_code'] for idx, row in stocks_df.iterrows()}\n",
+    "\n",
+    "    for future in as_completed(future_to_date):\n",
+    "        ts_code = future_to_date[future]\n",
+    "        try:\n",
+    "            result = future.result()\n",
+    "            daily_data_list.append(result)\n",
+    "            print(f\"任务 {ts_code} 完成\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"获取 {ts_code} 数据时出错: {e}\")\n",
     "    # 计数一次 API 调用\n",
     "    api_call_count += 1\n",
     "\n",
     "    # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n",
-    "    if api_call_count % 300 == 0:\n",
+    "    if api_call_count % 150 == 0:\n",
     "        elapsed = time.time() - batch_start_time\n",
     "        if elapsed < 60:\n",
     "            sleep_time = 60 - elapsed\n",
@@ -5483,12 +258,13 @@
     "            time.sleep(sleep_time)\n",
     "        # 重置批次起始时间\n",
     "        batch_start_time = time.time()\n",
+    "\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "85bdf760cb83fbd3",
    "metadata": {
     "ExecuteTime": {
@@ -5510,7 +286,7 @@
     "if daily_data_list:\n",
     "    all_daily_data = pd.concat(daily_data_list, ignore_index=True)\n",
     "    # 使用 HDF5 格式保存到本地文件（文件名：daily_data.h5，key设为 'daily_data'）\n",
-    "    all_daily_data.to_hdf('../../data/daily_data.h5', key='daily_data', mode='w', format='table')\n",
+    "    all_daily_data.to_hdf('/mnt/d/PyProject/NewStock/data/daily_data.h5', key='daily_data', mode='w', format='table')\n",
     "    print(\"所有日线数据已保存到 daily_data.h5\")\n",
     "else:\n",
     "    print(\"未获取到任何日线数据。\")"
@@ -5526,15 +302,35 @@
      "start_time": "2025-02-07T16:25:19.768620Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     ts_code trade_date     open     high      low    close  pre_close  \\\n",
+      "0  000001.SZ   20250822  1601.22  1603.86  1578.82  1589.36    1601.22   \n",
+      "1  000001.SZ   20250821  1588.04  1606.49  1585.41  1601.22    1590.68   \n",
+      "2  000001.SZ   20250820  1589.36  1597.27  1578.82  1590.68    1589.36   \n",
+      "3  000001.SZ   20250819  1594.63  1597.27  1584.09  1589.36    1592.00   \n",
+      "4  000001.SZ   20250818  1589.36  1601.22  1585.41  1592.00    1592.00   \n",
+      "\n",
+      "   change  pct_chg         vol       amount  \n",
+      "0  -11.86    -0.74  1644259.77  1979461.311  \n",
+      "1   10.54     0.66  1217418.25  1477053.292  \n",
+      "2    1.32     0.08  1048738.53  1265435.773  \n",
+      "3   -2.64    -0.17   892859.89  1077380.173  \n",
+      "4    0.00     0.00  1239058.37  1496527.039  \n"
+     ]
+    }
+   ],
    "source": [
-    "print(all_daily_data[all_daily_data['ts_code'] == '000001.SZ'].tail())"
+    "print(all_daily_data[all_daily_data['ts_code'] == '000001.SZ'].head())"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "stock",
    "language": "python",
    "name": "python3"
   },
@@ -5548,7 +344,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.19"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,
diff --git a/main/data/index_and_industry.ipynb b/main/data/index_and_industry.ipynb
index 87d32d2..47480b1 100644
--- a/main/data/index_and_industry.ipynb
+++ b/main/data/index_and_industry.ipynb
@@ -2,12 +2,11 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "initial_id",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-09T14:57:27.092313Z",
-     "start_time": "2025-04-09T14:57:26.124592Z"
+    "jupyter": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -24,11 +23,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "f448da220816bf98",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-09T14:57:37.680808Z",
+     "end_time": "2025-07-26T10:23:18.517518100Z",
      "start_time": "2025-04-09T14:57:27.392846Z"
     }
    },
@@ -70,11 +69,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "907f732d3c397bf",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-09T14:57:37.730922Z",
+     "end_time": "2025-07-26T10:23:18.552166300Z",
      "start_time": "2025-04-09T14:57:37.695917Z"
     }
    },
@@ -84,32 +83,32 @@
      "output_type": "stream",
      "text": [
       "         ts_code trade_date      close       open       high        low  \\\n",
-      "0      000905.SH   20250530  5671.0723  5704.7710  5704.7710  5665.5177   \n",
-      "1      000905.SH   20250529  5719.9101  5637.0633  5724.5185  5637.0633   \n",
-      "2      000905.SH   20250528  5637.2378  5651.8755  5660.4696  5628.4165   \n",
-      "3      000905.SH   20250527  5652.1454  5666.3027  5667.8710  5629.1343   \n",
-      "4      000905.SH   20250526  5669.4609  5653.2063  5693.6250  5644.5794   \n",
+      "0      000905.SH   20251010  7398.2241  7499.3917  7509.1161  7373.9841   \n",
+      "1      000905.SH   20251009  7548.9226  7470.0474  7559.0920  7437.3242   \n",
+      "2      000905.SH   20250930  7412.3684  7372.5240  7428.0307  7372.0634   \n",
+      "3      000905.SH   20250929  7350.5599  7251.5221  7377.2217  7216.7357   \n",
+      "4      000905.SH   20250926  7240.9114  7311.8433  7351.7931  7237.0459   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
-      "13546  399006.SZ   20100607  1069.4680  1005.0280  1075.2250  1001.7020   \n",
-      "13547  399006.SZ   20100604  1027.6810   989.6810  1027.6810   986.5040   \n",
-      "13548  399006.SZ   20100603   998.3940  1002.3550  1026.7020   997.7750   \n",
-      "13549  399006.SZ   20100602   997.1190   967.6090   997.1190   952.6110   \n",
-      "13550  399006.SZ   20100601   973.2330   986.0150   994.7930   948.1180   \n",
+      "13810  399006.SZ   20100607  1069.4680  1005.0280  1075.2250  1001.7020   \n",
+      "13811  399006.SZ   20100604  1027.6810   989.6810  1027.6810   986.5040   \n",
+      "13812  399006.SZ   20100603   998.3940  1002.3550  1026.7020   997.7750   \n",
+      "13813  399006.SZ   20100602   997.1190   967.6090   997.1190   952.6110   \n",
+      "13814  399006.SZ   20100601   973.2330   986.0150   994.7930   948.1180   \n",
       "\n",
-      "       pre_close   change  pct_chg           vol        amount  \n",
-      "0      5719.9101 -48.8378  -0.8538  1.099007e+08  1.376706e+08  \n",
-      "1      5637.2378  82.6723   1.4665  1.146825e+08  1.480951e+08  \n",
-      "2      5652.1454 -14.9076  -0.2638  9.490888e+07  1.199598e+08  \n",
-      "3      5669.4609 -17.3155  -0.3054  9.514936e+07  1.252757e+08  \n",
-      "4      5653.0436  16.4173   0.2904  9.717099e+07  1.273436e+08  \n",
-      "...          ...      ...      ...           ...           ...  \n",
-      "13546  1027.6810  41.7870   4.0661  2.655275e+06  9.106095e+06  \n",
-      "13547   998.3940  29.2870   2.9334  1.500295e+06  5.269441e+06  \n",
-      "13548   997.1190   1.2750   0.1279  1.616805e+06  6.240835e+06  \n",
-      "13549   973.2330  23.8860   2.4543  1.074628e+06  4.001206e+06  \n",
-      "13550  1000.0000 -26.7670  -2.6767  1.356285e+06  4.924177e+06  \n",
+      "       pre_close    change  pct_chg           vol        amount  \n",
+      "0      7548.9226 -150.6985  -1.9963  2.622566e+08  5.021274e+08  \n",
+      "1      7412.3684  136.5542   1.8422  2.831308e+08  5.357568e+08  \n",
+      "2      7350.5599   61.8085   0.8409  2.207075e+08  4.449564e+08  \n",
+      "3      7240.9114  109.6485   1.5143  2.335394e+08  4.338645e+08  \n",
+      "4      7341.3238 -100.4124  -1.3678  2.114441e+08  4.301976e+08  \n",
+      "...          ...       ...      ...           ...           ...  \n",
+      "13810  1027.6810   41.7870   4.0661  2.655275e+06  9.106095e+06  \n",
+      "13811   998.3940   29.2870   2.9334  1.500295e+06  5.269441e+06  \n",
+      "13812   997.1190    1.2750   0.1279  1.616805e+06  6.240835e+06  \n",
+      "13813   973.2330   23.8860   2.4543  1.074628e+06  4.001206e+06  \n",
+      "13814  1000.0000  -26.7670  -2.6767  1.356285e+06  4.924177e+06  \n",
       "\n",
-      "[13551 rows x 11 columns]\n"
+      "[13815 rows x 11 columns]\n"
      ]
     }
    ],
diff --git a/main/data/update/cyq-perf.ipynb b/main/data/update/cyq-perf.ipynb
index 3a3eea3..789dceb 100644
--- a/main/data/update/cyq-perf.ipynb
+++ b/main/data/update/cyq-perf.ipynb
@@ -39,15 +39,15 @@
       "3      000006.SZ   20250312\n",
       "4      000007.SZ   20250312\n",
       "...          ...        ...\n",
-      "26917  920445.BJ   20250519\n",
-      "26918  920489.BJ   20250519\n",
-      "26919  920682.BJ   20250519\n",
-      "26920  920799.BJ   20250519\n",
-      "26921  920819.BJ   20250519\n",
+      "27111  920445.BJ   20250922\n",
+      "27112  920489.BJ   20250922\n",
+      "27113  920682.BJ   20250922\n",
+      "27114  920799.BJ   20250922\n",
+      "27115  920819.BJ   20250922\n",
       "\n",
-      "[7724334 rows x 2 columns]\n",
-      "20250523\n",
-      "start_date: 20250526\n"
+      "[8205543 rows x 2 columns]\n",
+      "20250926\n",
+      "start_date: 20250929\n"
      ]
     }
    ],
@@ -64,7 +64,7 @@
     "    max_date = df['trade_date'].max()\n",
     "\n",
     "print(max_date)\n",
-    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250620')\n",
+    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
     "trade_cal = trade_cal[trade_cal['is_open'] == 1]  # 只保留交易日\n",
     "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
     "start_date = min(trade_dates)\n",
@@ -86,25 +86,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "任务 20250620 完成\n",
-      "任务 20250619 完成\n",
-      "任务 20250618 完成\n",
-      "任务 20250617 完成\n",
-      "任务 20250616 完成\n",
-      "任务 20250613 完成\n",
-      "任务 20250612 完成\n",
-      "任务 20250611 完成\n",
-      "任务 20250610 完成\n",
-      "任务 20250609 完成\n",
-      "任务 20250606 完成\n",
-      "任务 20250605 完成\n",
-      "任务 20250604 完成\n",
-      "任务 20250603 完成\n",
-      "任务 20250530 完成\n",
-      "任务 20250529 完成\n",
-      "任务 20250528 完成\n",
-      "任务 20250527 完成\n",
-      "任务 20250526 完成\n"
+      "任务 20251020 完成\n",
+      "任务 20251017 完成\n",
+      "任务 20251016 完成\n",
+      "任务 20251015 完成\n",
+      "任务 20251014 完成\n",
+      "任务 20251013 完成\n",
+      "任务 20251010 完成\n",
+      "任务 20251009 完成\n",
+      "任务 20250930 完成\n",
+      "任务 20250929 完成\n"
      ]
     }
    ],
diff --git a/main/data/update/sw_daily.ipynb b/main/data/update/sw_daily.ipynb
index 88344a0..923987f 100644
--- a/main/data/update/sw_daily.ipynb
+++ b/main/data/update/sw_daily.ipynb
@@ -39,15 +39,15 @@
       "3     801005.SI   20250221\n",
       "4     801010.SI   20250221\n",
       "...         ...        ...\n",
-      "2190  859811.SI   20250519\n",
-      "2191  859821.SI   20250519\n",
-      "2192  859822.SI   20250519\n",
-      "2193  859852.SI   20250519\n",
-      "2194  859951.SI   20250519\n",
+      "2190  859811.SI   20250922\n",
+      "2191  859821.SI   20250922\n",
+      "2192  859822.SI   20250922\n",
+      "2193  859852.SI   20250922\n",
+      "2194  859951.SI   20250922\n",
       "\n",
-      "[1071172 rows x 2 columns]\n",
-      "20250523\n",
-      "start_date: 20250526\n"
+      "[1110243 rows x 2 columns]\n",
+      "20250926\n",
+      "start_date: 20250929\n"
      ]
     }
    ],
@@ -64,7 +64,7 @@
     "    max_date = df['trade_date'].max()\n",
     "\n",
     "print(max_date)\n",
-    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250620')\n",
+    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
     "trade_cal = trade_cal[trade_cal['is_open'] == 1]  # 只保留交易日\n",
     "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
     "start_date = min(trade_dates)\n",
@@ -86,25 +86,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "任务 20250619 完成\n",
-      "任务 20250620 完成\n",
-      "任务 20250618 完成\n",
-      "任务 20250617 完成\n",
-      "任务 20250616 完成\n",
-      "任务 20250613 完成\n",
-      "任务 20250612 完成\n",
-      "任务 20250611 完成\n",
-      "任务 20250609 完成\n",
-      "任务 20250610 完成\n",
-      "任务 20250606 完成\n",
-      "任务 20250605 完成\n",
-      "任务 20250604 完成\n",
-      "任务 20250603 完成\n",
-      "任务 20250530 完成\n",
-      "任务 20250529 完成\n",
-      "任务 20250527 完成\n",
-      "任务 20250528 完成\n",
-      "任务 20250526 完成\n"
+      "任务 20251020 完成\n",
+      "任务 20251017 完成\n",
+      "任务 20251016 完成\n",
+      "任务 20251015 完成\n",
+      "任务 20251014 完成\n",
+      "任务 20251013 完成\n",
+      "任务 20251010 完成\n",
+      "任务 20251009 完成\n",
+      "任务 20250930 完成\n",
+      "任务 20250929 完成\n"
      ]
     }
    ],
diff --git a/main/data/update/update_daily_basic.ipynb b/main/data/update/update_daily_basic.ipynb
index f86a7ac..b27b4bb 100644
--- a/main/data/update/update_daily_basic.ipynb
+++ b/main/data/update/update_daily_basic.ipynb
@@ -94,17 +94,17 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 8674588 entries, 0 to 26945\n",
+      "Index: 9155905 entries, 0 to 27115\n",
       "Data columns (total 2 columns):\n",
       " #   Column      Dtype \n",
       "---  ------      ----- \n",
       " 0   ts_code     object\n",
       " 1   trade_date  object\n",
       "dtypes: object(2)\n",
-      "memory usage: 198.5+ MB\n",
+      "memory usage: 209.6+ MB\n",
       "None\n",
-      "20250523\n",
-      "20250526\n"
+      "20250926\n",
+      "20250929\n"
      ]
     }
    ],
@@ -121,7 +121,7 @@
     "    max_date = df['trade_date'].max()\n",
     "\n",
     "print(max_date)\n",
-    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250720')\n",
+    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
     "trade_cal = trade_cal[trade_cal['is_open'] == 1]  # 只保留交易日\n",
     "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
     "start_date = min(trade_dates)\n",
@@ -144,45 +144,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "任务 20250718 完成\n",
-      "任务 20250717 完成\n",
-      "任务 20250716 完成\n",
-      "任务 20250715 完成\n",
-      "任务 20250714 完成\n",
-      "任务 20250711 完成\n",
-      "任务 20250709 完成\n",
-      "任务 20250710 完成\n",
-      "任务 20250708 完成\n",
-      "任务 20250707 完成\n",
-      "任务 20250704 完成\n",
-      "任务 20250703 完成\n",
-      "任务 20250702 完成\n",
-      "任务 20250701 完成\n",
-      "任务 20250630 完成\n",
-      "任务 20250627 完成\n",
-      "任务 20250626 完成\n",
-      "任务 20250625 完成\n",
-      "任务 20250624 完成\n",
-      "任务 20250623 完成\n",
-      "任务 20250620 完成\n",
-      "任务 20250619 完成\n",
-      "任务 20250618 完成\n",
-      "任务 20250617 完成\n",
-      "任务 20250616 完成\n",
-      "任务 20250613 完成\n",
-      "任务 20250612 完成\n",
-      "任务 20250611 完成\n",
-      "任务 20250610 完成\n",
-      "任务 20250609 完成\n",
-      "任务 20250606 完成\n",
-      "任务 20250605 完成\n",
-      "任务 20250603 完成\n",
-      "任务 20250604 完成\n",
-      "任务 20250530 完成\n",
-      "任务 20250529 完成\n",
-      "任务 20250528 完成\n",
-      "任务 20250527 完成\n",
-      "任务 20250526 完成\n"
+      "任务 20251017 完成\n",
+      "任务 20251020 完成\n",
+      "任务 20251015 完成\n",
+      "任务 20251016 完成\n",
+      "任务 20251014 完成\n",
+      "任务 20251013 完成\n",
+      "任务 20251010 完成\n",
+      "任务 20251009 完成\n",
+      "任务 20250930 完成\n",
+      "任务 20250929 完成\n"
      ]
     }
    ],
@@ -253,58 +224,58 @@
      "output_type": "stream",
      "text": [
       "         ts_code trade_date  close  turnover_rate  turnover_rate_f  \\\n",
-      "0      603990.SH   20250530  14.96         3.7919           4.9168   \n",
-      "1      603666.SH   20250530  33.72         2.4954           4.7137   \n",
-      "2      001339.SZ   20250530  45.78         7.0710           7.0710   \n",
-      "3      002006.SZ   20250530  16.67         2.4368           3.4806   \n",
-      "4      603353.SH   20250530  15.21         1.3567           4.1316   \n",
+      "0      600642.SH   20251010   8.03         0.4806           1.3835   \n",
+      "1      600295.SH   20251010  10.76         0.8549           3.7056   \n",
+      "2      600444.SH   20251010  19.00         9.6611          17.4605   \n",
+      "3      605100.SH   20251010  28.72         3.4770           7.6902   \n",
+      "4      301399.SZ   20251010  19.53         3.9562           4.6772   \n",
       "...          ...        ...    ...            ...              ...   \n",
-      "26918  002670.SZ   20250526  11.86         0.7662           2.3092   \n",
-      "26919  839946.BJ   20250526   9.67         4.8520           6.8863   \n",
-      "26920  688076.SH   20250526  49.59         5.9483           9.5054   \n",
-      "26921  300519.SZ   20250526  14.44         2.4601           3.8976   \n",
-      "26922  300468.SZ   20250526  18.15         6.8275           8.8410   \n",
+      "21679  600653.SH   20250929   2.13         2.1746           2.9589   \n",
+      "21680  002344.SZ   20250929   4.49         1.7080           3.6338   \n",
+      "21681  301162.SZ   20250929  60.30         2.8491           3.5744   \n",
+      "21682  920077.BJ   20250929  14.43         1.1113           1.6410   \n",
+      "21683  300283.SZ   20250929   7.04         4.8583           5.7018   \n",
       "\n",
-      "       volume_ratio        pe    pe_ttm      pb          ps      ps_ttm  \\\n",
-      "0              0.65       NaN       NaN  5.5665      9.8735     11.0137   \n",
-      "1              1.15       NaN       NaN  3.2133     11.8990     10.3525   \n",
-      "2              1.22   91.7742   74.3709  5.3909      2.8419      2.7478   \n",
-      "3              0.81   58.9666   65.5384  3.6508      5.0124      5.4591   \n",
-      "4              1.10   90.1163   80.8019  1.5917      0.9380      0.9517   \n",
-      "...             ...       ...       ...     ...         ...         ...   \n",
-      "26918          0.75  137.0866  106.8454  2.0610  15093.0115  14821.3328   \n",
-      "26919          0.55       NaN       NaN  5.7695      2.5489      2.4978   \n",
-      "26920          3.15   27.5757   22.7263  3.7628      6.8632      6.0784   \n",
-      "26921          1.14   45.8504   44.3443  2.7022      8.6318      8.8737   \n",
-      "26922          1.08  142.9746  150.8960  5.8350     13.0086     13.6702   \n",
+      "       volume_ratio        pe    pe_ttm      pb       ps   ps_ttm  dv_ratio  \\\n",
+      "0              1.49    9.9635   10.2617  1.1073   1.3268   1.3600    4.9816   \n",
+      "1              1.56   16.3053   16.4683  1.4839   1.0603   1.1230    7.4349   \n",
+      "2              2.84   69.2746   55.7147  3.8398   3.6313   3.5392    0.5263   \n",
+      "3              0.55   66.7896  123.2961  2.7276   5.3634   6.7180    2.0794   \n",
+      "4              0.94   60.7990   75.8958  2.7675   6.8812   7.1828    1.2177   \n",
+      "...             ...       ...       ...     ...      ...      ...       ...   \n",
+      "21679          0.72  107.4073  227.6354  5.4498   0.9887   0.9724    0.0000   \n",
+      "21680          0.70   64.8238   75.9239  0.6834   5.5516   5.5560    0.9577   \n",
+      "21681          0.96   85.4251   76.2427  5.3380  14.5424  12.3677    0.5586   \n",
+      "21682          0.51   90.3399   82.4861  3.3572   5.2895   4.1636       NaN   \n",
+      "21683          0.94       NaN       NaN  3.2821   1.1161   0.9970    0.2499   \n",
       "\n",
-      "       dv_ratio  dv_ttm  total_share  float_share  free_share      total_mv  \\\n",
-      "0        0.0000     NaN   30628.2731   30628.2731  23620.5583  4.581990e+05   \n",
-      "1        0.0000     NaN   20649.0816   20649.0816  10931.3716  6.962870e+05   \n",
-      "2        0.2622  0.3498   25042.9670    7313.0995   7313.0995  1.146467e+06   \n",
-      "3        0.7749  0.7749   51979.3440   45516.0000  31865.7600  8.664957e+05   \n",
-      "4        0.6462  1.3036   17339.4000   17041.8000   5596.0000  2.637323e+05   \n",
-      "...         ...     ...          ...          ...         ...           ...   \n",
-      "26918    0.0000     NaN  193508.4653  162335.0634  53860.6790  2.295010e+06   \n",
-      "26919       NaN     NaN   13499.0443    9702.8595   6836.5574  1.305358e+05   \n",
-      "26920       NaN     NaN   22487.0915   22487.0915  14071.9565  1.115135e+06   \n",
-      "26921    2.7701  2.7701   16000.0000   11410.0000   7201.9100  2.310400e+05   \n",
-      "26922    0.3306  0.3306   53064.9275   52979.4065  40913.5262  9.631284e+05   \n",
+      "       dv_ttm  total_share  float_share   free_share      total_mv  \\\n",
+      "0      5.6040  489407.9376  489381.3156  170006.8520  3.929946e+06   \n",
+      "1      5.5762  279877.6254  197557.6254   45577.9458  3.011483e+06   \n",
+      "2      0.5789   14642.1932   14642.1932    8101.7360  2.782017e+05   \n",
+      "3      1.0446   17113.2000   16993.2000    7683.2000  4.914911e+05   \n",
+      "4      1.0594   18502.0000    5468.3586    4625.5000  3.613441e+05   \n",
+      "...       ...          ...          ...          ...           ...   \n",
+      "21679     NaN  194638.0317  194638.0317  143048.5612  4.145790e+05   \n",
+      "21680  0.8463  128261.6960  128145.0092   60233.0025  5.758950e+05   \n",
+      "21681  0.9704   13258.3724    8522.5548    6793.1764  7.994799e+05   \n",
+      "21682     NaN   58768.1817   31695.6817   21464.7599  8.480249e+05   \n",
+      "21683     NaN   49697.8222   36721.8502   31289.2680  3.498727e+05   \n",
       "\n",
       "            circ_mv  is_st  \n",
-      "0      4.581990e+05  False  \n",
-      "1      6.962870e+05  False  \n",
-      "2      3.347937e+05  False  \n",
-      "3      7.587517e+05  False  \n",
-      "4      2.592058e+05  False  \n",
+      "0      3.929732e+06  False  \n",
+      "1      2.125720e+06  False  \n",
+      "2      2.782017e+05  False  \n",
+      "3      4.880447e+05  False  \n",
+      "4      1.067970e+05  False  \n",
       "...             ...    ...  \n",
-      "26918  1.925294e+06  False  \n",
-      "26919  9.382665e+04  False  \n",
-      "26920  1.115135e+06  False  \n",
-      "26921  1.647604e+05  False  \n",
-      "26922  9.615762e+05  False  \n",
+      "21679  4.145790e+05  False  \n",
+      "21680  5.753711e+05  False  \n",
+      "21681  5.139101e+05  False  \n",
+      "21682  4.573687e+05  False  \n",
+      "21683  2.585218e+05  False  \n",
       "\n",
-      "[26923 rows x 19 columns]\n"
+      "[21684 rows x 19 columns]\n"
      ]
     }
    ],
@@ -329,58 +300,45 @@
      "output_type": "stream",
      "text": [
       "         ts_code trade_date  close  turnover_rate  turnover_rate_f  \\\n",
-      "16     300536.SZ   20250530   8.67         2.8854           3.5632   \n",
-      "78     000668.SZ   20250530   7.94         4.1498           7.0226   \n",
-      "112    002231.SZ   20250530   3.28         8.9944          10.0552   \n",
-      "147    300313.SZ   20250530   6.28         6.0110          12.4720   \n",
-      "158    603838.SH   20250530   5.73         0.9777           2.6542   \n",
+      "9      300313.SZ   20251010   8.84         3.1146           6.4625   \n",
+      "20     603838.SH   20251010   7.80         0.5503           1.5146   \n",
+      "29     603813.SH   20251010  24.06         1.5835           4.5173   \n",
+      "48     002742.SZ   20251010   4.65         1.0473           1.2924   \n",
+      "69     603559.SH   20251010   8.50         0.2072           0.2945   \n",
       "...          ...        ...    ...            ...              ...   \n",
-      "26733  603828.SH   20250526   4.98         0.9734           1.9562   \n",
-      "26751  600599.SH   20250526   7.46         2.5125           6.3118   \n",
-      "26785  000820.SZ   20250526   3.02        13.6997          14.0750   \n",
-      "26885  002005.SZ   20250526   1.77         0.3214           0.5145   \n",
-      "26905  603869.SH   20250526   6.15         0.3000           0.7946   \n",
+      "21466  603021.SH   20250929   4.62         1.3860           2.3418   \n",
+      "21552  300020.SZ   20250929   3.58         1.5031           1.6828   \n",
+      "21554  000506.SZ   20250929  10.88        10.5560          15.7565   \n",
+      "21603  600636.SH   20250929   8.29         0.4693           0.7963   \n",
+      "21661  603843.SH   20250929   5.17         0.3798           0.5364   \n",
       "\n",
-      "       volume_ratio       pe     pe_ttm       pb       ps   ps_ttm  dv_ratio  \\\n",
-      "16             0.55      NaN        NaN   4.9112  10.9775  12.1174       0.0   \n",
-      "78             1.07      NaN        NaN   1.6212   8.7361   5.6924       0.0   \n",
-      "112            0.74      NaN        NaN   4.3227   3.9056   5.3690       0.0   \n",
-      "147            0.92      NaN        NaN      NaN  14.2840  13.5826       0.0   \n",
-      "158            1.06      NaN        NaN   1.9039   6.4291   5.8279       0.0   \n",
-      "...             ...      ...        ...      ...      ...      ...       ...   \n",
-      "26733          0.56  345.783  1670.8958   3.9261   1.2065   1.3013       0.0   \n",
-      "26751          0.68      NaN        NaN  11.2319   3.8238   3.9211       0.0   \n",
-      "26785          2.40      NaN        NaN  12.4588  15.8309  20.1399       0.0   \n",
-      "26885          0.48      NaN        NaN  15.9120   4.2066   4.2221       0.0   \n",
-      "26905          1.00  149.594   167.2545   0.8344   4.6640   5.0668       0.0   \n",
+      "       volume_ratio  pe  pe_ttm       pb       ps   ps_ttm  dv_ratio  dv_ttm  \\\n",
+      "9              1.30 NaN     NaN      NaN  20.1067  20.9731    0.0000     NaN   \n",
+      "20             0.57 NaN     NaN   2.6121   8.7517   6.9304    0.0000     NaN   \n",
+      "29             1.88 NaN     NaN   4.5222   8.4776   7.5124    1.0313     NaN   \n",
+      "48             1.28 NaN     NaN      NaN   1.6800   2.1226    0.0000     NaN   \n",
+      "69             0.60 NaN     NaN   3.5043   9.5964   8.2315    0.0000     NaN   \n",
+      "...             ...  ..     ...      ...      ...      ...       ...     ...   \n",
+      "21466          0.80 NaN     NaN      NaN   3.5891   3.7851    0.0000     NaN   \n",
+      "21552          1.00 NaN     NaN   0.9812   5.1924  18.4036    0.0000     NaN   \n",
+      "21554          3.17 NaN     NaN  16.4257  30.3341  23.4860    0.0000     NaN   \n",
+      "21603          0.81 NaN     NaN   1.7909  12.8512  11.0116    0.4825  0.6031   \n",
+      "21661          0.05 NaN     NaN  12.5612   2.6558   3.1369    0.0000     NaN   \n",
       "\n",
-      "       dv_ttm  total_share  float_share   free_share     total_mv  \\\n",
-      "16        NaN   29328.8133   29325.3240   23747.3240  254280.8113   \n",
-      "78        NaN   14684.1890   14684.1890    8677.2104  116592.4607   \n",
-      "112       NaN   34685.0017   29481.8767   26371.6067  113766.8056   \n",
-      "147       NaN   31297.7396   19735.2789    9511.5479  196549.8047   \n",
-      "158       NaN   32001.6000   32001.6000   11788.1468  183369.1680   \n",
-      "...       ...          ...          ...          ...          ...   \n",
-      "26733     NaN   59596.0158   59593.9625   29654.2988  296788.1587   \n",
-      "26751     NaN   16600.0000   16600.0000    6607.7948  123836.0000   \n",
-      "26785     NaN   64655.5179   29696.6877   28904.9696  195259.6641   \n",
-      "26885     NaN  175242.4858  175199.3158  109452.0915  310179.1999   \n",
-      "26905     NaN   50450.0508   50450.0508   19045.9689  310267.8124   \n",
+      "       total_share  float_share  free_share      total_mv       circ_mv  is_st  \n",
+      "9       31297.7396   19735.2789   9511.5479  2.766720e+05  1.744599e+05   True  \n",
+      "20      32001.6000   32001.6000  11627.0468  2.496125e+05  2.496125e+05   True  \n",
+      "29      10501.5000   10501.5000   3681.2000  2.526661e+05  2.526661e+05   True  \n",
+      "48      43200.0000   43185.8082  34994.8239  2.008800e+05  2.008140e+05   True  \n",
+      "69      40127.6979   40127.6979  28231.9697  3.410854e+05  3.410854e+05   True  \n",
+      "...            ...          ...         ...           ...           ...    ...  \n",
+      "21466   31994.8070   31994.8070  18936.7934  1.478160e+05  1.478160e+05   True  \n",
+      "21552   79467.7974   76663.9584  68475.6577  2.844947e+05  2.744570e+05   True  \n",
+      "21554   92901.7761   92858.4361  62210.1427  1.010771e+06  1.010300e+06   True  \n",
+      "21603   43863.6802   43863.6802  25849.6552  3.636299e+05  3.636299e+05   True  \n",
+      "21661   69962.3237   69962.3237  49541.4702  3.617052e+05  3.617052e+05   True  \n",
       "\n",
-      "           circ_mv  is_st  \n",
-      "16     254250.5591   True  \n",
-      "78     116592.4607   True  \n",
-      "112     96700.5556   True  \n",
-      "147    123937.5515   True  \n",
-      "158    183369.1680   True  \n",
-      "...            ...    ...  \n",
-      "26733  296777.9333   True  \n",
-      "26751  123836.0000   True  \n",
-      "26785   89683.9969   True  \n",
-      "26885  310102.7890   True  \n",
-      "26905  310267.8124   True  \n",
-      "\n",
-      "[944 rows x 19 columns]\n"
+      "[749 rows x 19 columns]\n"
      ]
     }
    ],
@@ -430,7 +388,7 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 8701511 entries, 0 to 26922\n",
+      "Index: 9177589 entries, 0 to 21683\n",
       "Data columns (total 3 columns):\n",
       " #   Column      Dtype \n",
       "---  ------      ----- \n",
@@ -438,7 +396,7 @@
       " 1   trade_date  object\n",
       " 2   is_st       bool  \n",
       "dtypes: bool(1), object(2)\n",
-      "memory usage: 207.5+ MB\n",
+      "memory usage: 218.8+ MB\n",
       "None\n"
      ]
     }
diff --git a/main/data/update/update_daily_data.ipynb b/main/data/update/update_daily_data.ipynb
index e5a0b94..e412527 100644
--- a/main/data/update/update_daily_data.ipynb
+++ b/main/data/update/update_daily_data.ipynb
@@ -38,17 +38,17 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 8745589 entries, 0 to 26765\n",
+      "Index: 9213093 entries, 0 to 26779\n",
       "Data columns (total 2 columns):\n",
       " #   Column      Dtype \n",
       "---  ------      ----- \n",
       " 0   ts_code     object\n",
       " 1   trade_date  object\n",
       "dtypes: object(2)\n",
-      "memory usage: 200.2+ MB\n",
+      "memory usage: 210.9+ MB\n",
       "None\n",
-      "20250523\n",
-      "20250526\n"
+      "20250926\n",
+      "20250929\n"
      ]
     }
    ],
@@ -62,7 +62,7 @@
     "    max_date = df['trade_date'].max()\n",
     "\n",
     "print(max_date)\n",
-    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250620')\n",
+    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
     "trade_cal = trade_cal[trade_cal['is_open'] == 1]  # 只保留交易日\n",
     "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
     "start_date = min(trade_dates)\n",
@@ -86,98 +86,98 @@
      "output_type": "stream",
      "text": [
       "任务 000001.SZ 完成\n",
-      "任务 000002.SZ 完成\n",
       "任务 000004.SZ 完成\n",
       "任务 000006.SZ 完成\n",
+      "任务 000002.SZ 完成\n",
       "任务 000007.SZ 完成\n",
-      "任务 000008.SZ 完成\n",
       "任务 000009.SZ 完成\n",
+      "任务 000008.SZ 完成\n",
       "任务 000010.SZ 完成\n",
-      "任务 000012.SZ 完成\n",
       "任务 000011.SZ 完成\n",
+      "任务 000012.SZ 完成\n",
       "任务 000014.SZ 完成\n",
       "任务 000016.SZ 完成\n",
-      "任务 000019.SZ 完成\n",
       "任务 000017.SZ 完成\n",
+      "任务 000019.SZ 完成\n",
       "任务 000020.SZ 完成\n",
       "任务 000021.SZ 完成\n",
-      "任务 000025.SZ 完成\n",
       "任务 000026.SZ 完成\n",
-      "任务 000028.SZ 完成\n",
+      "任务 000025.SZ 完成\n",
       "任务 000027.SZ 完成\n",
-      "任务 000030.SZ 完成\n",
+      "任务 000028.SZ 完成\n",
       "任务 000029.SZ 完成\n",
+      "任务 000030.SZ 完成\n",
       "任务 000031.SZ 完成\n",
       "任务 000032.SZ 完成\n",
-      "任务 000035.SZ 完成\n",
       "任务 000034.SZ 完成\n",
+      "任务 000035.SZ 完成\n",
       "任务 000036.SZ 完成\n",
       "任务 000037.SZ 完成\n",
-      "任务 000039.SZ 完成\n",
       "任务 000040.SZ 完成\n",
-      "任务 000045.SZ 完成\n",
+      "任务 000039.SZ 完成\n",
       "任务 000042.SZ 完成\n",
-      "任务 000049.SZ 完成\n",
+      "任务 000045.SZ 完成\n",
       "任务 000048.SZ 完成\n",
-      "任务 000055.SZ 完成\n",
+      "任务 000049.SZ 完成\n",
       "任务 000050.SZ 完成\n",
+      "任务 000055.SZ 完成\n",
       "任务 000056.SZ 完成\n",
       "任务 000058.SZ 完成\n",
       "任务 000059.SZ 完成\n",
       "任务 000060.SZ 完成\n",
-      "任务 000062.SZ 完成\n",
       "任务 000061.SZ 完成\n",
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-      "任务 301130.SZ 完成\n",
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+      "任务 301130.SZ 完成\n",
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+      "任务 301159.SZ 完成\n",
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-      "任务 301211.SZ 完成\n",
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+      "任务 301211.SZ 完成\n",
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-      "任务 301217.SZ 完成\n",
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@@ -2768,12 +2768,14 @@
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+      "HTTPConnectionPool(host='api.waditu.com', port=80): Max retries exceeded with url: /dataapi/daily (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7114a862bf00>: Failed to establish a new connection: [Errno 101] Network is unreachable'))\n",
       "任务 301298.SZ 完成\n",
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+      "HTTPConnectionPool(host='api.waditu.com', port=80): Max retries exceeded with url: /dataapi/adj_factor (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7114a9066cf0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))\n",
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+      "任务 301355.SZ 完成\n",
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@@ -2821,13 +2823,13 @@
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-      "任务 301366.SZ 完成\n",
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+      "任务 301366.SZ 完成\n",
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+      "任务 301371.SZ 完成\n",
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-      "任务 301499.SZ 完成\n",
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+      "任务 301499.SZ 完成\n",
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-      "任务 301508.SZ 完成\n",
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       "任务 872895.BJ 完成\n",
+      "任务 872808.BJ 完成\n",
       "任务 872925.BJ 完成\n",
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       "任务 872953.BJ 完成\n",
-      "任务 873001.BJ 完成\n",
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+      "任务 873001.BJ 完成\n",
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@@ -5467,28 +5469,28 @@
       "任务 920002.BJ 完成\n",
       "任务 920008.BJ 完成\n",
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-      "任务 920060.BJ 完成\n",
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-      "任务 920082.BJ 完成\n",
+      "任务 920060.BJ 完成\n",
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+      "任务 920082.BJ 完成\n",
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-      "任务 920106.BJ 完成\n",
       "任务 920099.BJ 完成\n",
-      "任务 920111.BJ 完成\n",
+      "任务 920106.BJ 完成\n",
       "任务 920108.BJ 完成\n",
-      "任务 920118.BJ 完成\n",
+      "任务 920111.BJ 完成\n",
       "任务 920116.BJ 完成\n",
-      "任务 689009.SH 完成\n",
+      "任务 920118.BJ 完成\n",
       "任务 920128.BJ 完成\n",
+      "任务 689009.SH 完成\n",
       "任务 000003.SZ 完成\n",
       "任务 000005.SZ 完成\n",
       "任务 000013.SZ 完成\n",
       "任务 000015.SZ 完成\n",
       "任务 000018.SZ 完成\n",
       "任务 000023.SZ 完成\n",
-      "任务 000033.SZ 完成\n",
       "任务 000024.SZ 完成\n",
+      "任务 000033.SZ 完成\n",
       "任务 000038.SZ 完成\n",
       "任务 000046.SZ 完成\n",
       "任务 000047.SZ 完成\n",
@@ -5504,14 +5506,14 @@
       "任务 000511.SZ 完成\n",
       "任务 000515.SZ 完成\n",
       "任务 000522.SZ 完成\n",
-      "任务 000535.SZ 完成\n",
       "任务 000527.SZ 完成\n",
-      "任务 000542.SZ 完成\n",
+      "任务 000535.SZ 完成\n",
       "任务 000540.SZ 完成\n",
-      "任务 000556.SZ 完成\n",
       "任务 000549.SZ 完成\n",
-      "任务 000569.SZ 完成\n",
+      "任务 000542.SZ 完成\n",
+      "任务 000556.SZ 完成\n",
       "任务 000562.SZ 完成\n",
+      "任务 000569.SZ 完成\n",
       "任务 000578.SZ 完成\n",
       "任务 000583.SZ 完成\n",
       "任务 000585.SZ 完成\n",
@@ -5522,20 +5524,20 @@
       "任务 000606.SZ 完成\n",
       "任务 000611.SZ 完成\n",
       "任务 000613.SZ 完成\n",
-      "任务 000618.SZ 完成\n",
       "任务 000616.SZ 完成\n",
-      "任务 000653.SZ 完成\n",
+      "任务 000618.SZ 完成\n",
       "任务 000621.SZ 完成\n",
-      "任务 000660.SZ 完成\n",
+      "任务 000653.SZ 完成\n",
       "任务 000658.SZ 完成\n",
-      "任务 000666.SZ 完成\n",
+      "任务 000660.SZ 完成\n",
       "任务 000662.SZ 完成\n",
-      "任务 000671.SZ 完成\n",
+      "任务 000666.SZ 完成\n",
       "任务 000667.SZ 完成\n",
+      "任务 000671.SZ 完成\n",
       "任务 000673.SZ 完成\n",
       "任务 000675.SZ 完成\n",
-      "任务 000689.SZ 完成\n",
       "任务 000687.SZ 完成\n",
+      "任务 000689.SZ 完成\n",
       "任务 000693.SZ 完成\n",
       "任务 000699.SZ 完成\n",
       "任务 000730.SZ 完成\n",
@@ -5572,12 +5574,12 @@
       "任务 002070.SZ 完成\n",
       "任务 002071.SZ 完成\n",
       "任务 002087.SZ 完成\n",
-      "任务 002113.SZ 完成\n",
       "任务 002089.SZ 完成\n",
-      "任务 002143.SZ 完成\n",
+      "任务 002113.SZ 完成\n",
       "任务 002118.SZ 完成\n",
-      "任务 002220.SZ 完成\n",
+      "任务 002143.SZ 完成\n",
       "任务 002147.SZ 完成\n",
+      "任务 002220.SZ 完成\n",
       "任务 002260.SZ 完成\n",
       "任务 002280.SZ 完成\n",
       "任务 002288.SZ 完成\n",
@@ -5612,8 +5614,8 @@
       "任务 002711.SZ 完成\n",
       "任务 002740.SZ 完成\n",
       "任务 002751.SZ 完成\n",
-      "任务 002770.SZ 完成\n",
       "任务 002776.SZ 完成\n",
+      "任务 002770.SZ 完成\n",
       "任务 002781.SZ 完成\n",
       "任务 300023.SZ 完成\n",
       "任务 300028.SZ 完成\n",
@@ -5622,8 +5624,8 @@
       "任务 300089.SZ 完成\n",
       "任务 300090.SZ 完成\n",
       "任务 300104.SZ 完成\n",
-      "任务 300116.SZ 完成\n",
       "任务 300156.SZ 完成\n",
+      "任务 300116.SZ 完成\n",
       "任务 300178.SZ 完成\n",
       "任务 300186.SZ 完成\n",
       "任务 300202.SZ 完成\n",
@@ -5657,8 +5659,8 @@
       "任务 600074.SH 完成\n",
       "任务 600077.SH 完成\n",
       "任务 600086.SH 完成\n",
-      "任务 600087.SH 完成\n",
       "任务 600090.SH 完成\n",
+      "任务 600087.SH 完成\n",
       "任务 600091.SH 完成\n",
       "任务 600092.SH 完成\n",
       "任务 600093.SH 完成\n",
@@ -5788,14 +5790,16 @@
     "\n",
     "\n",
     "def get_data(ts_code):\n",
+    "    start_time = time.time()\n",
     "    daily_data = ts.pro_bar(ts_code=ts_code, adj='hfq', start_date=start_date)\n",
+    "    time.sleep(0.5)\n",
     "    # 如果返回数据不为空，则添加一列标识股票代码\n",
     "    if daily_data is not None and not daily_data.empty:\n",
     "        return daily_data\n",
     "\n",
     "\n",
     "# 循环遍历每个股票代码并获取数据\n",
-    "with ThreadPoolExecutor(max_workers=2) as executor:\n",
+    "with ThreadPoolExecutor(max_workers=5) as executor:\n",
     "    future_to_date = {executor.submit(get_data, row['ts_code']): row['ts_code'] for idx, row in stocks_df.iterrows()}\n",
     "\n",
     "    for future in as_completed(future_to_date):\n",
@@ -5810,7 +5814,7 @@
     "    api_call_count += 1\n",
     "\n",
     "    # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n",
-    "    if api_call_count % 300 == 0:\n",
+    "    if api_call_count % 150 == 0:\n",
     "        elapsed = time.time() - batch_start_time\n",
     "        if elapsed < 60:\n",
     "            sleep_time = 60 - elapsed\n",
@@ -5836,32 +5840,32 @@
      "output_type": "stream",
      "text": [
       "         ts_code trade_date     open     high      low    close  pre_close  \\\n",
-      "0      000001.SZ   20250530  1465.68  1479.74  1461.85  1477.18    1464.41   \n",
-      "1      000001.SZ   20250529  1472.07  1475.91  1463.13  1464.41    1473.35   \n",
-      "2      000001.SZ   20250528  1469.52  1475.91  1461.85  1473.35    1468.24   \n",
-      "3      000001.SZ   20250527  1463.13  1474.63  1459.29  1468.24    1459.29   \n",
-      "4      000001.SZ   20250526  1461.85  1469.52  1456.74  1459.29    1464.41   \n",
+      "0      000001.SZ   20251010  1498.43  1514.24  1497.11  1506.33    1502.38   \n",
+      "1      000001.SZ   20251009  1493.16  1503.70  1485.25  1502.38    1494.47   \n",
+      "2      000001.SZ   20250930  1498.43  1498.43  1487.88  1494.47    1498.43   \n",
+      "3      000001.SZ   20250929  1502.38  1512.92  1485.25  1498.43    1502.38   \n",
+      "4      000004.SZ   20251010    47.96    49.82    47.51    49.82      47.47   \n",
       "...          ...        ...      ...      ...      ...      ...        ...   \n",
-      "26736  920128.BJ   20250530    42.50    42.95    38.13    38.13      44.88   \n",
-      "26737  920128.BJ   20250529    41.00    46.98    40.78    44.88      39.52   \n",
-      "26738  920128.BJ   20250528    38.90    42.98    38.50    39.52      39.30   \n",
-      "26739  920128.BJ   20250527    37.97    43.29    36.62    39.30      39.25   \n",
-      "26740  920128.BJ   20250526    35.55    40.00    34.80    39.25      34.99   \n",
+      "20428  920128.BJ   20250929    30.33    31.13    30.30    30.39      30.34   \n",
+      "20429  689009.SH   20251010    68.82    71.09    68.25    69.20      68.88   \n",
+      "20430  689009.SH   20251009    68.59    69.63    68.13    68.88      68.64   \n",
+      "20431  689009.SH   20250930    67.87    69.10    67.83    68.64      67.83   \n",
+      "20432  689009.SH   20250929    67.71    67.97    66.41    67.83      67.87   \n",
       "\n",
       "       change  pct_chg         vol       amount  \n",
-      "0       12.77     0.87  1130849.31  1303433.716  \n",
-      "1       -8.94    -0.61   919806.76  1056726.384  \n",
-      "2        5.11     0.35   658179.47   757571.319  \n",
-      "3        8.95     0.61   801915.14   921912.281  \n",
-      "4       -5.12    -0.35   699158.58   800495.685  \n",
+      "0        3.95     0.26  1087947.75  1244213.513  \n",
+      "1        7.91     0.53  1047469.06  1187061.683  \n",
+      "2       -3.96    -0.26   832479.47   942224.081  \n",
+      "3       -3.95    -0.26  1176012.43  1334456.974  \n",
+      "4        2.35     4.95   162581.95   196172.385  \n",
       "...       ...      ...         ...          ...  \n",
-      "26736   -6.75   -15.04    80798.97   324916.233  \n",
-      "26737    5.36    13.56   101158.14   448509.533  \n",
-      "26738    0.22     0.56    67022.73   273635.585  \n",
-      "26739    0.05     0.13    94793.54   378706.573  \n",
-      "26740    4.26    12.17    77292.59   289655.061  \n",
+      "20428    0.05     0.16     7821.19    23566.171  \n",
+      "20429    0.32     0.46   160757.82  1088426.960  \n",
+      "20430    0.24     0.35   126481.16   849770.776  \n",
+      "20431    0.81     1.19    99332.03   665007.961  \n",
+      "20432   -0.04    -0.06   107634.30   705412.972  \n",
       "\n",
-      "[26741 rows x 11 columns]\n"
+      "[20433 rows x 11 columns]\n"
      ]
     }
    ],
diff --git a/main/data/update/update_money_flow.ipynb b/main/data/update/update_money_flow.ipynb
index 2307f06..97ddb41 100644
--- a/main/data/update/update_money_flow.ipynb
+++ b/main/data/update/update_money_flow.ipynb
@@ -34,17 +34,17 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 8507431 entries, 0 to 25615\n",
+      "Index: 8964780 entries, 0 to 25739\n",
       "Data columns (total 2 columns):\n",
       " #   Column      Dtype \n",
       "---  ------      ----- \n",
       " 0   ts_code     object\n",
       " 1   trade_date  object\n",
       "dtypes: object(2)\n",
-      "memory usage: 194.7+ MB\n",
+      "memory usage: 205.2+ MB\n",
       "None\n",
-      "20250523\n",
-      "start_date: 20250526\n"
+      "20250926\n",
+      "start_date: 20250929\n"
      ]
     }
    ],
@@ -61,7 +61,7 @@
     "    max_date = df['trade_date'].max()\n",
     "\n",
     "print(max_date)\n",
-    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250720')\n",
+    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
     "trade_cal = trade_cal[trade_cal['is_open'] == 1]  # 只保留交易日\n",
     "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
     "start_date = min(trade_dates)\n",
@@ -84,45 +84,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "任务 20250717 完成\n",
-      "任务 20250718 完成\n",
-      "任务 20250715 完成\n",
-      "任务 20250716 完成\n",
-      "任务 20250714 完成\n",
-      "任务 20250711 完成\n",
-      "任务 20250710 完成\n",
-      "任务 20250709 完成\n",
-      "任务 20250708 完成\n",
-      "任务 20250707 完成\n",
-      "任务 20250704 完成\n",
-      "任务 20250703 完成\n",
-      "任务 20250702 完成\n",
-      "任务 20250701 完成\n",
-      "任务 20250630 完成\n",
-      "任务 20250627 完成\n",
-      "任务 20250626 完成\n",
-      "任务 20250625 完成\n",
-      "任务 20250624 完成\n",
-      "任务 20250623 完成\n",
-      "任务 20250620 完成\n",
-      "任务 20250619 完成\n",
-      "任务 20250618 完成\n",
-      "任务 20250617 完成\n",
-      "任务 20250616 完成\n",
-      "任务 20250613 完成\n",
-      "任务 20250612 完成\n",
-      "任务 20250611 完成\n",
-      "任务 20250610 完成\n",
-      "任务 20250609 完成\n",
-      "任务 20250606 完成\n",
-      "任务 20250605 完成\n",
-      "任务 20250604 完成\n",
-      "任务 20250603 完成\n",
-      "任务 20250530 完成\n",
-      "任务 20250529 完成\n",
-      "任务 20250528 完成\n",
-      "任务 20250527 完成\n",
-      "任务 20250526 完成\n"
+      "任务 20251020 完成\n",
+      "任务 20251017 完成\n",
+      "任务 20251016 完成\n",
+      "任务 20251015 完成\n",
+      "任务 20251014 完成\n",
+      "任务 20251013 完成\n",
+      "任务 20251009 完成\n",
+      "任务 20251010 完成\n",
+      "任务 20250929 完成\n",
+      "任务 20250930 完成\n"
      ]
     }
    ],
@@ -200,6 +171,89 @@
     "\n",
     "print(\"所有每日基础数据获取并保存完毕！\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e6f2a2fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         ts_code trade_date  buy_sm_vol  buy_sm_amount  sell_sm_vol  \\\n",
+      "0      603290.SH   20251009       45532       52028.67        42778   \n",
+      "1      600936.SH   20251009       42537        1545.21        42382   \n",
+      "2      300429.SZ   20251009       81914       11768.07        64063   \n",
+      "3      300879.SZ   20251009       15330        5366.90        11651   \n",
+      "4      300031.SZ   20251009       51381       12650.70        43869   \n",
+      "...          ...        ...         ...            ...          ...   \n",
+      "20574  688083.SH   20250930       13247       10094.95        11236   \n",
+      "20575  002939.SZ   20250930      372609       43083.12       232240   \n",
+      "20576  688303.SH   20250930       62478       18094.19        55086   \n",
+      "20577  300146.SZ   20250930       50078        5792.85        35214   \n",
+      "20578  688351.SH   20250930       15096        3333.84        14017   \n",
+      "\n",
+      "       sell_sm_amount  buy_md_vol  buy_md_amount  sell_md_vol  sell_md_amount  \\\n",
+      "0            48942.98       53824       61495.85        54076        61851.39   \n",
+      "1             1538.97       24175         878.06        31948         1160.07   \n",
+      "2             9211.49       88583       12730.36        88244        12682.05   \n",
+      "3             4089.33       15591        5464.12        17057         5976.94   \n",
+      "4            10822.65       56173       13836.60        49423        12190.63   \n",
+      "...               ...         ...            ...          ...             ...   \n",
+      "20574         8561.02       10482        7994.12         9858         7514.37   \n",
+      "20575        26867.01      279904       32371.96       324997        37595.57   \n",
+      "20576        15952.67       55867       16177.83        53776        15573.61   \n",
+      "20577         4076.10       46159        5337.00        39420         4560.91   \n",
+      "20578         3095.89        6482        1430.69         6675         1474.59   \n",
+      "\n",
+      "       buy_lg_vol  buy_lg_amount  sell_lg_vol  sell_lg_amount  buy_elg_vol  \\\n",
+      "0           36150       41253.53        36789        41932.43        10514   \n",
+      "1           11158         405.04         9212          334.60         5672   \n",
+      "2           64282        9239.06        72904        10475.38         8221   \n",
+      "3           10167        3562.24        12327         4313.59         3221   \n",
+      "4           40306        9938.01        41035        10103.23         6112   \n",
+      "...           ...            ...          ...             ...          ...   \n",
+      "20574        6674        5082.80         8224         6273.43         3329   \n",
+      "20575      204229       23631.31       285167        32986.98       132696   \n",
+      "20576       33304        9638.04        34809        10074.64         5032   \n",
+      "20577       47161        5454.07        36321         4202.88         8662   \n",
+      "20578        2513         555.48         3398          749.54            0   \n",
+      "\n",
+      "       buy_elg_amount  sell_elg_vol  sell_elg_amount  net_mf_vol  \\\n",
+      "0            12073.88         12377         14125.13       20027   \n",
+      "1              205.33             0             0.00      -21182   \n",
+      "2             1183.11         17790          2551.67        -840   \n",
+      "3             1133.90          3275          1147.29       -4996   \n",
+      "4             1507.28         19645          4816.08        1531   \n",
+      "...               ...           ...              ...         ...   \n",
+      "20574         2538.01          4413          3361.05        7612   \n",
+      "20575        15366.29        147033         17003.12       84949   \n",
+      "20576         1459.24         13010          3768.39       15188   \n",
+      "20577         1000.95         41105          4744.98      -16754   \n",
+      "20578            0.00             0             0.00        3406   \n",
+      "\n",
+      "       net_mf_amount  \n",
+      "0           22734.35  \n",
+      "1            -766.75  \n",
+      "2             -90.83  \n",
+      "3           -1741.72  \n",
+      "4             385.00  \n",
+      "...              ...  \n",
+      "20574        5816.07  \n",
+      "20575        9927.60  \n",
+      "20576        4417.72  \n",
+      "20577       -1928.39  \n",
+      "20578         752.20  \n",
+      "\n",
+      "[20579 rows x 20 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(all_daily_data_df)"
+   ]
   }
  ],
  "metadata": {
diff --git a/main/data/update/update_stk_limit.ipynb b/main/data/update/update_stk_limit.ipynb
index a0d279c..3ade4bf 100644
--- a/main/data/update/update_stk_limit.ipynb
+++ b/main/data/update/update_stk_limit.ipynb
@@ -34,23 +34,23 @@
      "output_type": "stream",
      "text": [
       "        ts_code trade_date\n",
-      "2365  300067.SZ   20250509\n",
-      "2364  300066.SZ   20250509\n",
-      "2363  300065.SZ   20250509\n",
-      "2374  300076.SZ   20250509\n",
-      "7113  920819.BJ   20250509\n",
+      "4872  600206.SH   20250926\n",
+      "4873  600207.SH   20250926\n",
+      "4874  600208.SH   20250926\n",
+      "4876  600211.SH   20250926\n",
+      "7280  920037.BJ   20250926\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 10457633 entries, 0 to 7113\n",
+      "Index: 11170571 entries, 0 to 36462\n",
       "Data columns (total 2 columns):\n",
       " #   Column      Dtype \n",
       "---  ------      ----- \n",
       " 0   ts_code     object\n",
       " 1   trade_date  object\n",
       "dtypes: object(2)\n",
-      "memory usage: 239.4+ MB\n",
+      "memory usage: 255.7+ MB\n",
       "None\n",
-      "20250509\n",
-      "20250512\n"
+      "20250926\n",
+      "20250929\n"
      ]
     }
    ],
@@ -68,7 +68,7 @@
     "    max_date = df['trade_date'].max()\n",
     "\n",
     "print(max_date)\n",
-    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250720')\n",
+    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
     "trade_cal = trade_cal[trade_cal['is_open'] == 1]  # 只保留交易日\n",
     "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
     "start_date = min(trade_dates)\n",
@@ -91,55 +91,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "任务 20250717 完成\n",
-      "任务 20250718 完成\n",
-      "任务 20250716 完成\n",
-      "任务 20250715 完成\n",
-      "任务 20250714 完成\n",
-      "任务 20250711 完成\n",
-      "任务 20250710 完成\n",
-      "任务 20250709 完成\n",
-      "任务 20250707 完成\n",
-      "任务 20250708 完成\n",
-      "任务 20250704 完成\n",
-      "任务 20250703 完成\n",
-      "任务 20250702 完成\n",
-      "任务 20250701 完成\n",
-      "任务 20250627 完成\n",
-      "任务 20250630 完成\n",
-      "任务 20250626 完成\n",
-      "任务 20250625 完成\n",
-      "任务 20250624 完成\n",
-      "任务 20250623 完成\n",
-      "任务 20250620 完成\n",
-      "任务 20250619 完成\n",
-      "任务 20250617 完成\n",
-      "任务 20250618 完成\n",
-      "任务 20250613 完成\n",
-      "任务 20250616 完成\n",
-      "任务 20250612 完成\n",
-      "任务 20250611 完成\n",
-      "任务 20250610 完成\n",
-      "任务 20250609 完成\n",
-      "任务 20250606 完成\n",
-      "任务 20250605 完成\n",
-      "任务 20250604 完成\n",
-      "任务 20250603 完成\n",
-      "任务 20250530 完成\n",
-      "任务 20250529 完成\n",
-      "任务 20250528 完成\n",
-      "任务 20250527 完成\n",
-      "任务 20250526 完成\n",
-      "任务 20250523 完成\n",
-      "任务 20250521 完成\n",
-      "任务 20250522 完成\n",
-      "任务 20250519 完成\n",
-      "任务 20250520 完成\n",
-      "任务 20250516 完成\n",
-      "任务 20250515 完成\n",
-      "任务 20250514 完成\n",
-      "任务 20250513 完成\n",
-      "任务 20250512 完成\n"
+      "任务 20251020 完成\n",
+      "任务 20251017 完成\n",
+      "任务 20251015 完成\n",
+      "任务 20251016 完成\n",
+      "任务 20251013 完成\n",
+      "任务 20251014 完成\n",
+      "任务 20251010 完成\n",
+      "任务 20251009 完成\n",
+      "任务 20250929 完成\n",
+      "任务 20250930 完成\n"
      ]
     }
    ],
@@ -191,201 +152,58 @@
      "output_type": "stream",
      "text": [
       "[     trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250530  000001.SZ     12.61       10.31\n",
-      "1      20250530  000002.SZ      7.37        6.03\n",
-      "2      20250530  000004.SZ     10.38        9.40\n",
-      "3      20250530  000006.SZ      7.69        6.29\n",
-      "4      20250530  000007.SZ      8.61        7.05\n",
+      "0      20251010  000001.SZ     12.54       10.26\n",
+      "1      20251010  000002.SZ      7.47        6.11\n",
+      "2      20251010  000004.SZ     12.26       11.10\n",
+      "3      20251010  000006.SZ     11.94        9.77\n",
+      "4      20251010  000007.SZ      8.12        6.64\n",
       "...         ...        ...       ...         ...\n",
-      "7136   20250530  920445.BJ     13.61        7.33\n",
-      "7137   20250530  920489.BJ     32.64       17.58\n",
-      "7138   20250530  920682.BJ     13.81        7.45\n",
-      "7139   20250530  920799.BJ     78.92       42.50\n",
-      "7140   20250530  920819.BJ      5.90        3.18\n",
+      "7309   20251010  920978.BJ     50.08       26.98\n",
+      "7310   20251010  920981.BJ     48.04       25.88\n",
+      "7311   20251010  920982.BJ    354.64      190.96\n",
+      "7312   20251010  920985.BJ     11.86        6.40\n",
+      "7313   20251010  920992.BJ     27.87       15.01\n",
       "\n",
-      "[7141 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250529  000001.SZ     12.68       10.38\n",
-      "1      20250529  000002.SZ      7.35        6.01\n",
-      "2      20250529  000004.SZ     10.44        9.44\n",
-      "3      20250529  000006.SZ      7.78        6.36\n",
-      "4      20250529  000007.SZ      8.48        6.94\n",
+      "[7314 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
+      "0      20251009  000001.SZ     12.47       10.21\n",
+      "1      20251009  000002.SZ      7.58        6.20\n",
+      "2      20251009  000004.SZ     11.68       10.56\n",
+      "3      20251009  000006.SZ     11.32        9.26\n",
+      "4      20251009  000007.SZ      8.02        6.56\n",
       "...         ...        ...       ...         ...\n",
-      "7132   20250529  920445.BJ     13.28        7.16\n",
-      "7133   20250529  920489.BJ     31.73       17.09\n",
-      "7134   20250529  920682.BJ     13.55        7.31\n",
-      "7135   20250529  920799.BJ     73.17       39.41\n",
-      "7136   20250529  920819.BJ      5.86        3.16\n",
+      "7306   20251009  920978.BJ     50.44       27.16\n",
+      "7307   20251009  920981.BJ     48.11       25.91\n",
+      "7308   20251009  920982.BJ    366.06      197.12\n",
+      "7309   20251009  920985.BJ     12.01        6.47\n",
+      "7310   20251009  920992.BJ     27.39       14.75\n",
       "\n",
-      "[7137 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250528  000001.SZ     12.64       10.34\n",
-      "1      20250528  000002.SZ      7.34        6.00\n",
-      "2      20250528  000004.SZ     10.52        9.52\n",
-      "3      20250528  000006.SZ      7.96        6.52\n",
-      "4      20250528  000007.SZ      8.51        6.97\n",
+      "[7311 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
+      "0      20250929  000001.SZ     12.54       10.26\n",
+      "1      20250929  000002.SZ      7.48        6.12\n",
+      "2      20250929  000004.SZ     11.00        9.96\n",
+      "3      20250929  000006.SZ     10.46        8.56\n",
+      "4      20250929  000007.SZ      7.63        6.25\n",
       "...         ...        ...       ...         ...\n",
-      "7130   20250528  920445.BJ     13.50        7.28\n",
-      "7131   20250528  920489.BJ     32.70       17.62\n",
-      "7132   20250528  920682.BJ     13.71        7.39\n",
-      "7133   20250528  920799.BJ     73.60       39.64\n",
-      "7134   20250528  920819.BJ      5.87        3.17\n",
+      "7302   20250929  920445.BJ     14.37        7.75\n",
+      "7303   20250929  920489.BJ     29.34       15.80\n",
+      "7304   20250929  920682.BJ     13.10        7.06\n",
+      "7305   20250929  920799.BJ     70.78       38.12\n",
+      "7306   20250929  920819.BJ      5.52        2.98\n",
       "\n",
-      "[7135 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250527  000001.SZ     12.56       10.28\n",
-      "1      20250527  000002.SZ      7.29        5.97\n",
-      "2      20250527  000004.SZ     10.02        9.06\n",
-      "3      20250527  000006.SZ      7.58        6.20\n",
-      "4      20250527  000007.SZ      8.37        6.85\n",
+      "[7307 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
+      "0      20250930  000001.SZ     12.51       10.23\n",
+      "1      20250930  000002.SZ      7.49        6.13\n",
+      "2      20250930  000004.SZ     11.12       10.06\n",
+      "3      20250930  000006.SZ     10.29        8.42\n",
+      "4      20250930  000007.SZ      7.92        6.48\n",
       "...         ...        ...       ...         ...\n",
-      "7128   20250527  920445.BJ     13.28        7.16\n",
-      "7129   20250527  920489.BJ     33.96       18.30\n",
-      "7130   20250527  920682.BJ     13.59        7.33\n",
-      "7131   20250527  920799.BJ     73.38       39.52\n",
-      "7132   20250527  920819.BJ      5.55        2.99\n",
+      "7305   20250930  920445.BJ     14.67        7.91\n",
+      "7306   20250930  920489.BJ     29.26       15.76\n",
+      "7307   20250930  920682.BJ     12.92        6.96\n",
+      "7308   20250930  920799.BJ     73.19       39.41\n",
+      "7309   20250930  920819.BJ      5.55        2.99\n",
       "\n",
-      "[7133 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250526  000001.SZ     12.61       10.31\n",
-      "1      20250526  000002.SZ      7.29        5.97\n",
-      "2      20250526  000004.SZ      9.54        8.64\n",
-      "3      20250526  000006.SZ      7.44        6.08\n",
-      "4      20250526  000007.SZ      8.66        7.08\n",
-      "...         ...        ...       ...         ...\n",
-      "7130   20250526  920445.BJ     12.88        6.94\n",
-      "7131   20250526  920489.BJ     31.96       17.22\n",
-      "7132   20250526  920682.BJ     12.77        6.89\n",
-      "7133   20250526  920799.BJ     72.35       38.97\n",
-      "7134   20250526  920819.BJ      5.48        2.96\n",
-      "\n",
-      "[7135 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250523  000001.SZ     12.71       10.40\n",
-      "1      20250523  000002.SZ      7.34        6.00\n",
-      "2      20250523  000004.SZ      9.87        8.93\n",
-      "3      20250523  000006.SZ      7.54        6.17\n",
-      "4      20250523  000007.SZ      8.80        7.20\n",
-      "...         ...        ...       ...         ...\n",
-      "7130   20250523  920445.BJ     13.01        7.01\n",
-      "7131   20250523  920489.BJ     30.58       16.48\n",
-      "7132   20250523  920682.BJ     12.83        6.91\n",
-      "7133   20250523  920799.BJ     74.10       39.90\n",
-      "7134   20250523  920819.BJ      5.56        3.00\n",
-      "\n",
-      "[7135 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250521  000001.SZ     12.53       10.25\n",
-      "1      20250521  000002.SZ      7.46        6.10\n",
-      "2      20250521  000004.SZ      9.47        8.57\n",
-      "3      20250521  000006.SZ      7.61        6.23\n",
-      "4      20250521  000007.SZ      8.28        6.78\n",
-      "...         ...        ...       ...         ...\n",
-      "7129   20250521  920445.BJ     14.02        7.56\n",
-      "7130   20250521  920489.BJ     32.89       17.71\n",
-      "7131   20250521  920682.BJ     13.83        7.45\n",
-      "7132   20250521  920799.BJ     77.87       41.93\n",
-      "7133   20250521  920819.BJ      5.95        3.21\n",
-      "\n",
-      "[7134 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250522  000001.SZ     12.63       10.33\n",
-      "1      20250522  000002.SZ      7.44        6.08\n",
-      "2      20250522  000004.SZ      9.94        9.00\n",
-      "3      20250522  000006.SZ      7.43        6.08\n",
-      "4      20250522  000007.SZ      8.43        6.89\n",
-      "...         ...        ...       ...         ...\n",
-      "7130   20250522  920445.BJ     13.68        7.38\n",
-      "7131   20250522  920489.BJ     32.95       17.75\n",
-      "7132   20250522  920682.BJ     13.41        7.23\n",
-      "7133   20250522  920799.BJ     77.42       41.70\n",
-      "7134   20250522  920819.BJ      5.81        3.13\n",
-      "\n",
-      "[7135 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250519  000001.SZ     12.52       10.24\n",
-      "1      20250519  000002.SZ      7.45        6.09\n",
-      "2      20250519  000004.SZ      8.68        7.86\n",
-      "3      20250519  000006.SZ      7.17        5.87\n",
-      "4      20250519  000007.SZ      8.05        6.59\n",
-      "...         ...        ...       ...         ...\n",
-      "7128   20250519  920445.BJ     13.96        7.52\n",
-      "7129   20250519  920489.BJ     30.29       16.31\n",
-      "7130   20250519  920682.BJ     13.35        7.19\n",
-      "7131   20250519  920799.BJ     77.87       41.93\n",
-      "7132   20250519  920819.BJ      5.91        3.19\n",
-      "\n",
-      "[7133 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250520  000001.SZ     12.51       10.23\n",
-      "1      20250520  000002.SZ      7.48        6.12\n",
-      "2      20250520  000004.SZ      9.02        8.16\n",
-      "3      20250520  000006.SZ      7.66        6.26\n",
-      "4      20250520  000007.SZ      8.18        6.70\n",
-      "...         ...        ...       ...         ...\n",
-      "7128   20250520  920445.BJ     13.97        7.53\n",
-      "7129   20250520  920489.BJ     31.75       17.11\n",
-      "7130   20250520  920682.BJ     13.23        7.13\n",
-      "7131   20250520  920799.BJ     77.83       41.91\n",
-      "7132   20250520  920819.BJ      5.86        3.16\n",
-      "\n",
-      "[7133 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250516  000001.SZ     12.53       10.25\n",
-      "1      20250516  000002.SZ      7.47        6.11\n",
-      "2      20250516  000004.SZ      9.14        8.27\n",
-      "3      20250516  000006.SZ      7.17        5.87\n",
-      "4      20250516  000007.SZ      8.03        6.57\n",
-      "...         ...        ...       ...         ...\n",
-      "7125   20250516  920445.BJ     14.80        7.98\n",
-      "7126   20250516  920489.BJ     30.31       16.33\n",
-      "7127   20250516  920682.BJ     13.71        7.39\n",
-      "7128   20250516  920799.BJ     78.03       42.03\n",
-      "7129   20250516  920819.BJ      5.74        3.10\n",
-      "\n",
-      "[7130 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250515  000001.SZ     12.57       10.29\n",
-      "1      20250515  000002.SZ      7.58        6.20\n",
-      "2      20250515  000004.SZ      8.90        8.06\n",
-      "3      20250515  000006.SZ      7.26        5.94\n",
-      "4      20250515  000007.SZ      8.01        6.55\n",
-      "...         ...        ...       ...         ...\n",
-      "7119   20250515  920445.BJ     14.80        7.98\n",
-      "7120   20250515  920489.BJ     31.12       16.76\n",
-      "7121   20250515  920682.BJ     16.96        9.14\n",
-      "7122   20250515  920799.BJ     82.13       44.23\n",
-      "7123   20250515  920819.BJ      5.59        3.01\n",
-      "\n",
-      "[7124 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250514  000001.SZ     12.42       10.16\n",
-      "1      20250514  000002.SZ      7.55        6.17\n",
-      "2      20250514  000004.SZ      8.96        8.10\n",
-      "3      20250514  000006.SZ      7.14        5.84\n",
-      "4      20250514  000007.SZ      8.02        6.56\n",
-      "...         ...        ...       ...         ...\n",
-      "7117   20250514  920445.BJ     14.04        7.56\n",
-      "7118   20250514  920489.BJ     31.42       16.92\n",
-      "7119   20250514  920682.BJ     17.23        9.29\n",
-      "7120   20250514  920799.BJ     78.22       42.12\n",
-      "7121   20250514  920819.BJ      5.59        3.01\n",
-      "\n",
-      "[7122 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250513  000001.SZ     12.28       10.04\n",
-      "1      20250513  000002.SZ      7.54        6.17\n",
-      "2      20250513  000004.SZ      8.53        7.71\n",
-      "3      20250513  000006.SZ      7.12        5.82\n",
-      "4      20250513  000007.SZ      7.82        6.40\n",
-      "...         ...        ...       ...         ...\n",
-      "7116   20250513  920445.BJ     13.36        7.20\n",
-      "7117   20250513  920489.BJ     31.07       16.73\n",
-      "7118   20250513  920682.BJ     16.73        9.01\n",
-      "7119   20250513  920799.BJ     80.47       43.33\n",
-      "7120   20250513  920819.BJ      5.60        3.02\n",
-      "\n",
-      "[7121 rows x 4 columns],      trade_date    ts_code  up_limit  down_limit\n",
-      "0      20250512  000001.SZ     12.27       10.04\n",
-      "1      20250512  000002.SZ      7.46        6.10\n",
-      "2      20250512  000004.SZ      8.12        7.34\n",
-      "3      20250512  000006.SZ      7.08        5.80\n",
-      "4      20250512  000007.SZ      7.81        6.39\n",
-      "...         ...        ...       ...         ...\n",
-      "7112   20250512  920445.BJ     13.19        7.11\n",
-      "7113   20250512  920489.BJ     30.55       16.45\n",
-      "7114   20250512  920682.BJ     16.34        8.80\n",
-      "7115   20250512  920799.BJ     78.13       42.07\n",
-      "7116   20250512  920819.BJ      5.57        3.01\n",
-      "\n",
-      "[7117 rows x 4 columns]]\n"
+      "[7310 rows x 4 columns]]\n"
      ]
     }
    ],
diff --git a/main/factor/operators.py b/main/factor/operators.py
deleted file mode 100644
index 9566075..0000000
--- a/main/factor/operators.py
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
-from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data
-
-
-import sys
-print(sys.path)
\ No newline at end of file
diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb
index d108428..bc593b0 100644
--- a/main/train/Classify2.ipynb
+++ b/main/train/Classify2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "79a7758178bafdd3",
    "metadata": {
     "ExecuteTime": {
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "4a481c60",
    "metadata": {},
    "outputs": [],
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -71,13 +71,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "daily data\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "daily data\n",
       "daily basic\n",
       "inner merge on ['ts_code', 'trade_date']\n",
       "stk limit\n",
@@ -87,7 +81,7 @@
       "cyq perf\n",
       "left merge on ['ts_code', 'trade_date']\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 8692146 entries, 0 to 8692145\n",
+      "RangeIndex: 8820754 entries, 0 to 8820753\n",
       "Data columns (total 33 columns):\n",
       " #   Column         Dtype         \n",
       "---  ------         -----         \n",
@@ -163,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -231,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -331,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -347,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -414,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -442,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -472,63 +466,6 @@
     "                            df=None)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "823a4a88",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ 成功从 Redis Hash 'concept_stocks_daily_lists_pickle' 读取 1794 条每日概念股票数据。\n"
-     ]
-    }
-   ],
-   "source": [
-    "import redis\n",
-    "import pickle\n",
-    "from datetime import date, datetime\n",
-    "\n",
-    "# --- 配置 Redis 连接 ---\n",
-    "REDIS_HOST = '140.143.91.66'\n",
-    "REDIS_PORT = 6389\n",
-    "REDIS_DB = 0\n",
-    "\n",
-    "# --- 定义 Redis 键名 ---\n",
-    "HASH_KEY = \"concept_stocks_daily_lists_pickle\" # 区分之前的 JSON 版本\n",
-    "MAX_DATE_KEY = \"concept_stocks_max_date_pickle\" # 区分之前的 JSON 版本\n",
-    "\n",
-    "concept_dict = {}\n",
-    "\n",
-    "# --- 连接 Redis ---\n",
-    "try:\n",
-    "    r = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, password='Redis520102')\n",
-    "\n",
-    "    all_data_from_redis = r.hgetall(HASH_KEY) # 返回的是字典，键是字节，值是字节\n",
-    "    \n",
-    "    if all_data_from_redis:\n",
-    "        for date_bytes, stocks_bytes in all_data_from_redis.items(): # 将变量名改为 date_bytes 更清晰\n",
-    "            try:\n",
-    "                # *** 修正点：将日期字节解码为字符串 ***\n",
-    "                date_str = date_bytes.decode('utf-8') \n",
-    "                date_obj = datetime.strptime(date_str, '%Y%m%d').date()\n",
-    "                \n",
-    "                stocks_list = pickle.loads(stocks_bytes)\n",
-    "                concept_dict[date_obj] = stocks_list\n",
-    "            except (ValueError, pickle.UnpicklingError) as e:\n",
-    "                print(f\"⚠️ 警告: 解析 Redis 数据时出错 (日期键: '{date_bytes.decode('utf-8', errors='ignore')}')，跳过此条数据: {e}\") # 打印警告时也解码一下\n",
-    "        print(f\"✅ 成功从 Redis Hash '{HASH_KEY}' 读取 {len(concept_dict)} 条每日概念股票数据。\")\n",
-    "    else:\n",
-    "        print(f\"ℹ️ Redis Hash '{HASH_KEY}' 中没有找到任何数据。\")\n",
-    "\n",
-    "except redis.exceptions.ConnectionError as e:\n",
-    "    print(f\"❌ 错误: 无法连接到 Redis 服务器，请检查 Redis 是否正在运行或连接配置: {e}\")\n",
-    "except Exception as e:\n",
-    "    print(f\"❌ 从 Redis 读取数据时发生未知错误: {e}\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -544,39 +481,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4554725\n",
-      "开始生成概念相关因子...\n",
-      "开始计算概念内截面排序因子，基于: ['pct_chg', 'turnover_rate', 'volume_ratio']\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Ranking Features in Concepts: 100%|██████████| 3/3 [00:00<00:00, 15.21it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "概念相关因子生成完毕。\n",
-      "4554725\n",
-      "开始计算股东增减持因子...\n",
-      "警告: 'in_de' 列中存在未映射的值，可能导致 _direction 列出现NaN。\n",
-      "股东增减持因子计算完成。\n",
-      "Calculating cat_senti_mom_vol_spike...\n",
-      "Finished cat_senti_mom_vol_spike.\n",
-      "Calculating cat_senti_pre_breakout...\n",
-      "Calculating atr_10 as it's missing...\n",
-      "Calculating atr_40 as it's missing...\n",
-      "Finished cat_senti_pre_breakout.\n",
-      "计算因子 ts_turnover_rate_acceleration_5_20\n",
-      "计算因子 ts_vol_sustain_10_30\n",
-      "计算因子 cs_amount_outlier_10\n",
-      "计算因子 ts_ff_to_total_turnover_ratio\n",
-      "计算因子 ts_price_volume_trend_coherence_5_20\n",
-      "计算因子 ts_ff_turnover_rate_surge_10\n",
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
       "使用 'ann_date' 作为财务数据生效日期。\n",
       "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
       "使用 'ann_date' 作为财务数据生效日期。\n",
@@ -599,14 +507,29 @@
       "错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n",
       "错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n",
       "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n",
-      "       'amount', 'pct_chg', 'turnover_rate',\n",
-      "       ...\n",
+      "       'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n",
+      "       'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n",
+      "       'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n",
+      "       'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n",
+      "       'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n",
+      "       'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe',\n",
+      "       'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n",
+      "       'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20',\n",
+      "       'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol',\n",
+      "       'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n",
+      "       'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n",
+      "       'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n",
+      "       'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n",
+      "       'floating_chip_proxy', 'cost_support_15pct_change',\n",
+      "       'cat_winner_price_zone', 'flow_chip_consistency',\n",
+      "       'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n",
+      "       'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n",
+      "       '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n",
+      "       'return_skew', 'return_kurtosis', 'volume_change_rate',\n",
       "       'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n",
       "       'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n",
       "       'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n",
-      "      dtype='object', length=104)\n",
-      "Calculating senti_strong_inflow...\n",
-      "Finished senti_strong_inflow.\n",
+      "      dtype='object')\n",
       "Calculating lg_flow_mom_corr_20_60...\n",
       "Finished lg_flow_mom_corr_20_60.\n",
       "Calculating lg_flow_accel...\n",
@@ -681,88 +604,62 @@
       "Finished cs_rank_size.\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 4554725 entries, 0 to 4554724\n",
-      "Columns: 198 entries, ts_code to cs_rank_size\n",
-      "dtypes: bool(10), datetime64[ns](1), float64(176), int64(6), int8(1), object(4)\n",
-      "memory usage: 6.4+ GB\n",
+      "Columns: 181 entries, ts_code to cs_rank_size\n",
+      "dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(2)\n",
+      "memory usage: 5.8+ GB\n",
       "None\n",
-      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'cat_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'holder_direction_score_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
+      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
      ]
     }
    ],
    "source": [
+    "\n",
     "import numpy as np\n",
     "from main.factor.factor import *\n",
-    "from main.factor.money_factor import * \n",
-    "from main.factor.concept_factor import * \n",
+    "from main.factor.money_factor import *\n",
     "\n",
     "\n",
     "def filter_data(df):\n",
     "    # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
-    "    df = df[~df[\"is_st\"]]\n",
-    "    df = df[~df[\"ts_code\"].str.endswith(\"BJ\")]\n",
-    "    df = df[~df[\"ts_code\"].str.startswith(\"30\")]\n",
-    "    df = df[~df[\"ts_code\"].str.startswith(\"68\")]\n",
-    "    df = df[~df[\"ts_code\"].str.startswith(\"8\")]\n",
-    "    df = df[df[\"trade_date\"] >= \"2019-01-01\"]\n",
-    "    if \"in_date\" in df.columns:\n",
-    "        df = df.drop(columns=[\"in_date\"])\n",
+    "    df = df[df['trade_date'] <= '2025-06-01']\n",
+    "    df = df[~df['is_st']]\n",
+    "    df = df[~df['ts_code'].str.endswith('BJ')]\n",
+    "    df = df[~df['ts_code'].str.startswith('30')]\n",
+    "    df = df[~df['ts_code'].str.startswith('68')]\n",
+    "    df = df[~df['ts_code'].str.startswith('8')]\n",
+    "    df = df[df['trade_date'] >= '2019-01-01']\n",
+    "    if 'in_date' in df.columns:\n",
+    "        df = df.drop(columns=['in_date'])\n",
     "    df = df.reset_index(drop=True)\n",
     "    return df\n",
     "\n",
-    "\n",
     "gc.collect()\n",
     "\n",
     "df = filter_data(df)\n",
-    "df = df.sort_values(by=[\"ts_code\", \"trade_date\"])\n",
+    "df = df.sort_values(by=['ts_code', 'trade_date'])\n",
     "\n",
     "# df = price_minus_deduction_price(df, n=120)\n",
     "# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n",
     "# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n",
     "# df = cat_reason(df, top_list_df)\n",
     "# df = cat_is_on_top_list(df, top_list_df)\n",
-    "print(len(df))\n",
-    "df = generate_concept_factors(df, concept_dict)\n",
-    "print(len(df))\n",
     "\n",
-    "df = holder_trade_factors(df, stk_holdertrade_df)\n",
-    "\n",
-    "df = cat_senti_mom_vol_spike(\n",
-    "    df,\n",
-    "    return_period=3,\n",
-    "    return_threshold=0.03,  # 近3日涨幅超3%\n",
-    "    volume_ratio_threshold=1.3,\n",
-    "    current_pct_chg_min=0.0,  # 当日必须收红\n",
-    "    current_pct_chg_max=0.05,\n",
-    ")  # 当日涨幅不宜过大\n",
-    "\n",
-    "df = cat_senti_pre_breakout(\n",
-    "    df,\n",
-    "    atr_short_N=10,\n",
-    "    atr_long_M=40,\n",
-    "    vol_atrophy_N=10,\n",
-    "    vol_atrophy_M=40,\n",
-    "    price_stab_N=5,\n",
-    "    price_stab_threshold=0.06,\n",
-    "    current_pct_chg_min_signal=0.002,\n",
-    "    current_pct_chg_max_signal=0.05,\n",
-    "    volume_ratio_signal_threshold=1.1,\n",
-    ")\n",
-    "\n",
-    "df = ts_turnover_rate_acceleration_5_20(df)\n",
-    "df = ts_vol_sustain_10_30(df)\n",
+    "# df = ts_turnover_rate_acceleration_5_20(df)\n",
+    "# df = ts_vol_sustain_10_30(df)\n",
     "# df = cs_turnover_rate_relative_strength_20(df)\n",
-    "df = cs_amount_outlier_10(df)\n",
-    "df = ts_ff_to_total_turnover_ratio(df)\n",
-    "df = ts_price_volume_trend_coherence_5_20(df)\n",
-    "# df = ts_turnover_rate_trend_strength_5(df)\n",
-    "df = ts_ff_turnover_rate_surge_10(df)\n",
+    "# df = cs_amount_outlier_10(df)\n",
+    "# df = holder_trade_factors(stk_holdertrade_df, df)\n",
+    "\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roa')\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roe')\n",
     "\n",
-    "df = add_financial_factor(df, fina_indicator_df, factor_value_col=\"undist_profit_ps\")\n",
-    "df = add_financial_factor(df, fina_indicator_df, factor_value_col=\"ocfps\")\n",
     "calculate_arbr(df, N=26)\n",
-    "df[\"log_circ_mv\"] = np.log(df[\"circ_mv\"])\n",
+    "df['log_circ_mv'] = np.log(df['circ_mv'])\n",
     "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n",
     "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n",
+    "\n",
     "df = turnover_rate_n(df, n=5)\n",
     "df = variance_n(df, n=20)\n",
     "df = bbi_ratio_factor(df)\n",
@@ -771,10 +668,8 @@
     "df, _ = get_rolling_factor(df)\n",
     "df, _ = get_simple_factor(df)\n",
     "\n",
-    "df = calculate_strong_inflow_signal(df)\n",
-    "\n",
-    "df = df.rename(columns={\"l1_code\": \"cat_l1_code\"})\n",
-    "df = df.rename(columns={\"l2_code\": \"cat_l2_code\"})\n",
+    "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n",
+    "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n",
     "\n",
     "lg_flow_mom_corr(df, N=20, M=60)\n",
     "lg_flow_accel(df)\n",
@@ -795,7 +690,7 @@
     "\n",
     "cs_rank_net_lg_flow_val(df)\n",
     "cs_rank_flow_divergence(df)\n",
-    "cs_rank_industry_adj_lg_flow(df)  # Needs cat_l2_code\n",
+    "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n",
     "cs_rank_elg_buy_ratio(df)\n",
     "cs_rank_rel_profit_margin(df)\n",
     "cs_rank_cost_breadth(df)\n",
@@ -803,16 +698,16 @@
     "cs_rank_winner_rate(df)\n",
     "cs_rank_intraday_range(df)\n",
     "cs_rank_close_pos_in_range(df)\n",
-    "cs_rank_opening_gap(df)  # Needs pre_close\n",
-    "cs_rank_pos_in_hist_range(df)  # Needs his_low, his_high\n",
+    "cs_rank_opening_gap(df) # Needs pre_close\n",
+    "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n",
     "cs_rank_vol_x_profit_margin(df)\n",
     "cs_rank_lg_flow_price_concordance(df)\n",
     "cs_rank_turnover_per_winner(df)\n",
-    "cs_rank_ind_cap_neutral_pe(df)  # Placeholder - needs external libraries\n",
-    "cs_rank_volume_ratio(df)  # Needs volume_ratio\n",
+    "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n",
+    "cs_rank_volume_ratio(df) # Needs volume_ratio\n",
     "cs_rank_elg_buy_sell_sm_ratio(df)\n",
-    "cs_rank_cost_dist_vol_ratio(df)  # Needs volume_ratio\n",
-    "cs_rank_size(df)  # Needs circ_mv\n",
+    "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n",
+    "cs_rank_size(df) # Needs circ_mv\n",
     "\n",
     "\n",
     "# df = df.merge(index_data, on='trade_date', how='left')\n",
@@ -1449,7 +1344,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "209\n"
+      "191\n"
      ]
     }
    ],
@@ -1509,7 +1404,7 @@
       "0  000001.SZ 2019-01-02    16.574219\n",
       "1  000001.SZ 2019-01-03    16.583965\n",
       "2  000001.SZ 2019-01-04    16.633371\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "去除极值\n",
       "开始截面 MAD 去极值处理 (k=3.0)...\n"
      ]
@@ -1518,7 +1413,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 146/146 [00:19<00:00,  7.58it/s]\n"
+      "MAD Filtering: 100%|██████████| 131/131 [00:15<00:00,  8.53it/s]\n"
      ]
     },
     {
@@ -1533,7 +1428,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 146/146 [00:13<00:00, 10.96it/s]\n"
+      "MAD Filtering: 100%|██████████| 131/131 [00:11<00:00, 11.23it/s]\n"
      ]
     },
     {
@@ -1571,7 +1466,7 @@
      "output_type": "stream",
      "text": [
       "截面 MAD 去极值处理完成。\n",
-      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "df最小日期: 2019-01-02\n",
       "df最大日期: 2025-05-30\n",
       "2057465\n",
@@ -1580,7 +1475,6 @@
       "1781706\n",
       "test_data最小日期: 2023-01-03\n",
       "test_data最大日期: 2025-05-30\n",
-      "cat_columns: ['cat_hot_concept_stock', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'cat_winner_price_zone', 'cat_is_positive', 'cat_volume_breakout', 'cat_turnover_spike', 'cat_volume_ratio_breakout', 'cat_vol_spike', 'cat_af2', 'cat_af3', 'cat_af4', 'cat_vol_break', 'cat_golden_resonance', 'cat_up_limit']\n",
       "     ts_code trade_date  log_circ_mv\n",
       "0  000001.SZ 2019-01-02    16.574219\n",
       "1  000001.SZ 2019-01-03    16.583965\n",
@@ -1707,7 +1601,6 @@
     "print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
     "\n",
     "cat_columns = [col for col in feature_columns if col.startswith('cat')]\n",
-    "print(f'cat_columns: {cat_columns}')\n",
     "for col in cat_columns:\n",
     "    train_data[col] = train_data[col].astype('category')\n",
     "    test_data[col] = test_data[col].astype('category')\n",
@@ -1717,7 +1610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 19,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1781,17 +1674,17 @@
     "        params = {\n",
     "            'loss_function': 'Logloss',  # 适用于二分类\n",
     "            'eval_metric': 'Logloss',  # 评估指标\n",
-    "            'iterations': 750,\n",
+    "            'iterations': 1500,\n",
     "            'learning_rate': 0.01,\n",
-    "            'depth': 8,  # 控制模型复杂度\n",
+    "            'depth': 10,  # 控制模型复杂度\n",
     "            'l2_leaf_reg': 50,  # L2 正则化\n",
     "            'verbose': 5000,\n",
-    "            # 'early_stopping_rounds': 300,\n",
+    "            'early_stopping_rounds': 300,\n",
     "            # 'od_type': 'Iter',  # Overfitting detector type\n",
     "            # 'od_wait': 300,     # Number of iterations to wait after the bes\n",
-    "            # 'one_hot_max_size': 100,\n",
+    "            'one_hot_max_size': 50,\n",
     "            'class_weights': [0.6, 1.2],\n",
-    "            # 'task_type': 'GPU',\n",
+    "            'task_type': 'GPU',\n",
     "            'has_time': True,\n",
     "            'random_seed': 7\n",
     "        }\n",
@@ -1858,7 +1751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 20,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1892,7 +1785,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "547eff15f2c842308d4306b1aa5e5705",
+       "model_id": "9de2338da1fc42ec952054f233070da7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1907,13 +1800,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:\tlearn: 0.6887420\ttest: 0.6896418\tbest: 0.6896418 (0)\ttotal: 18ms\tremaining: 13.5s\n",
-      "749:\tlearn: 0.3565319\ttest: 0.5068883\tbest: 0.5054505 (576)\ttotal: 12.3s\tremaining: 0us\n",
-      "\n",
-      "bestTest = 0.5054505212\n",
-      "bestIteration = 576\n",
-      "\n",
-      "Shrink model to first 577 iterations.\n"
+      "0:\tlearn: 0.6890148\ttest: 0.6905107\tbest: 0.6905107 (0)\ttotal: 92.5ms\tremaining: 2m 18s\n",
+      "bestTest = 0.5221693203\n",
+      "bestIteration = 874\n",
+      "Shrink model to first 875 iterations.\n"
      ]
     }
    ],
@@ -1935,7 +1825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 21,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -1973,7 +1863,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 22,
+   "id": "c1c40917",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "模型已保存到: /mnt/d/PyProject/NewStock/main/train/catboost_model/catboost_model_2025-06-01.cbm\n"
+     ]
+    }
+   ],
+   "source": [
+    "current_date = datetime.datetime.now()\n",
+    "\n",
+    "# 2. 格式化日期为字符串，例如 '2025-07-06'\n",
+    "# 你可以根据需要调整日期格式，例如 '%Y%m%d' 会得到 '20250706'\n",
+    "date_str = current_date.strftime('%Y-%m-%d')\n",
+    "\n",
+    "# 3. 构建包含日期的模型文件名\n",
+    "model_filename = f'/mnt/d/PyProject/NewStock/main/train/catboost_model/catboost_model_2025-06-01.cbm'\n",
+    "\n",
+    "model.save_model(model_filename)\n",
+    "print(f\"模型已保存到: {model_filename}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -1981,8 +1899,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "209\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "191\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "[]\n"
      ]
     }
@@ -1995,37 +1913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "31c4852d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# model_path = 'my_catboost_model.cbm' # 通常以 .cbm 为扩展名\n",
-    "# # model.save_model(model_path)\n",
-    "# # print(f\"模型已保存到: {model_path}\")\n",
-    "\n",
-    "# loaded_model = CatBoostClassifier() # 或 CatBoostRegressor 等，取决于你保存的模型类型\n",
-    "# loaded_model.load_model(model_path)\n",
-    "# print(f\"模型已从 {model_path} 加载成功。\")\n",
-    "\n",
-    "# if type == 'cat':\n",
-    "#     score_df['score'] = loaded_model.predict_proba(score_df[feature_columns])[:, 1]\n",
-    "# elif type == 'light':\n",
-    "#     score_df['score'] = loaded_model.predict(score_df[feature_columns])\n",
-    "# score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n",
-    "\n",
-    "# score_df = score_df.groupby('trade_date', group_keys=False).apply(\n",
-    "#     lambda x: x[x['score'] >= x['score'].quantile(0.90)] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n",
-    "# ).reset_index(drop=True) # drop=True 避免添加旧索引列\n",
-    "# # save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1, 'score')).reset_index()\n",
-    "# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index()\n",
-    "# save_df = save_df.sort_values(['trade_date', 'score'])\n",
-    "# save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 24,
    "id": "e53b209a",
    "metadata": {},
    "outputs": [
@@ -2058,7 +1946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 25,
    "id": "364e821a",
    "metadata": {},
    "outputs": [],
@@ -2142,7 +2030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 26,
    "id": "1f6e6336",
    "metadata": {},
    "outputs": [
@@ -2156,32 +2044,26 @@
       "1e+05-1e+05\n",
       "1e+05-2e+05\n",
       "2e+05-2e+05\n",
-      "2e+05-2e+05\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "2e+05-2e+05\n",
       "2e+05-2e+05\n",
       "2e+05-3e+05\n",
       "3e+05-3e+05\n",
       "二分类评估指标:\n",
-      "accuracy: 0.6597\n",
-      "precision: 0.4824\n",
-      "recall: 0.0535\n",
-      "f1: 0.0963\n",
-      "roc_auc: 0.6190\n",
-      "fpr: (array of length 7484)\n",
-      "tpr: (array of length 7484)\n",
-      "thresholds: (array of length 7484)\n",
-      "score_return_correlation: -0.0515\n",
-      "mv_roc_auc: {'6e+04-9e+04': np.float64(0.6170032241642627), '9e+04-1e+05': np.float64(0.5933723849372385), '1e+05-1e+05': np.float64(0.5784531090723751), '1e+05-2e+05': np.float64(0.5759209014471692), '2e+05-2e+05': np.float64(0.6110322483330337), '2e+05-3e+05': np.float64(0.6014283834583336), '3e+05-3e+05': np.float64(0.58325233281493)}\n"
+      "accuracy: 0.6449\n",
+      "precision: 0.4384\n",
+      "recall: 0.2532\n",
+      "f1: 0.3210\n",
+      "roc_auc: 0.6147\n",
+      "fpr: (array of length 7456)\n",
+      "tpr: (array of length 7456)\n",
+      "thresholds: (array of length 7456)\n",
+      "score_return_correlation: -0.0356\n",
+      "mv_roc_auc: {'6e+04-9e+04': np.float64(0.5291280148423005), '9e+04-1e+05': np.float64(0.5695028952947505), '1e+05-1e+05': np.float64(0.5623844792554237), '1e+05-2e+05': np.float64(0.5622699726201068), '2e+05-2e+05': np.float64(0.6035659704533877), '2e+05-3e+05': np.float64(0.6119956359669062), '3e+05-3e+05': np.float64(0.5959528412973004)}\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -2217,7 +2099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 27,
    "id": "7e9023cc",
    "metadata": {},
    "outputs": [],
@@ -2417,7 +2299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 28,
    "id": "a0000d75",
    "metadata": {},
    "outputs": [
@@ -2427,7 +2309,7 @@
      "text": [
       "开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n",
       "准备数据，处理 NaN 值...\n",
-      "原始数据 17430 行，移除 NaN 后剩余 17125 行用于分析。\n",
+      "原始数据 17430 行，移除 NaN 后剩余 17119 行用于分析。\n",
       "对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n",
       "按二维分箱分组计算 Spearman Rank IC...\n",
       "整理结果用于绘图...\n",
@@ -2663,7 +2545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 29,
    "id": "a436dba4",
    "metadata": {},
    "outputs": [
diff --git a/main/train/Classify2_load_model.ipynb b/main/train/Classify2_load_model.ipynb
new file mode 100644
index 0000000..131edef
--- /dev/null
+++ b/main/train/Classify2_load_model.ipynb
@@ -0,0 +1,1903 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "79a7758178bafdd3",
+   "metadata": {
+    "jupyter": {
+     "is_executing": true,
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/mnt/d/PyProject/NewStock\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "# %load_ext cudf.pandas\n",
+    "\n",
+    "import gc\n",
+    "import os\n",
+    "import sys\n",
+    "sys.path.append('/mnt/d/PyProject/NewStock/')\n",
+    "print(os.getcwd())\n",
+    "import pandas as pd\n",
+    "from main.factor.factor import get_rolling_factor, get_simple_factor\n",
+    "from main.utils.factor import read_industry_data\n",
+    "from main.utils.factor_processor import calculate_score\n",
+    "from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4a481c60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 设置使用核心\n",
+    "import os\n",
+    "os.environ[\"MODIN_CPUS\"] = \"4\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a79cafb06a7e0e43",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.637416500Z",
+     "start_time": "2025-04-03T12:46:06.998047Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "daily data\n",
+      "daily basic\n",
+      "inner merge on ['ts_code', 'trade_date']\n",
+      "stk limit\n",
+      "left merge on ['ts_code', 'trade_date']\n",
+      "money flow\n",
+      "left merge on ['ts_code', 'trade_date']\n",
+      "cyq perf\n",
+      "left merge on ['ts_code', 'trade_date']\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 9162612 entries, 0 to 9162611\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column         Dtype         \n",
+      "---  ------         -----         \n",
+      " 0   ts_code        object        \n",
+      " 1   trade_date     datetime64[ns]\n",
+      " 2   open           float64       \n",
+      " 3   close          float64       \n",
+      " 4   high           float64       \n",
+      " 5   low            float64       \n",
+      " 6   vol            float64       \n",
+      " 7   amount         float64       \n",
+      " 8   pct_chg        float64       \n",
+      " 9   turnover_rate  float64       \n",
+      " 10  pe_ttm         float64       \n",
+      " 11  circ_mv        float64       \n",
+      " 12  total_mv       float64       \n",
+      " 13  volume_ratio   float64       \n",
+      " 14  is_st          bool          \n",
+      " 15  up_limit       float64       \n",
+      " 16  down_limit     float64       \n",
+      " 17  buy_sm_vol     float64       \n",
+      " 18  sell_sm_vol    float64       \n",
+      " 19  buy_lg_vol     float64       \n",
+      " 20  sell_lg_vol    float64       \n",
+      " 21  buy_elg_vol    float64       \n",
+      " 22  sell_elg_vol   float64       \n",
+      " 23  net_mf_vol     float64       \n",
+      " 24  his_low        float64       \n",
+      " 25  his_high       float64       \n",
+      " 26  cost_5pct      float64       \n",
+      " 27  cost_15pct     float64       \n",
+      " 28  cost_50pct     float64       \n",
+      " 29  cost_85pct     float64       \n",
+      " 30  cost_95pct     float64       \n",
+      " 31  weight_avg     float64       \n",
+      " 32  winner_rate    float64       \n",
+      "dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
+      "memory usage: 2.2+ GB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from main.utils.utils import read_and_merge_h5_data\n",
+    "\n",
+    "print('daily data')\n",
+    "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_data.h5', key='daily_data',\n",
+    "                            columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg'],\n",
+    "                            df=None)\n",
+    "\n",
+    "print('daily basic')\n",
+    "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_basic.h5', key='daily_basic',\n",
+    "                            columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n",
+    "                                     'is_st'], df=df, join='inner')\n",
+    "\n",
+    "print('stk limit')\n",
+    "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_limit.h5', key='stk_limit',\n",
+    "                            columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n",
+    "                            df=df)\n",
+    "print('money flow')\n",
+    "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/money_flow.h5', key='money_flow',\n",
+    "                            columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n",
+    "                                     'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n",
+    "                            df=df)\n",
+    "print('cyq perf')\n",
+    "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cyq_perf.h5', key='cyq_perf',\n",
+    "                            columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
+    "                                     'cost_50pct',\n",
+    "                                     'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n",
+    "                            df=df)\n",
+    "print(df.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cac01788dac10678",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.666533500Z",
+     "start_time": "2025-04-03T12:47:00.488715Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "industry\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('industry')\n",
+    "industry_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/industry_data.h5', key='industry_data',\n",
+    "                                     columns=['ts_code', 'l2_code', 'in_date'],\n",
+    "                                     df=None, on=['ts_code'], join='left')\n",
+    "\n",
+    "\n",
+    "def merge_with_industry_data(df, industry_df):\n",
+    "    # 确保日期字段是 datetime 类型\n",
+    "    df['trade_date'] = pd.to_datetime(df['trade_date'])\n",
+    "    industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n",
+    "\n",
+    "    # 对 industry_df 按 ts_code 和 in_date 排序\n",
+    "    industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n",
+    "\n",
+    "    # 对原始 df 按 ts_code 和 trade_date 排序\n",
+    "    df_sorted = df.sort_values(['trade_date', 'ts_code'])\n",
+    "\n",
+    "    # 使用 merge_asof 进行向后合并\n",
+    "    merged = pd.merge_asof(\n",
+    "        df_sorted,\n",
+    "        industry_df_sorted,\n",
+    "        by='ts_code',  # 按 ts_code 分组\n",
+    "        left_on='trade_date',\n",
+    "        right_on='in_date',\n",
+    "        direction='backward'\n",
+    "    )\n",
+    "\n",
+    "    # 获取每个 ts_code 的最早 in_date 记录\n",
+    "    min_in_date_per_ts = (industry_df_sorted\n",
+    "    .groupby('ts_code')\n",
+    "    .first()\n",
+    "    .reset_index()[['ts_code', 'l2_code']])\n",
+    "\n",
+    "    # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n",
+    "    merged['l2_code'] = merged['l2_code'].fillna(\n",
+    "        merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n",
+    "    )\n",
+    "\n",
+    "    # 保留需要的列并重置索引\n",
+    "    result = merged.reset_index(drop=True)\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "# 使用示例\n",
+    "df = merge_with_industry_data(df, industry_df)\n",
+    "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c4e9e1d31da6dba6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.667529800Z",
+     "start_time": "2025-04-03T12:47:10.541247Z"
+    },
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from main.factor.factor import *\n",
+    "\n",
+    "def calculate_indicators(df):\n",
+    "    \"\"\"\n",
+    "    计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n",
+    "    \"\"\"\n",
+    "    df = df.sort_values('trade_date')\n",
+    "    df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n",
+    "    # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n",
+    "    delta = df['close'].diff()\n",
+    "    gain = delta.where(delta > 0, 0)\n",
+    "    loss = -delta.where(delta < 0, 0)\n",
+    "    avg_gain = gain.rolling(window=14).mean()\n",
+    "    avg_loss = loss.rolling(window=14).mean()\n",
+    "    rs = avg_gain / avg_loss\n",
+    "    df['RSI'] = 100 - (100 / (1 + rs))\n",
+    "\n",
+    "    # 计算MACD\n",
+    "    ema12 = df['close'].ewm(span=12, adjust=False).mean()\n",
+    "    ema26 = df['close'].ewm(span=26, adjust=False).mean()\n",
+    "    df['MACD'] = ema12 - ema26\n",
+    "    df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n",
+    "    df['MACD_hist'] = df['MACD'] - df['Signal_line']\n",
+    "\n",
+    "    # 4. 情绪因子1：市场上涨比例（Up Ratio）\n",
+    "    df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n",
+    "    df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean()  # 过去20天上涨比例\n",
+    "\n",
+    "    # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n",
+    "    df['volume_mean'] = df['vol'].rolling(window=20).mean()  # 过去20天的平均成交量\n",
+    "    df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100  # 成交量变化率\n",
+    "\n",
+    "    # 6. 情绪因子3：波动率（Volatility）\n",
+    "    df['volatility'] = df['daily_return'].rolling(window=20).std()  # 过去20天的日收益率标准差\n",
+    "\n",
+    "    # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n",
+    "    df['amount_mean'] = df['amount'].rolling(window=20).mean()  # 过去20天的平均成交额\n",
+    "    df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100  # 成交额变化率\n",
+    "\n",
+    "    # df = sentiment_panic_greed_index(df)\n",
+    "    # df = sentiment_market_breadth_proxy(df)\n",
+    "    # df = sentiment_reversal_indicator(df)\n",
+    "\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def generate_index_indicators(h5_filename):\n",
+    "    df = pd.read_hdf(h5_filename, key='index_data')\n",
+    "    df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n",
+    "    df = df.sort_values('trade_date')\n",
+    "\n",
+    "    # 计算每个ts_code的相关指标\n",
+    "    df_indicators = []\n",
+    "    for ts_code in df['ts_code'].unique():\n",
+    "        df_index = df[df['ts_code'] == ts_code].copy()\n",
+    "        df_index = calculate_indicators(df_index)\n",
+    "        df_indicators.append(df_index)\n",
+    "\n",
+    "    # 合并所有指数的结果\n",
+    "    df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n",
+    "\n",
+    "    # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n",
+    "    df_final = df_all_indicators.pivot_table(\n",
+    "        index='trade_date',\n",
+    "        columns='ts_code',\n",
+    "        values=['daily_return', \n",
+    "                'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n",
+    "                # 'sentiment_panic_greed_index',\n",
+    "                'up_ratio_20d', 'volume_change_rate', 'volatility',\n",
+    "                'amount_change_rate', 'amount_mean'],\n",
+    "        aggfunc='last'\n",
+    "    )\n",
+    "\n",
+    "    df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n",
+    "    df_final = df_final.reset_index()\n",
+    "\n",
+    "    return df_final\n",
+    "\n",
+    "\n",
+    "# 使用函数\n",
+    "h5_filename = '/mnt/d/PyProject/NewStock/data/index_data.h5'\n",
+    "index_data = generate_index_indicators(h5_filename)\n",
+    "index_data = index_data.dropna()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a735bc02ceb4d872",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.668526400Z",
+     "start_time": "2025-04-03T12:47:10.751831Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import talib\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "53f86ddc0677a6d7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.679056800Z",
+     "start_time": "2025-04-03T12:47:10.826179Z"
+    },
+    "jupyter": {
+     "source_hidden": true
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from main.utils.factor import get_act_factor\n",
+    "\n",
+    "\n",
+    "def read_industry_data(h5_filename):\n",
+    "    # 读取 H5 文件中所有的行业数据\n",
+    "    industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n",
+    "        'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n",
+    "    ])  # 假设 H5 文件的键是 'industry_data'\n",
+    "    industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n",
+    "    industry_data = industry_data.reindex()\n",
+    "    industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n",
+    "\n",
+    "    grouped = industry_data.groupby('ts_code', group_keys=False)\n",
+    "    industry_data['obv'] = grouped.apply(\n",
+    "        lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n",
+    "    )\n",
+    "    industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n",
+    "    industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n",
+    "\n",
+    "    industry_data = get_act_factor(industry_data, cat=False)\n",
+    "    industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n",
+    "\n",
+    "    # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n",
+    "    # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']  # 因子列\n",
+    "    # \n",
+    "    # for factor in factor_columns:\n",
+    "    #     if factor in industry_data.columns:\n",
+    "    #         # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n",
+    "    #         industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n",
+    "    #             lambda x: x - x.mean())\n",
+    "\n",
+    "    industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n",
+    "        lambda x: x.rank(pct=True))\n",
+    "    industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n",
+    "        lambda x: x.rank(pct=True))\n",
+    "\n",
+    "    # cs_rank_intraday_range(industry_data)\n",
+    "    # cs_rank_close_pos_in_range(industry_data)\n",
+    "\n",
+    "    industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n",
+    "\n",
+    "    industry_data = industry_data.rename(\n",
+    "        columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n",
+    "\n",
+    "    industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n",
+    "    return industry_data\n",
+    "\n",
+    "\n",
+    "industry_df = read_industry_data('/mnt/d/PyProject/NewStock/data/sw_daily.h5')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dbe2fd8021b9417f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.681045300Z",
+     "start_time": "2025-04-03T12:47:15.963327Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ts_code', 'open', 'close', 'high', 'low', 'amount', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n"
+     ]
+    }
+   ],
+   "source": [
+    "origin_columns = df.columns.tolist()\n",
+    "origin_columns = [col for col in origin_columns if\n",
+    "                  col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n",
+    "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n",
+    "origin_columns = [col for col in origin_columns if 'cyq' not in col]\n",
+    "print(origin_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "85c3e3d0235ffffa",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.682043900Z",
+     "start_time": "2025-04-03T12:47:15.990101Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fina_indicator_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/fina_indicator.h5', key='fina_indicator',\n",
+    "                            columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps', 'roa', 'roe'],\n",
+    "                            df=None)\n",
+    "cashflow_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cashflow.h5', key='cashflow',\n",
+    "                            columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n",
+    "                            df=None)\n",
+    "balancesheet_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/balancesheet.h5', key='balancesheet',\n",
+    "                            columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n",
+    "                            df=None)\n",
+    "top_list_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/top_list.h5', key='top_list',\n",
+    "                            columns=['ts_code', 'trade_date', 'reason'],\n",
+    "                            df=None)\n",
+    "\n",
+    "top_list_df = top_list_df.sort_values(by='trade_date', ascending=False).drop_duplicates(subset=['ts_code', 'trade_date'], keep='first').sort_values(by='trade_date')\n",
+    "\n",
+    "stk_holdertrade_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_holdertrade.h5', key='stk_holdertrade',\n",
+    "                            columns=['ts_code', 'ann_date', 'in_de', 'change_ratio', 'after_ratio'],\n",
+    "                            df=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "92d84ce15a562ec6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.683047Z",
+     "start_time": "2025-04-03T12:47:16.121802Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
+      "开始计算因子: AR, BR (原地修改)...\n",
+      "因子 AR, BR 计算成功。\n",
+      "因子 AR, BR 计算流程结束。\n",
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "使用 'ann_date' 作为财务数据生效日期。\n",
+      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
+      "计算 BBI...\n",
+      "--- 计算日级别偏离度 (使用 pct_chg) ---\n",
+      "--- 计算日级别动量基准 (使用 pct_chg) ---\n",
+      "日级别动量基准计算完成 (使用 pct_chg)。\n",
+      "日级别偏离度计算完成 (使用 pct_chg)。\n",
+      "--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n",
+      "--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n",
+      "错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n",
+      "错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n",
+      "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n",
+      "       'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n",
+      "       'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n",
+      "       'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n",
+      "       'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n",
+      "       'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n",
+      "       'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe',\n",
+      "       'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n",
+      "       'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20',\n",
+      "       'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol',\n",
+      "       'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n",
+      "       'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n",
+      "       'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n",
+      "       'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n",
+      "       'floating_chip_proxy', 'cost_support_15pct_change',\n",
+      "       'cat_winner_price_zone', 'flow_chip_consistency',\n",
+      "       'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n",
+      "       'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n",
+      "       '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n",
+      "       'return_skew', 'return_kurtosis', 'volume_change_rate',\n",
+      "       'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n",
+      "       'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n",
+      "       'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n",
+      "      dtype='object')\n",
+      "Calculating lg_flow_mom_corr_20_60...\n",
+      "Finished lg_flow_mom_corr_20_60.\n",
+      "Calculating lg_flow_accel...\n",
+      "Finished lg_flow_accel.\n",
+      "Calculating profit_pressure...\n",
+      "Finished profit_pressure.\n",
+      "Calculating underwater_resistance...\n",
+      "Finished underwater_resistance.\n",
+      "Calculating cost_conc_std_20...\n",
+      "Finished cost_conc_std_20.\n",
+      "Calculating profit_decay_20...\n",
+      "Finished profit_decay_20.\n",
+      "Calculating vol_amp_loss_20...\n",
+      "Finished vol_amp_loss_20.\n",
+      "Calculating vol_drop_profit_cnt_5...\n",
+      "Finished vol_drop_profit_cnt_5.\n",
+      "Calculating lg_flow_vol_interact_20...\n",
+      "Finished lg_flow_vol_interact_20.\n",
+      "Calculating cost_break_confirm_cnt_5...\n",
+      "Finished cost_break_confirm_cnt_5.\n",
+      "Calculating atr_norm_channel_pos_14...\n",
+      "Finished atr_norm_channel_pos_14.\n",
+      "Calculating turnover_diff_skew_20...\n",
+      "Finished turnover_diff_skew_20.\n",
+      "Calculating lg_sm_flow_diverge_20...\n",
+      "Finished lg_sm_flow_diverge_20.\n",
+      "Calculating pullback_strong_20_20...\n",
+      "Finished pullback_strong_20_20.\n",
+      "Calculating vol_wgt_hist_pos_20...\n",
+      "Finished vol_wgt_hist_pos_20.\n",
+      "Calculating vol_adj_roc_20...\n",
+      "Finished vol_adj_roc_20.\n",
+      "Calculating cs_rank_net_lg_flow_val...\n",
+      "Finished cs_rank_net_lg_flow_val.\n",
+      "Calculating cs_rank_flow_divergence...\n",
+      "Finished cs_rank_flow_divergence.\n",
+      "Calculating cs_rank_ind_adj_lg_flow...\n",
+      "Finished cs_rank_ind_adj_lg_flow.\n",
+      "Calculating cs_rank_elg_buy_ratio...\n",
+      "Finished cs_rank_elg_buy_ratio.\n",
+      "Calculating cs_rank_rel_profit_margin...\n",
+      "Finished cs_rank_rel_profit_margin.\n",
+      "Calculating cs_rank_cost_breadth...\n",
+      "Finished cs_rank_cost_breadth.\n",
+      "Calculating cs_rank_dist_to_upper_cost...\n",
+      "Finished cs_rank_dist_to_upper_cost.\n",
+      "Calculating cs_rank_winner_rate...\n",
+      "Finished cs_rank_winner_rate.\n",
+      "Calculating cs_rank_intraday_range...\n",
+      "Finished cs_rank_intraday_range.\n",
+      "Calculating cs_rank_close_pos_in_range...\n",
+      "Finished cs_rank_close_pos_in_range.\n",
+      "Calculating cs_rank_opening_gap...\n",
+      "Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n",
+      "Calculating cs_rank_pos_in_hist_range...\n",
+      "Finished cs_rank_pos_in_hist_range.\n",
+      "Calculating cs_rank_vol_x_profit_margin...\n",
+      "Finished cs_rank_vol_x_profit_margin.\n",
+      "Calculating cs_rank_lg_flow_price_concordance...\n",
+      "Finished cs_rank_lg_flow_price_concordance.\n",
+      "Calculating cs_rank_turnover_per_winner...\n",
+      "Finished cs_rank_turnover_per_winner.\n",
+      "Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n",
+      "Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n",
+      "Calculating cs_rank_volume_ratio...\n",
+      "Finished cs_rank_volume_ratio.\n",
+      "Calculating cs_rank_elg_buy_sell_sm_ratio...\n",
+      "Finished cs_rank_elg_buy_sell_sm_ratio.\n",
+      "Calculating cs_rank_cost_dist_vol_ratio...\n",
+      "Finished cs_rank_cost_dist_vol_ratio.\n",
+      "Calculating cs_rank_size...\n",
+      "Finished cs_rank_size.\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4819708 entries, 0 to 4819707\n",
+      "Columns: 181 entries, ts_code to cs_rank_size\n",
+      "dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(2)\n",
+      "memory usage: 6.2+ GB\n",
+      "None\n",
+      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import numpy as np\n",
+    "from main.factor.factor import *\n",
+    "from main.factor.money_factor import *\n",
+    "\n",
+    "\n",
+    "def filter_data(df):\n",
+    "    # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
+    "    df = df[~df['is_st']]\n",
+    "    df = df[~df['ts_code'].str.endswith('BJ')]\n",
+    "    df = df[~df['ts_code'].str.startswith('30')]\n",
+    "    df = df[~df['ts_code'].str.startswith('68')]\n",
+    "    df = df[~df['ts_code'].str.startswith('8')]\n",
+    "    df = df[df['trade_date'] >= '2019-01-01']\n",
+    "    if 'in_date' in df.columns:\n",
+    "        df = df.drop(columns=['in_date'])\n",
+    "    df = df.reset_index(drop=True)\n",
+    "    return df\n",
+    "\n",
+    "gc.collect()\n",
+    "\n",
+    "df = filter_data(df)\n",
+    "df = df.sort_values(by=['ts_code', 'trade_date'])\n",
+    "\n",
+    "# df = price_minus_deduction_price(df, n=120)\n",
+    "# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n",
+    "# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n",
+    "# df = cat_reason(df, top_list_df)\n",
+    "# df = cat_is_on_top_list(df, top_list_df)\n",
+    "\n",
+    "# df = ts_turnover_rate_acceleration_5_20(df)\n",
+    "# df = ts_vol_sustain_10_30(df)\n",
+    "# df = cs_turnover_rate_relative_strength_20(df)\n",
+    "# df = cs_amount_outlier_10(df)\n",
+    "# df = holder_trade_factors(stk_holdertrade_df, df)\n",
+    "\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roa')\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roe')\n",
+    "\n",
+    "calculate_arbr(df, N=26)\n",
+    "df['log_circ_mv'] = np.log(df['circ_mv'])\n",
+    "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n",
+    "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n",
+    "\n",
+    "df = turnover_rate_n(df, n=5)\n",
+    "df = variance_n(df, n=20)\n",
+    "df = bbi_ratio_factor(df)\n",
+    "df = daily_deviation(df)\n",
+    "df = daily_industry_deviation(df)\n",
+    "df, _ = get_rolling_factor(df)\n",
+    "df, _ = get_simple_factor(df)\n",
+    "\n",
+    "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n",
+    "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n",
+    "\n",
+    "lg_flow_mom_corr(df, N=20, M=60)\n",
+    "lg_flow_accel(df)\n",
+    "profit_pressure(df)\n",
+    "underwater_resistance(df)\n",
+    "cost_conc_std(df, N=20)\n",
+    "profit_decay(df, N=20)\n",
+    "vol_amp_loss(df, N=20)\n",
+    "vol_drop_profit_cnt(df, N=20, M=5)\n",
+    "lg_flow_vol_interact(df, N=20)\n",
+    "cost_break_confirm_cnt(df, M=5)\n",
+    "atr_norm_channel_pos(df, N=14)\n",
+    "turnover_diff_skew(df, N=20)\n",
+    "lg_sm_flow_diverge(df, N=20)\n",
+    "pullback_strong(df, N=20, M=20)\n",
+    "vol_wgt_hist_pos(df, N=20)\n",
+    "vol_adj_roc(df, N=20)\n",
+    "\n",
+    "cs_rank_net_lg_flow_val(df)\n",
+    "cs_rank_flow_divergence(df)\n",
+    "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n",
+    "cs_rank_elg_buy_ratio(df)\n",
+    "cs_rank_rel_profit_margin(df)\n",
+    "cs_rank_cost_breadth(df)\n",
+    "cs_rank_dist_to_upper_cost(df)\n",
+    "cs_rank_winner_rate(df)\n",
+    "cs_rank_intraday_range(df)\n",
+    "cs_rank_close_pos_in_range(df)\n",
+    "cs_rank_opening_gap(df) # Needs pre_close\n",
+    "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n",
+    "cs_rank_vol_x_profit_margin(df)\n",
+    "cs_rank_lg_flow_price_concordance(df)\n",
+    "cs_rank_turnover_per_winner(df)\n",
+    "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n",
+    "cs_rank_volume_ratio(df) # Needs volume_ratio\n",
+    "cs_rank_elg_buy_sell_sm_ratio(df)\n",
+    "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n",
+    "cs_rank_size(df) # Needs circ_mv\n",
+    "\n",
+    "\n",
+    "# df = df.merge(index_data, on='trade_date', how='left')\n",
+    "\n",
+    "print(df.info())\n",
+    "print(df.columns.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f80b2f9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b87b938028afa206",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.683047Z",
+     "start_time": "2025-04-03T13:08:02.469611Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import ks_2samp, wasserstein_distance\n",
+    "\n",
+    "\n",
+    "def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1,\n",
+    "                            importance_threshold=0.05):\n",
+    "    dropped_features = []\n",
+    "\n",
+    "    # **统计数据漂移**\n",
+    "    numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n",
+    "    numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
+    "    for feature in numeric_columns:\n",
+    "        ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n",
+    "        wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n",
+    "\n",
+    "        if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n",
+    "            dropped_features.append(feature)\n",
+    "\n",
+    "    print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n",
+    "\n",
+    "    # **应用阈值进行最终筛选**\n",
+    "    filtered_features = [f for f in feature_columns if f not in dropped_features]\n",
+    "\n",
+    "    return filtered_features, dropped_features\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f4f16d63ad18d1bc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.684046Z",
+     "start_time": "2025-04-03T13:08:03.665739Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import statsmodels.api as sm # 用于中性化回归\n",
+    "from tqdm import tqdm # 可选，用于显示进度条\n",
+    "\n",
+    "# --- 常量 ---\n",
+    "epsilon = 1e-10 # 防止除零\n",
+    "\n",
+    "# --- 1. 中位数去极值 (MAD) ---\n",
+    "\n",
+    "def cs_mad_filter(df: pd.DataFrame,\n",
+    "                  features: list,\n",
+    "                  k: float = 3.0,\n",
+    "                  scale_factor: float = 1.4826):\n",
+    "    \"\"\"\n",
+    "    对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n",
+    "\n",
+    "    方法: 对每日截面数据，计算 median 和 MAD，\n",
+    "          将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n",
+    "          替换为边界值 (Winsorization)。\n",
+    "          scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n",
+    "\n",
+    "    Args:\n",
+    "        df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n",
+    "        features (list): 需要处理的特征列名列表。\n",
+    "        k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n",
+    "        scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n",
+    "\n",
+    "    WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
+    "    \"\"\"\n",
+    "    print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n",
+    "    if not all(col in df.columns for col in features):\n",
+    "        missing = [col for col in features if col not in df.columns]\n",
+    "        print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n",
+    "        return\n",
+    "\n",
+    "    grouped = df.groupby('trade_date')\n",
+    "\n",
+    "    for col in tqdm(features, desc=\"MAD Filtering\"):\n",
+    "        try:\n",
+    "            # 计算截面中位数\n",
+    "            median = grouped[col].transform('median')\n",
+    "            # 计算截面 MAD (Median Absolute Deviation from Median)\n",
+    "            mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n",
+    "\n",
+    "            # 计算上下边界\n",
+    "            lower_bound = median - k * scale_factor * mad\n",
+    "            upper_bound = median + k * scale_factor * mad\n",
+    "\n",
+    "            # 原地应用 clip\n",
+    "            df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
+    "\n",
+    "        except KeyError:\n",
+    "            print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n",
+    "\n",
+    "    print(\"截面 MAD 去极值处理完成。\")\n",
+    "\n",
+    "\n",
+    "# --- 2. 行业市值中性化 ---\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "def cs_neutralize_market_cap_numpy(df: pd.DataFrame,\n",
+    "                                   features: list,\n",
+    "                                   market_cap_col: str = 'circ_mv'):\n",
+    "    \"\"\"\n",
+    "    对 DataFrame 中的指定特征进行截面市值中性化 (NumPy 优化)。\n",
+    "\n",
+    "    Args:\n",
+    "        df (pd.DataFrame): 包含数据的 DataFrame，需要有 'trade_date' 和 market_cap_col 列。\n",
+    "        features (list): 需要进行市值中性化的特征列名列表。\n",
+    "        market_cap_col (str): 包含市值数据的列名，默认为 'circ_mv'。\n",
+    "    \"\"\"\n",
+    "    print(\"开始截面市值中性化 (NumPy 优化)...\")\n",
+    "    required_cols = features + ['trade_date', market_cap_col]\n",
+    "    if not all(col in df.columns for col in required_cols):\n",
+    "        missing = [col for col in required_cols if col not in df.columns]\n",
+    "        print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n",
+    "        return\n",
+    "\n",
+    "    df_copy = df\n",
+    "    log_cap_col = '_log_market_cap'\n",
+    "    df_copy[log_cap_col] = np.log1p(df_copy[market_cap_col])\n",
+    "\n",
+    "    # 创建一个 DataFrame 来存储所有日期的残差结果\n",
+    "    residuals_container = pd.DataFrame(index=df_copy.index, columns=features, dtype=float)\n",
+    "\n",
+    "    for date, group_df in tqdm(df_copy.groupby('trade_date'), desc=\"Neutralizing by Date (NumPy)\"):\n",
+    "        # 准备 X 矩阵 (自变量)：常数项和对数市值\n",
+    "        X_daily = np.concatenate([np.ones((len(group_df), 1)), group_df[[log_cap_col]].values], axis=1)\n",
+    "\n",
+    "        for feature_col in features:\n",
+    "            Y_daily = group_df[feature_col].values\n",
+    "\n",
+    "            # 处理 NaN：只对有效数据对进行回归\n",
+    "            valid_mask_y = ~np.isnan(Y_daily)\n",
+    "            valid_mask_x = ~np.isnan(X_daily).any(axis=1)\n",
+    "            valid_mask = valid_mask_y & valid_mask_x\n",
+    "\n",
+    "            current_feature_indices = group_df.index[valid_mask]\n",
+    "\n",
+    "            if np.sum(valid_mask) < X_daily.shape[1] + 1:\n",
+    "                # 有效数据不足，此特征在此日期保持 NaN\n",
+    "                continue\n",
+    "\n",
+    "            Y_valid = Y_daily[valid_mask]\n",
+    "            X_valid = X_daily[valid_mask, :]\n",
+    "\n",
+    "            try:\n",
+    "                # 使用 np.linalg.lstsq 进行 OLS 计算\n",
+    "                beta, sum_sq_resid, rank, s = np.linalg.lstsq(X_valid, Y_valid, rcond=None)\n",
+    "\n",
+    "                # 计算预测值 Y_hat = X_valid @ beta\n",
+    "                Y_hat_valid = X_valid @ beta\n",
+    "\n",
+    "                # 计算残差 residuals = Y_valid - Y_hat_valid\n",
+    "                residuals_valid = Y_valid - Y_hat_valid\n",
+    "\n",
+    "                # 将计算得到的残差放回 residuals_container\n",
+    "                residuals_container.loc[current_feature_indices, feature_col] = residuals_valid\n",
+    "\n",
+    "            except np.linalg.LinAlgError:\n",
+    "                pass\n",
+    "            except Exception as e:\n",
+    "                pass\n",
+    "\n",
+    "    # 将所有计算得到的残差更新回原始的 df (原地修改)\n",
+    "    for feature_col in features:\n",
+    "        df[feature_col] = residuals_container[feature_col]\n",
+    "\n",
+    "    # 清理临时列\n",
+    "    df.drop(columns=[log_cap_col], inplace=True, errors='ignore')\n",
+    "    print(\"截面市值中性化完成 (NumPy 优化)。\")\n",
+    "\n",
+    "# --- 3. Z-Score 标准化 ---\n",
+    "\n",
+    "def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n",
+    "    \"\"\"\n",
+    "    对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n",
+    "    方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n",
+    "\n",
+    "    Args:\n",
+    "        df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n",
+    "        features (list): 需要处理的特征列名列表。\n",
+    "        epsilon (float): 防止除以零的小常数。\n",
+    "\n",
+    "    WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
+    "    \"\"\"\n",
+    "    print(\"开始截面 Z-Score 标准化...\")\n",
+    "    if not all(col in df.columns for col in features):\n",
+    "        missing = [col for col in features if col not in df.columns]\n",
+    "        print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n",
+    "        return\n",
+    "\n",
+    "    grouped = df.groupby('trade_date')\n",
+    "\n",
+    "    for col in tqdm(features, desc=\"Standardizing\"):\n",
+    "        try:\n",
+    "            # 使用 transform 计算截面均值和标准差\n",
+    "            mean = grouped[col].transform('mean')\n",
+    "            std = grouped[col].transform('std')\n",
+    "\n",
+    "            # 计算 Z-Score 并原地赋值\n",
+    "            df[col] = (df[col] - mean) / (std + epsilon)\n",
+    "\n",
+    "        except KeyError:\n",
+    "            print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n",
+    "\n",
+    "    print(\"截面 Z-Score 标准化完成。\")\n",
+    "\n",
+    "def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n",
+    "\n",
+    "    参数:\n",
+    "    df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。\n",
+    "    feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n",
+    "\n",
+    "    返回:\n",
+    "    pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n",
+    "    \"\"\"\n",
+    "    processed_df = df.copy() # 在副本上操作，保留原始数据\n",
+    "\n",
+    "    # 确保 trade_date 是 datetime 类型以便正确分组\n",
+    "    processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n",
+    "\n",
+    "    def _fill_daily_nan(group):\n",
+    "        # group 是某一个交易日的 DataFrame\n",
+    "\n",
+    "        # 遍历指定的特征列\n",
+    "        for feature_col in feature_columns:\n",
+    "            # 检查列是否存在于当前分组中\n",
+    "            if feature_col in group.columns:\n",
+    "                # 计算当日该特征的中位数\n",
+    "                median_val = group[feature_col].median()\n",
+    "\n",
+    "                # 使用当日中位数填充该特征列的 NaN 值\n",
+    "                # inplace=True 会直接修改 group DataFrame\n",
+    "                group[feature_col].fillna(median_val, inplace=True)\n",
+    "            # else:\n",
+    "            #     print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n",
+    "\n",
+    "        return group\n",
+    "\n",
+    "    # 按交易日期分组，并应用每日填充函数\n",
+    "    # group_keys=False 避免将分组键添加到结果索引中\n",
+    "    filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n",
+    "\n",
+    "    return filled_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "40e6b68a91b30c79",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.685044Z",
+     "start_time": "2025-04-03T13:08:03.694904Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n",
+    "                                     log=True):\n",
+    "    if not (0 <= lower_percentile < upper_percentile <= 1):\n",
+    "        raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n",
+    "\n",
+    "    # Calculate lower and upper bounds based on percentiles\n",
+    "    lower_bound = label.quantile(lower_percentile)\n",
+    "    upper_bound = label.quantile(upper_percentile)\n",
+    "\n",
+    "    # Filter out values outside the bounds\n",
+    "    filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n",
+    "\n",
+    "    # Print the number of removed outliers\n",
+    "    if log:\n",
+    "        print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n",
+    "    return filtered_label\n",
+    "\n",
+    "\n",
+    "def calculate_risk_adjusted_target(df, days=5):\n",
+    "    df = df.sort_values(by=['ts_code', 'trade_date'])\n",
+    "\n",
+    "    df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n",
+    "    df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n",
+    "    df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n",
+    "\n",
+    "    df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n",
+    "        level=0, drop=True)\n",
+    "    sharpe_ratio = df['future_return'] * df['future_volatility']\n",
+    "    sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "\n",
+    "    return sharpe_ratio\n",
+    "\n",
+    "\n",
+    "def calculate_score(df, days=5, lambda_param=1.0):\n",
+    "    def calculate_max_drawdown(prices):\n",
+    "        peak = prices.iloc[0]  # 初始化峰值\n",
+    "        max_drawdown = 0  # 初始化最大回撤\n",
+    "\n",
+    "        for price in prices:\n",
+    "            if price > peak:\n",
+    "                peak = price  # 更新峰值\n",
+    "            else:\n",
+    "                drawdown = (peak - price) / peak  # 计算当前回撤\n",
+    "                max_drawdown = max(max_drawdown, drawdown)  # 更新最大回撤\n",
+    "\n",
+    "        return max_drawdown\n",
+    "\n",
+    "    def compute_stock_score(stock_df):\n",
+    "        stock_df = stock_df.sort_values(by=['trade_date'])\n",
+    "        future_return = stock_df['future_return']\n",
+    "        # 使用已有的 pct_chg 字段计算波动率\n",
+    "        volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n",
+    "        max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n",
+    "        score = future_return - lambda_param * max_drawdown\n",
+    "        return score\n",
+    "\n",
+    "    # # 确保 DataFrame 按照股票代码和交易日期排序\n",
+    "    # df = df.sort_values(by=['ts_code', 'trade_date'])\n",
+    "\n",
+    "    # 对每个股票分别计算 score\n",
+    "    df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n",
+    "\n",
+    "    return df['score']\n",
+    "\n",
+    "\n",
+    "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n",
+    "    numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n",
+    "    if not numeric_features:\n",
+    "        raise ValueError(\"No numeric features found in the provided data.\")\n",
+    "\n",
+    "    corr_matrix = df[numeric_features].corr().abs()\n",
+    "    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
+    "    to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
+    "    remaining_features = [col for col in feature_columns if col not in to_drop\n",
+    "                          or 'act' in col or 'af' in col]\n",
+    "    return remaining_features\n",
+    "\n",
+    "\n",
+    "def cross_sectional_standardization(df, features):\n",
+    "    df_sorted = df.sort_values(by='trade_date')  # 按时间排序\n",
+    "    df_standardized = df_sorted.copy()\n",
+    "\n",
+    "    for date in df_sorted['trade_date'].unique():\n",
+    "        # 获取当前时间点的数据\n",
+    "        current_data = df_standardized[df_standardized['trade_date'] == date]\n",
+    "\n",
+    "        # 只对指定特征进行标准化\n",
+    "        scaler = StandardScaler()\n",
+    "        standardized_values = scaler.fit_transform(current_data[features])\n",
+    "\n",
+    "        # 将标准化结果重新赋值回去\n",
+    "        df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n",
+    "\n",
+    "    return df_standardized\n",
+    "\n",
+    "\n",
+    "def neutralize_manual_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n",
+    "    对特征在行业内部进行市值中性化。\n",
+    "\n",
+    "    Args:\n",
+    "        df: 输入的 DataFrame，包含特征、行业分类和市值列。\n",
+    "        features: 需要进行中性化的特征列名列表。\n",
+    "        industry_col: 行业分类列的列名。\n",
+    "        mkt_cap_col: 市值列的列名。\n",
+    "\n",
+    "    Returns:\n",
+    "        中性化后的 DataFrame。\n",
+    "    \"\"\"\n",
+    "\n",
+    "    df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n",
+    "    df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n",
+    "    df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n",
+    "\n",
+    "    if df_cleaned.empty:\n",
+    "        print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n",
+    "        return df # 返回原始或空df，取决于清理前的状态\n",
+    "\n",
+    "    processed_df = df\n",
+    "\n",
+    "    for col in features:\n",
+    "        if col not in df_cleaned.columns:\n",
+    "            print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n",
+    "            # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（可能全是NaN）\n",
+    "            processed_df[col] = df[col] if col in df.columns else np.nan\n",
+    "            continue\n",
+    "\n",
+    "        # 跳过对控制变量本身进行中性化\n",
+    "        if col == mkt_cap_col or col == industry_col:\n",
+    "             print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n",
+    "             # 在结果 df 中也保持原样\n",
+    "             processed_df[col] = df[col] if col in df.columns else np.nan\n",
+    "             continue\n",
+    "\n",
+    "        residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n",
+    "\n",
+    "        # 在分组前处理特征列的 NaN，只对有因子值的行进行回归计算\n",
+    "        df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n",
+    "\n",
+    "        if not df_subset_factor.empty:\n",
+    "            for industry, group in df_subset_factor.groupby(industry_col):\n",
+    "                x = group[mkt_cap_col]  # 市值对数\n",
+    "                y = group[col]           # 因子值\n",
+    "\n",
+    "                # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n",
+    "                # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n",
+    "                if len(group) > 1 and np.var(x) > 1e-9:\n",
+    "                    try:\n",
+    "                        beta = np.cov(y, x)[0, 1] / np.var(x)\n",
+    "                        alpha = np.mean(y) - beta * np.mean(x)\n",
+    "\n",
+    "                        # 计算残差\n",
+    "                        resid = y - (alpha + beta * x)\n",
+    "\n",
+    "                        # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n",
+    "                        residual_series.loc[resid.index] = resid\n",
+    "\n",
+    "                    except Exception as e:\n",
+    "                         # 捕获可能的计算异常，例如np.cov或np.var因为极端数据报错\n",
+    "                         print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n",
+    "                         # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n",
+    "                         # 也可以选择保留原始值：residual_series.loc[group.index] = group[col]\n",
+    "\n",
+    "                else:\n",
+    "                    residual_series.loc[group.index] = group[col] # 保留原始因子值\n",
+    "            processed_df.loc[residual_series.index, col] = residual_series\n",
+    "\n",
+    "\n",
+    "        else:\n",
+    "             processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n",
+    "\n",
+    "    return processed_df\n",
+    "\n",
+    "\n",
+    "import gc\n",
+    "\n",
+    "gc.collect()\n",
+    "\n",
+    "\n",
+    "def mad_filter(df, features, n=3):\n",
+    "    for col in features:\n",
+    "        median = df[col].median()\n",
+    "        mad = np.median(np.abs(df[col] - median))\n",
+    "        upper = median + n * mad\n",
+    "        lower = median - n * mad\n",
+    "        df[col] = np.clip(df[col], lower, upper)  # 截断极值\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n",
+    "    for col in features:\n",
+    "        # 按日期分组计算上下百分位数\n",
+    "        lower_bound = df.groupby('trade_date')[col].transform(\n",
+    "            lambda x: x.quantile(lower_percentile)\n",
+    "        )\n",
+    "        upper_bound = df.groupby('trade_date')[col].transform(\n",
+    "            lambda x: x.quantile(upper_percentile)\n",
+    "        )\n",
+    "        # 截断超出范围的值\n",
+    "        df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "from scipy.stats import iqr\n",
+    "\n",
+    "\n",
+    "def iqr_filter(df, features):\n",
+    "    for col in features:\n",
+    "        df[col] = df.groupby('trade_date')[col].transform(\n",
+    "            lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n",
+    "        )\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n",
+    "    df = df.copy()\n",
+    "    for col in features:\n",
+    "        # 计算 rolling 统计量，需要按日期进行 groupby\n",
+    "        rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n",
+    "        rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n",
+    "\n",
+    "        # 对数据进行裁剪\n",
+    "        df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n",
+    "    \"\"\"\n",
+    "    计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n",
+    "\n",
+    "    Args:\n",
+    "        df: 包含特征列和目标列的 Pandas DataFrame。\n",
+    "        feature_columns: 包含所有待评估特征列名的列表。\n",
+    "        n: 希望选取的 RankIC 绝对值最高的特征数量。\n",
+    "        target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n",
+    "\n",
+    "    Returns:\n",
+    "        包含 RankIC 绝对值最高的 n 个特征列名的列表。\n",
+    "    \"\"\"\n",
+    "    numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
+    "    numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
+    "    if target_column not in df.columns:\n",
+    "        raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n",
+    "\n",
+    "    rankic_scores = {}\n",
+    "    for feature in numeric_columns:\n",
+    "        if feature not in df.columns:\n",
+    "            print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n",
+    "            continue\n",
+    "\n",
+    "        # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n",
+    "        # dropna() 是为了处理缺失值，确保相关性计算不失败\n",
+    "        valid_data = df[[feature, target_column]].dropna()\n",
+    "        if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n",
+    "             # 计算斯皮尔曼相关性\n",
+    "            correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n",
+    "            rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n",
+    "        else:\n",
+    "            rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n",
+    "\n",
+    "    # 将 RankIC 分数转换为 Series 便于排序\n",
+    "    rankic_series = pd.Series(rankic_scores)\n",
+    "\n",
+    "    # 按 RankIC 绝对值降序排序，选取前 n 个特征\n",
+    "    # handle case where n might be larger than available features\n",
+    "    n_actual = min(n, len(rankic_series))\n",
+    "    top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n",
+    "    top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n",
+    "    return top_features\n",
+    "\n",
+    "def create_deviation_within_dates(df, feature_columns):\n",
+    "    groupby_col = 'cat_l2_code'  # 使用 trade_date 进行分组\n",
+    "    new_columns = {}\n",
+    "    ret_feature_columns = feature_columns[:]\n",
+    "\n",
+    "    # 自动选择所有数值型特征\n",
+    "    num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n",
+    "\n",
+    "    # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n",
+    "    num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n",
+    "    num_features = [col for col in num_features if 'limit' not in col]\n",
+    "    num_features = [col for col in num_features if 'cyq' not in col]\n",
+    "\n",
+    "    # 遍历所有数值型特征\n",
+    "    for feature in num_features:\n",
+    "        if feature == 'trade_date':  # 不需要对 'trade_date' 计算偏差\n",
+    "            continue\n",
+    "\n",
+    "        # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n",
+    "        # deviation_col_name = f'deviation_mean_{feature}'\n",
+    "        # new_columns[deviation_col_name] = df[feature] - grouped_mean\n",
+    "        # ret_feature_columns.append(deviation_col_name)\n",
+    "\n",
+    "        grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n",
+    "        deviation_col_name = f'deviation_mean_{feature}'\n",
+    "        new_columns[deviation_col_name] = df[feature] - grouped_mean\n",
+    "        ret_feature_columns.append(deviation_col_name)\n",
+    "\n",
+    "    # 将新计算的偏差特征与原始 DataFrame 合并\n",
+    "    df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n",
+    "\n",
+    "    # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n",
+    "    #     df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n",
+    "\n",
+    "    return df, ret_feature_columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "47c12bb34062ae7a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.686049Z",
+     "start_time": "2025-04-03T14:49:25.889057Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "days = 5\n",
+    "validation_days = 120\n",
+    "\n",
+    "import gc\n",
+    "\n",
+    "gc.collect()\n",
+    "\n",
+    "df = df.sort_values(by=['ts_code', 'trade_date'])\n",
+    "df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n",
+    "# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n",
+    "#                       df.groupby('ts_code')['open'].shift(-1)\n",
+    "\n",
+    "df['cat_up_limit'] = df['pct_chg'] > 5\n",
+    "df['label'] = df.groupby('ts_code')['cat_up_limit'].rolling(window=5, min_periods=1).max().groupby('ts_code').shift(-5).fillna(0).astype(int).reset_index(level=0, drop=True)\n",
+    "\n",
+    "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n",
+    "\n",
+    "# for col in [col for col in df.columns]:\n",
+    "#     train_data[col] = train_data[col].astype('str')\n",
+    "#     test_data[col] = test_data[col].astype('str')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "29221dde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "191\n"
+     ]
+    }
+   ],
+   "source": [
+    "feature_columns = [col for col in df.head(10).merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left').merge(index_data, on='trade_date', how='left').columns]\n",
+    "feature_columns = [col for col in feature_columns if col not in ['trade_date',\n",
+    "                                                                 'ts_code',\n",
+    "                                                                 'label']]\n",
+    "feature_columns = [col for col in feature_columns if 'future' not in col]\n",
+    "feature_columns = [col for col in feature_columns if 'label' not in col]\n",
+    "feature_columns = [col for col in feature_columns if 'score' not in col]\n",
+    "feature_columns = [col for col in feature_columns if 'gen' not in col]\n",
+    "feature_columns = [col for col in feature_columns if 'is_st' not in col]\n",
+    "feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n",
+    "# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n",
+    "feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n",
+    "feature_columns = [col for col in feature_columns if 'code' not in col]\n",
+    "feature_columns = [col for col in feature_columns if col not in origin_columns]\n",
+    "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
+    "# feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n",
+    "feature_columns = [col for col in feature_columns if col not in ['intraday_lg_flow_corr_20', \n",
+    "                                                                 'cap_neutral_cost_metric', \n",
+    "                                                                 'hurst_net_mf_vol_60', \n",
+    "                                                                 'complex_factor_deap_1', \n",
+    "                                                                 'lg_buy_consolidation_20',\n",
+    "                                                                 'cs_rank_ind_cap_neutral_pe',\n",
+    "                                                                 'cs_rank_opening_gap',\n",
+    "                                                                 'cs_rank_ind_adj_lg_flow']]\n",
+    "feature_columns = [col for col in feature_columns if col not in ['roa', 'roe']]\n",
+    "print(len(feature_columns))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "03ee5daf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = fill_nan_with_daily_median(df, feature_columns)\n",
+    "for feature_col in [col for col in feature_columns if col in df.columns]:\n",
+    "    # median_val = df[feature_col].median()\n",
+    "    df[feature_col].fillna(0, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b76ea08a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     ts_code trade_date  log_circ_mv\n",
+      "0  000001.SZ 2019-01-02    16.574219\n",
+      "1  000001.SZ 2019-01-03    16.583965\n",
+      "2  000001.SZ 2019-01-04    16.633371\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "去除极值\n",
+      "开始截面 MAD 去极值处理 (k=3.0)...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MAD Filtering: 100%|██████████| 131/131 [00:14<00:00,  8.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "截面 MAD 去极值处理完成。\n",
+      "开始截面 MAD 去极值处理 (k=3.0)...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MAD Filtering:  82%|████████▏ | 107/131 [00:12<00:02,  9.41it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MAD Filtering: 100%|██████████| 131/131 [00:13<00:00,  9.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "截面 MAD 去极值处理完成。\n",
+      "开始截面 MAD 去极值处理 (k=3.0)...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MAD Filtering: 0it [00:00, ?it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "截面 MAD 去极值处理完成。\n",
+      "开始截面 MAD 去极值处理 (k=3.0)...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MAD Filtering: 0it [00:00, ?it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "截面 MAD 去极值处理完成。\n",
+      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "df最小日期: 2019-01-02\n",
+      "df最大日期: 2025-10-10\n",
+      "2056336\n",
+      "train_data最小日期: 2020-01-02\n",
+      "train_data最大日期: 2022-12-30\n",
+      "2045675\n",
+      "test_data最小日期: 2023-01-03\n",
+      "test_data最大日期: 2025-10-10\n",
+      "     ts_code trade_date  log_circ_mv\n",
+      "0  000001.SZ 2019-01-02    16.574219\n",
+      "1  000001.SZ 2019-01-03    16.583965\n",
+      "2  000001.SZ 2019-01-04    16.633371\n"
+     ]
+    }
+   ],
+   "source": [
+    "split_date = '2023-01-01'\n",
+    "train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')]\n",
+    "test_data = df[(df['trade_date'] >= split_date)]\n",
+    "\n",
+    "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n",
+    "\n",
+    "industry_df = industry_df.sort_values(by=['trade_date'])\n",
+    "index_data = index_data.sort_values(by=['trade_date'])\n",
+    "\n",
+    "# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
+    "# train_data = train_data.merge(index_data, on='trade_date', how='left')\n",
+    "# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
+    "# test_data = test_data.merge(index_data, on='trade_date', how='left')\n",
+    "\n",
+    "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n",
+    "\n",
+    "# feature_columns_new = feature_columns[:]\n",
+    "# train_data, _ = create_deviation_within_dates(train_data, [col for col in feature_columns if col in train_data.columns])\n",
+    "# test_data, _ = create_deviation_within_dates(test_data, [col for col in feature_columns if col in train_data.columns])\n",
+    "\n",
+    "# feature_columns = [\n",
+    "#        'undist_profit_ps', \n",
+    "#        'AR_BR',\n",
+    "#        'pe_ttm',\n",
+    "#        'alpha_22_improved', \n",
+    "#        'alpha_003', \n",
+    "#        'alpha_007', \n",
+    "#        'alpha_013', \n",
+    "#        'cat_up_limit', \n",
+    "#        'cat_down_limit', \n",
+    "#        'up_limit_count_10d', \n",
+    "#        'down_limit_count_10d', \n",
+    "#        'consecutive_up_limit', \n",
+    "#        'vol_break', \n",
+    "#        'weight_roc5', \n",
+    "#        'price_cost_divergence', \n",
+    "#        'smallcap_concentration', \n",
+    "#        'cost_stability', \n",
+    "#        'high_cost_break_days', \n",
+    "#        'liquidity_risk', \n",
+    "#        'turnover_std', \n",
+    "#        'mv_volatility', \n",
+    "#        'volume_growth', \n",
+    "#        'mv_growth', \n",
+    "#        'lg_flow_mom_corr_20_60', \n",
+    "#        'lg_flow_accel', \n",
+    "#        'profit_pressure', \n",
+    "#        'underwater_resistance', \n",
+    "#        'cost_conc_std_20', \n",
+    "#        'profit_decay_20', \n",
+    "#        'vol_amp_loss_20', \n",
+    "#        'vol_drop_profit_cnt_5', \n",
+    "#        'lg_flow_vol_interact_20', \n",
+    "#        'cost_break_confirm_cnt_5', \n",
+    "#        'atr_norm_channel_pos_14', \n",
+    "#        'turnover_diff_skew_20', \n",
+    "#        'lg_sm_flow_diverge_20', \n",
+    "#        'pullback_strong_20_20', \n",
+    "#        'vol_wgt_hist_pos_20', \n",
+    "#        'vol_adj_roc_20',\n",
+    "#        'cashflow_to_ev_factor',\n",
+    "#        'ocfps',\n",
+    "#        'book_to_price_ratio',\n",
+    "#        'turnover_rate_mean_5',\n",
+    "#        'variance_20',\n",
+    "#        'bbi_ratio_factor'\n",
+    "# ]\n",
+    "# feature_columns = [col for col in feature_columns if col in train_data.columns]\n",
+    "# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
+    "\n",
+    "numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
+    "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
+    "# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n",
+    "print(feature_columns)\n",
+    "\n",
+    "# train_data = fill_nan_with_daily_median(train_data, feature_columns)\n",
+    "# test_data = fill_nan_with_daily_median(test_data, feature_columns)\n",
+    "\n",
+    "train_data = train_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
+    "train_data = train_data.dropna(subset=['label'])\n",
+    "train_data = train_data.reset_index(drop=True)\n",
+    "# print(test_data.tail())\n",
+    "test_data = test_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
+    "# test_data = test_data.dropna(subset=['label'])\n",
+    "test_data = test_data.reset_index(drop=True)\n",
+    "\n",
+    "transform_feature_columns = feature_columns\n",
+    "transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
+    "# transform_feature_columns.remove('undist_profit_ps')\n",
+    "print('去除极值')\n",
+    "cs_mad_filter(train_data, transform_feature_columns)\n",
+    "# print('中性化')\n",
+    "# cs_neutralize_market_cap_numpy(train_data, transform_feature_columns)\n",
+    "# print('标准化')\n",
+    "# cs_zscore_standardize(train_data, transform_feature_columns)\n",
+    "\n",
+    "cs_mad_filter(test_data, transform_feature_columns)\n",
+    "# cs_neutralize_market_cap_numpy(test_data, transform_feature_columns)\n",
+    "# cs_zscore_standardize(test_data, transform_feature_columns)\n",
+    "\n",
+    "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
+    "cs_mad_filter(train_data, mad_filter_feature_columns)\n",
+    "cs_mad_filter(test_data, mad_filter_feature_columns)\n",
+    "\n",
+    "\n",
+    "print(f'feature_columns: {feature_columns}')\n",
+    "\n",
+    "\n",
+    "print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
+    "print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
+    "print(len(train_data))\n",
+    "print(f\"train_data最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
+    "print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
+    "print(len(test_data))\n",
+    "print(f\"test_data最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
+    "print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
+    "\n",
+    "cat_columns = [col for col in feature_columns if col.startswith('cat')]\n",
+    "for col in cat_columns:\n",
+    "    train_data[col] = train_data[col].astype('category')\n",
+    "    test_data[col] = test_data[col].astype('category')\n",
+    "\n",
+    "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3ff2d1c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "import matplotlib.pyplot as plt # 保持 matplotlib 导入，尽管LightGBM的绘图功能已移除\n",
+    "from sklearn.decomposition import PCA\n",
+    "import datetime # 用于日期计算\n",
+    "from catboost import CatBoostClassifier\n",
+    "from catboost import Pool\n",
+    "import lightgbm as lgb\n",
+    "\n",
+    "def train_model(train_data_df, feature_columns,\n",
+    "                                  print_info=True, # 调整参数名，更通用\n",
+    "                                  validation_days=180, use_pca=False, split_date=None,\n",
+    "                                  target_column='label', type='light'): # 增加目标列参数\n",
+    "\n",
+    "    print('train data size: ', len(train_data_df))\n",
+    "    print(train_data_df[['ts_code', 'trade_date', 'log_circ_mv']])\n",
+    "    # 确保数据按时间排序\n",
+    "    train_data_df = train_data_df.sort_values(by='trade_date')\n",
+    "\n",
+    "    # 识别数值型特征列\n",
+    "    numeric_feature_columns = train_data_df[feature_columns].select_dtypes(include=['float64', 'int64']).columns.tolist()\n",
+    "\n",
+    "    # 去除标签为空的样本\n",
+    "    initial_len = len(train_data_df)\n",
+    "    train_data_df = train_data_df.dropna(subset=[target_column])\n",
+    "\n",
+    "    if print_info:\n",
+    "        print(f'原始样本数: {initial_len}, 去除标签为空后样本数: {len(train_data_df)}')\n",
+    "\n",
+    "    # 提取特征和标签，只取数值型特征用于线性回归\n",
+    "        \n",
+    "    if split_date is None:\n",
+    "        all_dates = train_data_df['trade_date'].unique()  # 获取所有唯一的 trade_date\n",
+    "        split_date = all_dates[-validation_days]  # 划分点为倒数第 validation_days 天\n",
+    "    train_data_split = train_data_df[train_data_df['trade_date'] < split_date]  # 训练集\n",
+    "    val_data_split = train_data_df[train_data_df['trade_date'] >= split_date]  # 验证集\n",
+    "    \n",
+    "    X_train = train_data_split[feature_columns]\n",
+    "    y_train = train_data_split[target_column]\n",
+    "    \n",
+    "    X_val = val_data_split[feature_columns]\n",
+    "    y_val = val_data_split['label']\n",
+    "\n",
+    "\n",
+    "    # # 标准化数值特征 (使用 StandardScaler 对训练集fit并transform, 对验证集只transform)\n",
+    "    scaler = StandardScaler()\n",
+    "    # X_train = scaler.fit_transform(X_train)\n",
+    "\n",
+    "    # 训练线性回归模型\n",
+    "    # model = LogisticRegression(random_state=42)\n",
+    "    \n",
+    "    # # 使用处理后的特征和样本权重进行训练\n",
+    "    # model.fit(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "    if type == 'cat':\n",
+    "        params = {\n",
+    "            'loss_function': 'Logloss',  # 适用于二分类\n",
+    "            'eval_metric': 'Logloss',  # 评估指标\n",
+    "            'iterations': 1500,\n",
+    "            'learning_rate': 0.01,\n",
+    "            'depth': 10,  # 控制模型复杂度\n",
+    "            'l2_leaf_reg': 50,  # L2 正则化\n",
+    "            'verbose': 5000,\n",
+    "            'early_stopping_rounds': 300,\n",
+    "            # 'od_type': 'Iter',  # Overfitting detector type\n",
+    "            # 'od_wait': 300,     # Number of iterations to wait after the bes\n",
+    "            'one_hot_max_size': 50,\n",
+    "            'class_weights': [0.6, 1.2],\n",
+    "            'task_type': 'GPU',\n",
+    "            'has_time': True,\n",
+    "            'random_seed': 7\n",
+    "        }\n",
+    "        cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n",
+    "        train_pool = Pool(data=X_train, label=y_train, cat_features=cat_features)\n",
+    "        val_pool = Pool(data=X_val, label=y_val, cat_features=cat_features)\n",
+    "\n",
+    "\n",
+    "        model = CatBoostClassifier(**params)\n",
+    "        model.fit(train_pool,\n",
+    "                eval_set=val_pool, \n",
+    "                plot=True, \n",
+    "                use_best_model=True\n",
+    "                )\n",
+    "    elif type == 'light':\n",
+    "        params = {\n",
+    "            'objective': 'binary',\n",
+    "            'metric': 'average_precision',\n",
+    "            'learning_rate': 0.01,\n",
+    "            'is_unbalance': True,\n",
+    "            'num_leaves': 2048,\n",
+    "            'min_data_in_leaf': 1024,\n",
+    "            'max_depth': 32,\n",
+    "            'max_bin': 1024,\n",
+    "            'feature_fraction': 0.5,\n",
+    "            'bagging_fraction': 0.5,\n",
+    "            'bagging_freq': 1,\n",
+    "            'lambda_l1': 50,\n",
+    "            'lambda_l2': 50,\n",
+    "            'verbosity': -1,\n",
+    "            'num_threads' : 8\n",
+    "        }\n",
+    "        categorical_feature = [col for col in feature_columns if 'cat' in col]\n",
+    "        train_dataset = lgb.Dataset(\n",
+    "            X_train, label=y_train,\n",
+    "            categorical_feature=categorical_feature\n",
+    "        )\n",
+    "        val_dataset = lgb.Dataset(\n",
+    "            X_val, label=y_val,\n",
+    "            categorical_feature=categorical_feature\n",
+    "        )\n",
+    "\n",
+    "        evals = {}\n",
+    "        callbacks = [lgb.log_evaluation(period=1000),\n",
+    "                        lgb.callback.record_evaluation(evals),\n",
+    "                        lgb.early_stopping(100, first_metric_only=True)\n",
+    "                    ]\n",
+    "        # 训练模型\n",
+    "        model = lgb.train(\n",
+    "            params, train_dataset, num_boost_round=1000,\n",
+    "            valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n",
+    "            callbacks=callbacks\n",
+    "        )\n",
+    "\n",
+    "        # 打印特征重要性（如果需要）\n",
+    "        if True:\n",
+    "            lgb.plot_metric(evals)\n",
+    "            lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
+    "            plt.show()\n",
+    "\n",
+    "\n",
+    "    return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a5bbb8be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<catboost.core.CatBoostClassifier at 0x707ccc5ac1a0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type = 'cat'\n",
+    "\n",
+    "model_filename = f'/mnt/d/PyProject/NewStock/main/train/catboost_model/catboost_model_2025-07-06.cbm'\n",
+    "model = CatBoostClassifier()\n",
+    "model.load_model(model_filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5d1522a7538db91b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-07-26T16:59:40.692044800Z",
+     "start_time": "2025-04-03T15:04:39.298483Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(300, 'total_mv'))\n",
+    "# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n",
+    "# score_df = score_df[score_df['pe_ttm'] > 0]\n",
+    "score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
+    "score_df = score_df.merge(index_data, on='trade_date', how='left')\n",
+    "# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n",
+    "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n",
+    "numeric_columns = [col for col in feature_columns if col in numeric_columns]\n",
+    "# score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n",
+    "# score_df = cross_sectional_standardization(score_df, numeric_columns)\n",
+    "\n",
+    "if type == 'cat':\n",
+    "    score_df['score'] = model.predict_proba(score_df[feature_columns])[:, 1]\n",
+    "elif type == 'light':\n",
+    "    score_df['score'] = model.predict(score_df[feature_columns])\n",
+    "score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n",
+    "\n",
+    "score_df = score_df.groupby('trade_date', group_keys=False).apply(\n",
+    "    lambda x: x[x['score'] >= x['score'].quantile(0.90)] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n",
+    ").reset_index(drop=True) # drop=True 避免添加旧索引列\n",
+    "# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1, 'score')).reset_index()\n",
+    "save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index()\n",
+    "save_df = save_df.sort_values(['trade_date', 'score'])\n",
+    "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "09b1799e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "191\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(feature_columns))\n",
+    "print(feature_columns)\n",
+    "print([col for col in feature_columns if 'total_mv' in col])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e53b209a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5588 2056336\n",
+      "           ts_code trade_date  turnover_rate\n",
+      "0        000001.SZ 2023-01-03         1.1307\n",
+      "1        000001.SZ 2023-01-04         1.1284\n",
+      "2        000001.SZ 2023-01-05         0.8582\n",
+      "3        000001.SZ 2023-01-06         0.6162\n",
+      "4        000001.SZ 2023-01-09         0.5450\n",
+      "...            ...        ...            ...\n",
+      "2045670  605599.SH 2025-09-26         0.3434\n",
+      "2045671  605599.SH 2025-09-29         0.3943\n",
+      "2045672  605599.SH 2025-09-30         0.4982\n",
+      "2045673  605599.SH 2025-10-09         1.0319\n",
+      "2045674  605599.SH 2025-10-10         0.8859\n",
+      "\n",
+      "[2045675 rows x 3 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(train_data[train_data['pct_chg'] > 7]), len(train_data))\n",
+    "print(test_data[['ts_code', 'trade_date', 'turnover_rate']])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "stock",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/predictions_test.tsv b/predictions_test.tsv
index a53203a..6f18a92 100644
--- a/predictions_test.tsv
+++ b/predictions_test.tsv
@@ -1,1163 +1,1339 @@
 trade_date,score,ts_code
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