diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json
index c0f4aec..77ce259 100644
--- a/catboost_info/catboost_training.json
+++ b/catboost_info/catboost_training.json
@@ -1,970 +1,832 @@
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diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index 6d434fb..5b440bc 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 04d9bd1..29a165b 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 21ef36d..1d19255 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 0044f02..79bd556 100644
--- a/catboost_info/test_error.tsv
+++ b/catboost_info/test_error.tsv
@@ -1,967 +1,829 @@
 iter	Logloss
-0	0.6894367488
-1	0.6866323283
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diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index fcc1a20..37ce9fe 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,967 +1,829 @@
 iter	Passed	Remaining
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-2	99	33068
-3	135	33761
-4	168	33508
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-6	237	33720
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diff --git a/main/data/stk_holdertrade.ipynb b/main/data/stk_holdertrade.ipynb
new file mode 100644
index 0000000..160a500
--- /dev/null
+++ b/main/data/stk_holdertrade.ipynb
@@ -0,0 +1,2349 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "initial_id",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-12T15:31:25.004019Z",
+     "start_time": "2025-03-12T15:31:24.322440Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from operator import index\n",
+    "\n",
+    "import tushare as ts\n",
+    "import pandas as pd\n",
+    "import time\n",
+    "\n",
+    "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
+    "pro = ts.pro_api()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "972a5ac9f79fe373",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-12T15:31:40.917015Z",
+     "start_time": "2025-03-12T15:31:35.958771Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      ts_code  ann_date                       holder_name holder_type in_de  \\\n",
+      "0   300303.SZ  20180104                               王桂山           P    DE   \n",
+      "1   300303.SZ  20180104                               王桂山           P    DE   \n",
+      "2   300303.SZ  20180104                               王桂山           P    DE   \n",
+      "3   002604.SZ  20180104                               尹吉增           G    DE   \n",
+      "4   002604.SZ  20180104                                王燕           G    DE   \n",
+      "5   603188.SH  20180104                               尚俊平           G    DE   \n",
+      "6   000012.SZ  20180104                          中国北方工业公司           C    DE   \n",
+      "7   000012.SZ  20180104                          中国北方工业公司           C    DE   \n",
+      "8   300416.SZ  20180104                      苏州鸿华投资发展有限公司           C    DE   \n",
+      "9   300416.SZ  20180104                      苏州鸿华投资发展有限公司           C    DE   \n",
+      "10  300416.SZ  20180104                      苏州鸿华投资发展有限公司           C    DE   \n",
+      "11  300416.SZ  20180104                      苏州鸿华投资发展有限公司           C    DE   \n",
+      "12  600771.SH  20180104                   华能信托·悦晟3号单一资金信托           C    IN   \n",
+      "13  300371.SZ  20180104                               张继川           G    IN   \n",
+      "14  600512.SH  20180104                  云信-弘瑞20号集合资金信托计划           C    IN   \n",
+      "15  002385.SZ  20180104                               张立忠           G    IN   \n",
+      "16  002271.SZ  20180104                               向锦明           G    DE   \n",
+      "17  002271.SZ  20180104                               向锦明           G    DE   \n",
+      "18  002271.SZ  20180104                               向锦明           G    DE   \n",
+      "19  000652.SZ  20180104                                易蓉           P    IN   \n",
+      "20  002044.SZ  20180104                 云南信托-云起3号集合资金信托计划           C    IN   \n",
+      "21  300433.SZ  20180104                               旷洪峰           G    DE   \n",
+      "22  002305.SZ  20180104                               尹光华           P    DE   \n",
+      "23  002305.SZ  20180104                               尹光华           P    DE   \n",
+      "24  603021.SH  20180104                  芜湖瑞业股权投资基金(有限合伙)           C    DE   \n",
+      "25  603021.SH  20180104                  芜湖瑞业股权投资基金(有限合伙)           C    DE   \n",
+      "26  603021.SH  20180104                  芜湖瑞业股权投资基金(有限合伙)           C    DE   \n",
+      "27  300433.SZ  20180104                               彭孟武           G    DE   \n",
+      "28  300433.SZ  20180104                               彭孟武           G    DE   \n",
+      "29  300433.SZ  20180104                               刘曙光           G    DE   \n",
+      "30  300433.SZ  20180104                               李晓明           G    DE   \n",
+      "31  300433.SZ  20180104                               李晓明           G    DE   \n",
+      "32  300255.SZ  20180104                                陈曦           G    DE   \n",
+      "33  300388.SZ  20180104                               陈会武           G    DE   \n",
+      "34  300388.SZ  20180104                               贺燕峰           G    DE   \n",
+      "35  300390.SZ  20180104                               陆建平           G    DE   \n",
+      "36  300390.SZ  20180104                                成南           G    DE   \n",
+      "37  300266.SZ  20180104                               沈少鸿           G    DE   \n",
+      "38  300026.SZ  20180104                               孙长海           G    DE   \n",
+      "39  002390.SZ  20180104                                王鹏           P    DE   \n",
+      "40  600728.SH  20180104  云南国际信托有限公司-佳都科技第二期员工持股计划集合资金信托计划           C    IN   \n",
+      "41  600728.SH  20180104  云南国际信托有限公司-佳都科技第二期员工持股计划集合资金信托计划           C    IN   \n",
+      "\n",
+      "    change_vol  change_ratio  after_share  after_ratio  avg_price  total_share  \n",
+      "0      30000.0        0.0029  120420000.0      11.7959     3.8800  120420000.0  \n",
+      "1     100000.0        0.0098  120320000.0      11.7861     3.8600  120320000.0  \n",
+      "2     570000.0        0.0558  119750000.0      11.7303     3.6100  119750000.0  \n",
+      "3     222562.0        0.0447          0.0          NaN     8.8000     667683.0  \n",
+      "4       5000.0        0.0010      53876.0       0.0108     9.1500     230507.0  \n",
+      "5     960000.0        0.1667    2880000.0       0.5000    16.5500    2880000.0  \n",
+      "6   22720000.0        1.5052    8280000.0       0.3470        NaN    8280000.0  \n",
+      "7    1580000.0        0.1047    6700000.0       0.2808        NaN    6700000.0  \n",
+      "8     742200.0        1.1611    3450000.0       5.3974    34.5800    3450000.0  \n",
+      "9      21400.0        0.0335    3428600.0       5.3639    30.3000    3428600.0  \n",
+      "10    200000.0        0.3129    3228600.0       5.0510    28.2000    3228600.0  \n",
+      "11     78600.0        0.1230    3150000.0       4.9280    32.9700    3150000.0  \n",
+      "12     21700.0        0.0081      21700.0       0.0081    40.7143      21700.0  \n",
+      "13     15500.0        0.0276     713721.0       1.2722    24.8900    2854885.0  \n",
+      "14   2238800.0        0.1502    2238800.0       0.1502     4.5310    2238800.0  \n",
+      "15    193546.0        0.0085          NaN          NaN     6.0910   15261591.0  \n",
+      "16     30800.0        0.0054   11498918.0       2.0161    37.8400   11542918.0  \n",
+      "17     50000.0        0.0088   11448918.0       2.0073    38.6000   11492918.0  \n",
+      "18     40500.0        0.0071   11408418.0       2.0002    40.1400   11452418.0  \n",
+      "19     35000.0        0.0024      35000.0       0.0024     4.7900      35000.0  \n",
+      "20   3000000.0        0.2825          NaN          NaN    20.2400          NaN  \n",
+      "21    168750.0        0.0381          NaN          NaN    35.0300     506250.0  \n",
+      "22   8800000.0        0.5908   77899000.0       5.2297     4.1900   77899000.0  \n",
+      "23  24580000.0        1.6502   53319000.0       3.5795     4.1900   53319000.0  \n",
+      "24      1780.0        0.0009   12998220.0       6.2542    13.5500   12998220.0  \n",
+      "25     40000.0        0.0192   12958220.0       6.2350    13.3650   12958220.0  \n",
+      "26    790000.0        0.3801   12168220.0       5.8548    13.7410   12168220.0  \n",
+      "27    126500.0        0.0286          NaN          NaN    37.7300     548500.0  \n",
+      "28     42250.0        0.0095          NaN          NaN    40.0000     506250.0  \n",
+      "29    168750.0        0.0381          NaN          NaN    37.5600     506250.0  \n",
+      "30    100000.0        0.0226          NaN          NaN    40.1900     439880.0  \n",
+      "31     34880.0        0.0079          NaN          NaN    40.2400     405000.0  \n",
+      "32   2000000.0        0.3279    7164974.0       1.1748     5.7200   34659888.0  \n",
+      "33    180000.0        0.0600     142500.0       0.0475    23.4300     570000.0  \n",
+      "34    190000.0        0.0633     142500.0       0.0475    23.5100     570000.0  \n",
+      "35    220000.0        0.1371     475001.0       0.2959     9.6140    2560002.0  \n",
+      "36    145000.0        0.0903     154649.0       0.0963     9.6380    1953595.0  \n",
+      "37    498500.0        0.0554      11418.0       0.0013    27.3940    9891232.0  \n",
+      "38   5000000.0        0.2146    3002926.0       0.1289     4.2500   27011704.0  \n",
+      "39       100.0           NaN          NaN          NaN        NaN          NaN  \n",
+      "40   5518301.0        0.4077          NaN          NaN     8.1400          NaN  \n",
+      "41   5518301.0        0.4077          NaN          NaN     8.1400          NaN  \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "df = pro.stk_holdertrade(ann_date='20180104')\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1b5a82fbf4e380de",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-12T15:30:20.421604Z",
+     "start_time": "2025-03-12T15:30:20.224851Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import time\n",
+    "\n",
+    "h5_filename = '/mnt/d/PyProject/NewStock/data/stk_holdertrade.h5'\n",
+    "\n",
+    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250620')\n",
+    "trade_cal = trade_cal[trade_cal['is_open'] == 1]  # 只保留交易日\n",
+    "trade_dates = trade_cal['cal_date'].tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f448da220816bf98",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2025-03-12T15:30:20.436796Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "任务 20170109 完成\n",
+      "任务 20170106 完成\n",
+      "任务 20170105 完成\n",
+      "任务 20170104 完成\n",
+      "任务 20170103 完成\n"
+     ]
+    }
+   ],
+   "source": [
+    "from concurrent.futures import ThreadPoolExecutor, as_completed\n",
+    "\n",
+    "all_daily_data = []\n",
+    "\n",
+    "# API 调用计数和时间控制变量\n",
+    "api_call_count = 0\n",
+    "batch_start_time = time.time()\n",
+    "\n",
+    "\n",
+    "def get_data(trade_date):\n",
+    "    time.sleep(0.1)\n",
+    "    data = pro.stk_holdertrade(trade_date=trade_date)\n",
+    "    if data is not None and not data.empty:\n",
+    "        return data\n",
+    "\n",
+    "\n",
+    "with ThreadPoolExecutor(max_workers=2) as executor:\n",
+    "    future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n",
+    "\n",
+    "    for future in as_completed(future_to_date):\n",
+    "        trade_date = future_to_date[future]  # 获取对应的交易日期\n",
+    "        try:\n",
+    "            result = future.result()  # 获取任务执行的结果\n",
+    "            all_daily_data.append(result)\n",
+    "            print(f\"任务 {trade_date} 完成\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "907f732d3c397bf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-12T15:31:10.381348500Z",
+     "start_time": "2025-03-12T15:23:41.345460Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "所有每日基础数据获取并保存完毕！\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# 将所有数据合并为一个 DataFrame\n",
+    "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
+    "\n",
+    "# 将数据保存为 HDF5 文件（table 格式）\n",
+    "all_daily_data_df.to_hdf(h5_filename, key='stk_holdertrade', mode='w', format='table', data_columns=True)\n",
+    "\n",
+    "print(\"所有每日基础数据获取并保存完毕！\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "73e829ac-ff3d-408e-beb3-0b87f5b00b19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           ts_code  ann_date\n",
+      "0        300578.SZ  20250603\n",
+      "1        002927.SZ  20250603\n",
+      "2        002358.SZ  20250603\n",
+      "3        002972.SZ  20250603\n",
+      "4        002972.SZ  20250603\n",
+      "...            ...       ...\n",
+      "6161995  603090.SH  20250301\n",
+      "6161996  000937.SZ  20250301\n",
+      "6161997  603536.SH  20250301\n",
+      "6161998  002991.SZ  20250301\n",
+      "6161999  603458.SH  20250301\n",
+      "\n",
+      "[6162000 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "key = '/stk_holdertrade'\n",
+    "max_date = None\n",
+    "with pd.HDFStore(h5_filename, mode='r') as store:\n",
+    "    df = store[key][['ts_code', 'ann_date']]\n",
+    "    print(df)\n",
+    "    max_date = df['ann_date'].min()"
+   ]
+  }
+ ],
+ "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/main/factor/__pycache__/money.cpython-313.pyc b/main/factor/__pycache__/money.cpython-313.pyc
new file mode 100644
index 0000000..8fd19ea
Binary files /dev/null and b/main/factor/__pycache__/money.cpython-313.pyc differ
diff --git a/main/factor/__pycache__/money_factor.cpython-313.pyc b/main/factor/__pycache__/money_factor.cpython-313.pyc
new file mode 100644
index 0000000..de80b08
Binary files /dev/null and b/main/factor/__pycache__/money_factor.cpython-313.pyc differ
diff --git a/main/factor/money_factor.py b/main/factor/money_factor.py
new file mode 100644
index 0000000..3466188
--- /dev/null
+++ b/main/factor/money_factor.py
@@ -0,0 +1,115 @@
+import pandas as pd
+import numpy as np
+
+def holder_trade_factors(all_data_df: pd.DataFrame, stk_holdertrade_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    生成合并的股东增减持因子以及 change_ratio 相关因子（优化版）。
+
+    Args:
+        stk_holdertrade_df (pd.DataFrame): 股东增减持数据，包含 'ts_code', 'ann_date', 'in_de', 'change_ratio'。
+        all_data_df (pd.DataFrame): 所有日期所有股票数据，包含 'ts_code', 'trade_date'。
+
+    Returns:
+        pd.DataFrame: 包含增减持因子的 all_data_df。
+    """
+    print('正在计算股东增减持因子（优化版）...')
+
+    # 1. 确保日期列为 datetime 类型
+    stk_holdertrade_df['ann_date'] = pd.to_datetime(stk_holdertrade_df['ann_date'])
+    all_data_df['trade_date'] = pd.to_datetime(all_data_df['trade_date'])
+
+    # 2. 对增减持数据进行预处理和排序（排序在此阶段可能不是严格必需的，但保持良好习惯）
+    holder_data_processed = stk_holdertrade_df.copy()
+    holder_data_processed['change_ratio_in_agg'] = holder_data_processed['change_ratio'].where(holder_data_processed['in_de'] == 'IN', 0)
+    holder_data_processed['change_ratio_de_agg'] = holder_data_processed['change_ratio'].where(holder_data_processed['in_de'] == 'DE', 0)
+    holder_data_processed['change_ratio_total_agg'] = holder_data_processed['change_ratio']
+    holder_data_processed['in_de_numeric'] = holder_data_processed['in_de'].map({'IN': 1, 'DE': -1}).fillna(0) # 用于判断类型
+
+    # 提前获取所有唯一的交易日期集合，以提高查找效率
+    all_trade_dates_set = set(all_data_df['trade_date'].unique())
+
+    # 3. 构建一个辅助DataFrame，记录每个公告在未来10个日历日（且是交易日）的影响
+    expanded_holder_events = []
+    for _, row in holder_data_processed.iterrows():
+        ts_code = row['ts_code']
+        ann_date = row['ann_date']
+        
+        # 生成从公告日期开始的未来10个日历日的日期范围（包括公告日本身）
+        # pd.Timedelta(days=10) 表示从公告日+10天
+        # pd.date_range(start=ann_date, end=ann_date + pd.Timedelta(days=10), freq='D')
+        # 更精确地生成11个日期，涵盖公告日及其后的10个日历日
+        future_dates = pd.date_range(start=ann_date, periods=11, freq='D') 
+
+        for date_in_window in future_dates:
+            # 只有当日期是实际交易日时才添加
+            if date_in_window in all_trade_dates_set:
+                expanded_holder_events.append({
+                    'ts_code': ts_code,
+                    'trade_date': date_in_window,
+                    'in_de_numeric': row['in_de_numeric'],
+                    'change_ratio_total_agg': row['change_ratio_total_agg'],
+                    'change_ratio_in_agg': row['change_ratio_in_agg'],
+                    'change_ratio_de_agg': row['change_ratio_de_agg']
+                })
+    
+    if not expanded_holder_events: # 如果没有事件，直接返回原始 df
+        # 确保返回的DataFrame与原始df具有相同的列和顺序
+        # 并填充为默认值
+        default_factors = pd.DataFrame({
+            'holder_trade_type_10d': None,
+            'holder_change_ratio_sum_10d': 0.0,
+            'holder_in_change_ratio_sum_10d': 0.0,
+            'holder_de_change_ratio_sum_10d': 0.0
+        }, index=all_data_df.index)
+        return pd.concat([all_data_df, default_factors], axis=1)
+
+
+    expanded_holder_events_df = pd.DataFrame(expanded_holder_events)
+
+    # 4. 聚合每个 (ts_code, trade_date) 对上的事件
+    # 可能会有重复的 (ts_code, trade_date) 对，因为一个交易日可能受多个公告影响
+    daily_aggregated_factors = expanded_holder_events_df.groupby(['ts_code', 'trade_date']).agg(
+        holder_change_ratio_sum_10d=('change_ratio_total_agg', 'sum'),
+        holder_in_change_ratio_sum_10d=('change_ratio_in_agg', 'sum'),
+        holder_de_change_ratio_sum_10d=('change_ratio_de_agg', 'sum'),
+        # 对于 holder_trade_type_10d，聚合 in_de_numeric 的唯一值集合
+        _in_de_types_unique=('in_de_numeric', lambda x: set(x)) # 获取该日期窗口内所有独特的增减持类型
+    ).reset_index()
+
+    # 根据 _in_de_types_unique 确定 holder_trade_type_10d
+    def get_trade_type(unique_types_set):
+        if 1 in unique_types_set and -1 in unique_types_set:
+            return 'BOTH'
+        elif 1 in unique_types_set:
+            return 'IN'
+        elif -1 in unique_types_set:
+            return 'DE'
+        else:
+            return None # 理论上不应该发生，除非 unique_types_set 为空或只包含0
+            
+    daily_aggregated_factors['holder_trade_type_10d'] = daily_aggregated_factors['_in_de_types_unique'].apply(get_trade_type)
+
+    # 移除辅助列
+    daily_aggregated_factors.drop(columns=['_in_de_types_unique'], inplace=True)
+
+    # 5. 将计算得到的因子合并回 all_data_df
+    # 确保 all_data_df 也按 ts_code, trade_date 排序，以便 merge 高效
+    all_data_df_sorted = all_data_df.sort_values(['ts_code', 'trade_date']).reset_index(drop=True)
+    
+    final_df = pd.merge(
+        all_data_df_sorted,
+        daily_aggregated_factors,
+        on=['ts_code', 'trade_date'],
+        how='left'
+    )
+
+    # 6. 对于没有增减持记录的日期，因子值为 None 或 0
+    # 在 merge_asof 中确实需要排序，但在这种事件展开的方法中，merge 是普通的 left merge，不需要预排序。
+    # 考虑到最终的 merge，最好还是保持 `all_data_df` 和 `daily_aggregated_factors` 的键排序。
+    # 所以在 `merge` 前对 `all_data_df` 进行一次排序是好的实践。
+    
+    final_df['holder_trade_type_10d'] = final_df['holder_trade_type_10d'].fillna(None)
+    fillna_ratio_cols = ['holder_change_ratio_sum_10d', 'holder_in_change_ratio_sum_10d', 'holder_de_change_ratio_sum_10d']
+    final_df[fillna_ratio_cols] = final_df[fillna_ratio_cols].fillna(0.0)
+
+    return final_df
\ No newline at end of file
diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb
index 7d415c7..3a1694a 100644
--- a/main/train/Classify2.ipynb
+++ b/main/train/Classify2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "79a7758178bafdd3",
    "metadata": {
     "ExecuteTime": {
@@ -18,6 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n",
       "/mnt/d/PyProject/NewStock\n"
      ]
     }
@@ -25,6 +27,7 @@
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
+    "# %load_ext cudf.pandas\n",
     "\n",
     "import gc\n",
     "import os\n",
@@ -44,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "4a481c60",
    "metadata": {},
    "outputs": [],
@@ -56,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -156,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -224,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -324,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -340,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -407,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -435,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -458,12 +461,16 @@
     "                            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"
+    "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,
+   "execution_count": 13,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -611,6 +618,8 @@
     "\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",
@@ -640,6 +649,7 @@
     "# 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",
@@ -709,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "b87b938028afa206",
    "metadata": {
     "ExecuteTime": {
@@ -747,7 +757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "f4f16d63ad18d1bc",
    "metadata": {
     "ExecuteTime": {
@@ -973,7 +983,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "40e6b68a91b30c79",
    "metadata": {
     "ExecuteTime": {
@@ -1293,7 +1303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "47c12bb34062ae7a",
    "metadata": {
     "ExecuteTime": {
@@ -1327,7 +1337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "29221dde",
    "metadata": {},
    "outputs": [
@@ -1370,7 +1380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "03ee5daf",
    "metadata": {},
    "outputs": [],
@@ -1383,7 +1393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "id": "b76ea08a",
    "metadata": {},
    "outputs": [
@@ -1404,7 +1414,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 131/131 [00:12<00:00, 10.28it/s]\n"
+      "MAD Filtering: 100%|██████████| 131/131 [00:16<00:00,  7.78it/s]\n"
      ]
     },
     {
@@ -1419,7 +1429,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 131/131 [00:11<00:00, 11.00it/s]\n"
+      "MAD Filtering: 100%|██████████| 131/131 [00:11<00:00, 11.06it/s]\n"
      ]
     },
     {
@@ -1601,7 +1611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1742,7 +1752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 22,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1776,7 +1786,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0a4d14d383d0499e81773abe038f7d1d",
+       "model_id": "182bcb05602b45a586d931fa2bcb3dd1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1791,13 +1801,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:\tlearn: 0.6888297\ttest: 0.6894367\tbest: 0.6894367 (0)\ttotal: 30.9ms\tremaining: 30.9s\n",
-      "Stopped by overfitting detector  (300 iterations wait)\n",
-      "\n",
-      "bestTest = 0.5057357077\n",
-      "bestIteration = 665\n",
-      "\n",
-      "Shrink model to first 666 iterations.\n"
+      "0:\tlearn: 0.6890148\ttest: 0.6905108\tbest: 0.6905108 (0)\ttotal: 114ms\tremaining: 2m 50s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bestTest = 0.5226514078\n",
+      "bestIteration = 527\n",
+      "Shrink model to first 528 iterations.\n"
      ]
     }
    ],
@@ -1819,7 +1832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 23,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -1857,7 +1870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 24,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -1879,7 +1892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 25,
    "id": "e53b209a",
    "metadata": {},
    "outputs": [
@@ -1912,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 26,
    "id": "364e821a",
    "metadata": {},
    "outputs": [],
@@ -1996,7 +2009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 27,
    "id": "1f6e6336",
    "metadata": {},
    "outputs": [
@@ -2006,13 +2019,7 @@
      "text": [
       "6e+04-9e+04\n",
       "9e+04-1e+05\n",
-      "1e+05-1e+05\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "1e+05-1e+05\n",
       "1e+05-1e+05\n",
       "1e+05-2e+05\n",
       "2e+05-2e+05\n",
@@ -2021,21 +2028,21 @@
       "2e+05-3e+05\n",
       "3e+05-3e+05\n",
       "二分类评估指标:\n",
-      "accuracy: 0.6687\n",
-      "precision: 0.4667\n",
-      "recall: 0.0134\n",
-      "f1: 0.0260\n",
-      "roc_auc: 0.6166\n",
-      "fpr: (array of length 7520)\n",
-      "tpr: (array of length 7520)\n",
-      "thresholds: (array of length 7520)\n",
-      "score_return_correlation: -0.0419\n",
-      "mv_roc_auc: {'6e+04-9e+04': np.float64(0.6129972565157751), '9e+04-1e+05': np.float64(0.5481528934443733), '1e+05-1e+05': np.float64(0.5819692706968757), '1e+05-2e+05': np.float64(0.5802354633555421), '2e+05-2e+05': np.float64(0.610526564518331), '2e+05-3e+05': np.float64(0.6141327685032996), '3e+05-3e+05': np.float64(0.6069017365995996)}\n"
+      "accuracy: 0.6543\n",
+      "precision: 0.4629\n",
+      "recall: 0.1754\n",
+      "f1: 0.2544\n",
+      "roc_auc: 0.6206\n",
+      "fpr: (array of length 7484)\n",
+      "tpr: (array of length 7484)\n",
+      "thresholds: (array of length 7484)\n",
+      "score_return_correlation: -0.0293\n",
+      "mv_roc_auc: {'6e+04-9e+04': np.float64(0.5687499999999999), '9e+04-1e+05': np.float64(0.5662772981208735), '1e+05-1e+05': np.float64(0.5818040450154087), '1e+05-2e+05': np.float64(0.5694847679411162), '2e+05-2e+05': np.float64(0.6027478841082352), '2e+05-3e+05': np.float64(0.6169522445569707), '3e+05-3e+05': np.float64(0.6131663537577515)}\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -2071,7 +2078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 28,
    "id": "7e9023cc",
    "metadata": {},
    "outputs": [],
@@ -2271,7 +2278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 29,
    "id": "a0000d75",
    "metadata": {},
    "outputs": [
@@ -2281,7 +2288,7 @@
      "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 17140 行用于分析。\n",
+      "原始数据 17430 行，移除 NaN 后剩余 17123 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",
@@ -2517,7 +2524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 30,
    "id": "a436dba4",
    "metadata": {},
    "outputs": [
diff --git a/main/train/Rank2.ipynb b/main/train/Rank2.ipynb
index 51060b6..69bd026 100644
--- a/main/train/Rank2.ipynb
+++ b/main/train/Rank2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "id": "79a7758178bafdd3",
    "metadata": {
     "ExecuteTime": {
@@ -18,7 +18,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "e:\\PyProject\\NewStock\\main\\train\n"
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n",
+      "/mnt/d/PyProject/NewStock\n"
      ]
     }
    ],
@@ -29,7 +31,7 @@
     "import gc\n",
     "import os\n",
     "import sys\n",
-    "sys.path.append('../../')\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",
@@ -44,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -68,7 +70,7 @@
       "cyq perf\n",
       "left merge on ['ts_code', 'trade_date']\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 8665405 entries, 0 to 8665404\n",
+      "RangeIndex: 8692146 entries, 0 to 8692145\n",
       "Data columns (total 33 columns):\n",
       " #   Column         Dtype         \n",
       "---  ------         -----         \n",
@@ -115,26 +117,26 @@
     "from main.utils.utils import read_and_merge_h5_data\n",
     "\n",
     "print('daily data')\n",
-    "df = read_and_merge_h5_data('../../data/daily_data.h5', key='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', 'pct_chg', 'amount'],\n",
     "                            df=None)\n",
     "\n",
     "print('daily basic')\n",
-    "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='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('../../data/stk_limit.h5', key='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('../../data/money_flow.h5', key='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('../../data/cyq_perf.h5', key='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",
@@ -144,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -163,7 +165,7 @@
    ],
    "source": [
     "print('industry')\n",
-    "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\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",
@@ -212,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -305,14 +307,14 @@
     "\n",
     "\n",
     "# 使用函数\n",
-    "h5_filename = '../../data/index_data.h5'\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": 5,
+   "execution_count": 13,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -328,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -390,12 +392,12 @@
     "    return industry_data\n",
     "\n",
     "\n",
-    "industry_df = read_industry_data('../../data/sw_daily.h5')\n"
+    "industry_df = read_industry_data('/mnt/d/PyProject/NewStock/data/sw_daily.h5')\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -423,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -433,25 +435,29 @@
    },
    "outputs": [],
    "source": [
-    "fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n",
+    "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'],\n",
     "                            df=None)\n",
-    "cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\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('../../data/balancesheet.h5', key='balancesheet',\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('../../data/top_list.h5', key='top_list',\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"
+    "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'],\n",
+    "                            df=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -464,153 +470,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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",
-      "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",
-      "       'pct_chg', 'amount', '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', 'cat_senti_mom_vol_spike',\n",
-      "       'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20',\n",
-      "       'ts_vol_sustain_10_30', 'cs_amount_outlier_10',\n",
-      "       'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20',\n",
-      "       'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n",
-      "       'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n",
-      "       'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n",
-      "       'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n",
-      "       'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n",
-      "       'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n",
-      "       'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n",
-      "       'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n",
-      "       'cost_support_15pct_change', 'cat_winner_price_zone',\n",
-      "       'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n",
-      "       '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n",
-      "       '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n",
-      "       'vol_ratio', '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 senti_strong_inflow...\n",
-      "Finished senti_strong_inflow.\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: 4539678 entries, 0 to 4539677\n",
-      "Columns: 188 entries, ts_code to cs_rank_size\n",
-      "dtypes: bool(10), datetime64[ns](1), float64(169), int32(4), object(4)\n",
-      "memory usage: 6.0+ GB\n",
-      "None\n",
-      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'amount', '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_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"
+      "正在计算股东增减持因子（优化版）...\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 30\u001b[39m\n\u001b[32m     23\u001b[39m df = df.sort_values(by=[\u001b[33m\"\u001b[39m\u001b[33mts_code\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m     25\u001b[39m \u001b[38;5;66;03m# df = price_minus_deduction_price(df, n=120)\u001b[39;00m\n\u001b[32m     26\u001b[39m \u001b[38;5;66;03m# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\u001b[39;00m\n\u001b[32m     27\u001b[39m \u001b[38;5;66;03m# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\u001b[39;00m\n\u001b[32m     28\u001b[39m \u001b[38;5;66;03m# df = cat_reason(df, top_list_df)\u001b[39;00m\n\u001b[32m     29\u001b[39m \u001b[38;5;66;03m# df = cat_is_on_top_list(df, top_list_df)\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m30\u001b[39m df = \u001b[43mholder_trade_factors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstk_holdertrade_df\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     32\u001b[39m df = cat_senti_mom_vol_spike(\n\u001b[32m     33\u001b[39m     df,\n\u001b[32m     34\u001b[39m     return_period=\u001b[32m3\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m     38\u001b[39m     current_pct_chg_max=\u001b[32m0.05\u001b[39m,\n\u001b[32m     39\u001b[39m )  \u001b[38;5;66;03m# 当日涨幅不宜过大\u001b[39;00m\n\u001b[32m     41\u001b[39m df = cat_senti_pre_breakout(\n\u001b[32m     42\u001b[39m     df,\n\u001b[32m     43\u001b[39m     atr_short_N=\u001b[32m10\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m     51\u001b[39m     volume_ratio_signal_threshold=\u001b[32m1.1\u001b[39m,\n\u001b[32m     52\u001b[39m )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/mnt/d/PyProject/NewStock/main/factor/money_factor.py:50\u001b[39m, in \u001b[36mholder_trade_factors\u001b[39m\u001b[34m(all_data_df, stk_holdertrade_df)\u001b[39m\n\u001b[32m     43\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m date_in_window \u001b[38;5;129;01min\u001b[39;00m future_dates:\n\u001b[32m     44\u001b[39m         \u001b[38;5;66;03m# 只有当日期是实际交易日时才添加\u001b[39;00m\n\u001b[32m     45\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m date_in_window \u001b[38;5;129;01min\u001b[39;00m all_trade_dates_set:\n\u001b[32m     46\u001b[39m             expanded_holder_events.append({\n\u001b[32m     47\u001b[39m                 \u001b[33m'\u001b[39m\u001b[33mts_code\u001b[39m\u001b[33m'\u001b[39m: ts_code,\n\u001b[32m     48\u001b[39m                 \u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m: date_in_window,\n\u001b[32m     49\u001b[39m                 \u001b[33m'\u001b[39m\u001b[33min_de_numeric\u001b[39m\u001b[33m'\u001b[39m: row[\u001b[33m'\u001b[39m\u001b[33min_de_numeric\u001b[39m\u001b[33m'\u001b[39m],\n\u001b[32m---> \u001b[39m\u001b[32m50\u001b[39m                 \u001b[33m'\u001b[39m\u001b[33mchange_ratio_total_agg\u001b[39m\u001b[33m'\u001b[39m: \u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mchange_ratio_total_agg\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m,\n\u001b[32m     51\u001b[39m                 \u001b[33m'\u001b[39m\u001b[33mchange_ratio_in_agg\u001b[39m\u001b[33m'\u001b[39m: row[\u001b[33m'\u001b[39m\u001b[33mchange_ratio_in_agg\u001b[39m\u001b[33m'\u001b[39m],\n\u001b[32m     52\u001b[39m                 \u001b[33m'\u001b[39m\u001b[33mchange_ratio_de_agg\u001b[39m\u001b[33m'\u001b[39m: row[\u001b[33m'\u001b[39m\u001b[33mchange_ratio_de_agg\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m     53\u001b[39m             })\n\u001b[32m     55\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m expanded_holder_events: \u001b[38;5;66;03m# 如果没有事件，直接返回原始 df\u001b[39;00m\n\u001b[32m     56\u001b[39m     \u001b[38;5;66;03m# 确保返回的DataFrame与原始df具有相同的列和顺序\u001b[39;00m\n\u001b[32m     57\u001b[39m     \u001b[38;5;66;03m# 并填充为默认值\u001b[39;00m\n\u001b[32m     58\u001b[39m     default_factors = pd.DataFrame({\n\u001b[32m     59\u001b[39m         \u001b[33m'\u001b[39m\u001b[33mholder_trade_type_10d\u001b[39m\u001b[33m'\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m     60\u001b[39m         \u001b[33m'\u001b[39m\u001b[33mholder_change_ratio_sum_10d\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m0.0\u001b[39m,\n\u001b[32m     61\u001b[39m         \u001b[33m'\u001b[39m\u001b[33mholder_in_change_ratio_sum_10d\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m0.0\u001b[39m,\n\u001b[32m     62\u001b[39m         \u001b[33m'\u001b[39m\u001b[33mholder_de_change_ratio_sum_10d\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m0.0\u001b[39m\n\u001b[32m     63\u001b[39m     }, index=all_data_df.index)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/series.py:1121\u001b[39m, in \u001b[36mSeries.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   1118\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._values[key]\n\u001b[32m   1120\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[32m-> \u001b[39m\u001b[32m1121\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1123\u001b[39m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[32m   1124\u001b[39m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[32m   1125\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/series.py:1239\u001b[39m, in \u001b[36mSeries._get_value\u001b[39m\u001b[34m(self, label, takeable)\u001b[39m\n\u001b[32m   1236\u001b[39m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[32m   1237\u001b[39m loc = \u001b[38;5;28mself\u001b[39m.index.get_loc(label)\n\u001b[32m-> \u001b[39m\u001b[32m1239\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mis_integer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m   1240\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._values[loc]\n\u001b[32m   1242\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m.index, MultiIndex):\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
      ]
     }
    ],
    "source": [
     "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",
@@ -637,6 +518,7 @@
     "# 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",
+    "df = holder_trade_factors(df, stk_holdertrade_df)\n",
     "\n",
     "df = cat_senti_mom_vol_spike(\n",
     "    df,\n",
@@ -1364,7 +1246,9 @@
     "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', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n",
+    "df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1) + df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-2 * days) / x - 1)\n",
+    "\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",
@@ -1376,9 +1260,9 @@
     "#                 .fillna(0)          # 填充每个股票组最后的 NaN\n",
     "#                 .astype(int)\n",
     "#                 .reset_index(level=0, drop=True))\n",
-    "df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
-    "    lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n",
-    ")\n",
+    "# df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
+    "#     lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n",
+    "# )\n",
     "# filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n",
     "filter_index = df['future_return'].between(df['future_return'].quantile(0.001), 0.6)\n",
     "\n",
@@ -1460,13 +1344,7 @@
       "     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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "2  000001.SZ 2019-01-04    16.633371\n",
       "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', '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', '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', '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"
@@ -1476,7 +1354,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 139/139 [00:30<00:00,  4.55it/s]\n"
+      "MAD Filtering: 100%|██████████| 139/139 [00:06<00:00, 21.25it/s]\n"
      ]
     },
     {
@@ -1491,7 +1369,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 139/139 [00:25<00:00,  5.52it/s]\n"
+      "MAD Filtering: 100%|██████████| 139/139 [00:05<00:00, 26.68it/s]\n"
      ]
     },
     {
@@ -1531,13 +1409,13 @@
       "截面 MAD 去极值处理完成。\n",
       "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', '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', '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', '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: 2025-05-23\n",
-      "2097932\n",
+      "df最大日期: 2025-05-30\n",
+      "1091062\n",
       "train_data最小日期: 2020-01-02\n",
       "train_data最大日期: 2022-12-30\n",
-      "1766694\n",
+      "869968\n",
       "test_data最小日期: 2023-01-03\n",
-      "test_data最大日期: 2025-05-23\n",
+      "test_data最大日期: 2025-05-30\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",
@@ -1547,8 +1425,10 @@
    ],
    "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",
+    "train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')].groupby(\n",
+    "    'trade_date', group_keys=False).apply(lambda x: x.nsmallest(1500, 'total_mv'))\n",
+    "test_data = df[(df['trade_date'] >= split_date)].groupby(\n",
+    "    'trade_date', group_keys=False).apply(lambda x: x.nsmallest(1500, 'total_mv'))\n",
     "\n",
     "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n",
     "\n",
@@ -1562,6 +1442,13 @@
     "\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",
+    "train_data['label'] = train_data.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
+    "    lambda x: pd.qcut(x, q=100, labels=False, duplicates='drop')\n",
+    ")\n",
+    "test_data['label'] = test_data.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
+    "    lambda x: pd.qcut(x, q=100, labels=False, duplicates='drop')\n",
+    ")\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",
@@ -1673,7 +1560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 106,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1742,7 +1629,7 @@
     "\n",
     "    if type == 'cat':\n",
     "        params = {\n",
-    "            'loss_function': 'YetiRankPairwise',  # 适用于二分类\n",
+    "            'loss_function': 'QueryRMSE',  # 适用于二分类\n",
     "            'eval_metric': 'NDCG',  # 评估指标\n",
     "            'iterations': 1500,\n",
     "            'learning_rate': 0.03,\n",
@@ -1781,30 +1668,34 @@
     "                )\n",
     "    elif type == 'light':\n",
     "        label_gain = list(range(len(train_data_split['label'].unique())))\n",
+    "        \n",
     "        params = {\n",
     "            'label_gain': [gain * gain for gain in label_gain],\n",
     "            'objective': 'lambdarank',\n",
-    "            'metric': 'lambdarank',\n",
+    "            'metric': 'ndcg',\n",
     "            'learning_rate': 0.05,\n",
-    "            # 'num_leaves': 1024,\n",
-    "            # 'min_data_in_leaf': 256,\n",
-    "            'max_depth': 10,\n",
-    "            # 'max_bin': 1024,\n",
-    "            'feature_fraction': 0.7,\n",
-    "            'bagging_fraction': 0.7,\n",
+    "            'num_leaves': 1024,\n",
+    "            'min_data_in_leaf': 256,\n",
+    "            # 'max_depth': 10,\n",
+    "            'max_bin': 1024,\n",
+    "            'feature_fraction': 0.5,\n",
+    "            'bagging_fraction': 0.5,\n",
     "            'bagging_freq': 5,\n",
-    "            'lambda_l1': 10,\n",
-    "            'lambda_l2': 10,\n",
+    "            'lambda_l1': 5,\n",
+    "            'lambda_l2': 50,\n",
     "            'boosting': 'gbdt',\n",
     "            'verbosity': -1,\n",
     "            'extra_trees': True,\n",
     "            # 'max_position': 5,\n",
-    "            'ndcg_at': '1,3,5,7,9',\n",
+    "            'ndcg_at': '5',\n",
     "            'quant_train_renew_leaf': True,\n",
-    "            # 'lambdarank_truncation_level': 10,\n",
+    "            'lambdarank_truncation_level': 10,\n",
     "            'lambdarank_position_bias_regularization': 1,\n",
     "            'seed': 7\n",
     "        }\n",
+    "        feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n",
+    "        params['feature_contri'] = feature_contri\n",
+    "\n",
     "        train_groups = train_data_split.groupby('trade_date').size().tolist()\n",
     "        val_groups = val_data_split.groupby('trade_date').size().tolist()\n",
     "\n",
@@ -1823,11 +1714,11 @@
     "        evals = {}\n",
     "        callbacks = [lgb.log_evaluation(period=1000),\n",
     "                        lgb.callback.record_evaluation(evals),\n",
-    "                        lgb.early_stopping(300, first_metric_only=False)\n",
+    "                        # lgb.early_stopping(300, first_metric_only=False)\n",
     "                    ]\n",
     "        # 训练模型\n",
     "        model = lgb.train(\n",
-    "            params, train_dataset, num_boost_round=3000,\n",
+    "            params, train_dataset, num_boost_round=1000,\n",
     "            valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n",
     "            callbacks=callbacks\n",
     "        )\n",
@@ -1868,7 +1759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 107,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1881,7 +1772,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train data size:  1092000\n",
+      "train data size:  1091062\n",
       "           ts_code trade_date  log_circ_mv\n",
       "0        600306.SH 2020-01-02    11.552040\n",
       "1        603269.SH 2020-01-02    11.324801\n",
@@ -1889,48 +1780,36 @@
       "3        603991.SH 2020-01-02    11.181150\n",
       "4        000691.SZ 2020-01-02    11.677910\n",
       "...            ...        ...          ...\n",
-      "1091995  002181.SZ 2022-12-30    13.336533\n",
-      "1091996  603387.SH 2022-12-30    13.359713\n",
-      "1091997  000900.SZ 2022-12-30    13.360473\n",
-      "1091998  600748.SH 2022-12-30    13.360639\n",
-      "1091999  603533.SH 2022-12-30    13.362893\n",
+      "1091057  603533.SH 2022-12-30    13.362893\n",
+      "1091058  603416.SH 2022-12-30    13.364553\n",
+      "1091059  002277.SZ 2022-12-30    13.364740\n",
+      "1091060  002140.SZ 2022-12-30    13.086924\n",
+      "1091061  002374.SZ 2022-12-30    13.347147\n",
       "\n",
-      "[1092000 rows x 3 columns]\n",
-      "原始样本数: 1092000, 去除标签为空后样本数: 1092000\n"
+      "[1091062 rows x 3 columns]\n",
+      "原始样本数: 1091062, 去除标签为空后样本数: 1091062\n",
+      "[1000]\ttrain's ndcg@5: 0.667175\tvalid's ndcg@5: 0.357008\n"
      ]
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "39cbc7ff95cb4144822b634ff1a98cb8",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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2NujSpQt27txpdH8lD7HJZDJ8//33GDVqFKysrNCsWTP8/ffftfwsq44BiYiIqBpJkoTs/AKT/JMkqcJ1Ll++HD169MDUqVMRGxuL2NhYeHt7AwDeeustfPjhhzh//jzat2+PzMxMDB06FKGhoTh58iQGDx6M4cOHIzo6+q6PsWDBAjzxxBM4ffo0hg4divHjxyMlJeW+Xt/awkNsRERE1ShHq0PreTtM8tgRCwfBSlWxj3Z7e3uoVCpYWVnBw8MDAHDhwgUAwMKFCzFw4EDDtk5OTujQoYPh9qJFi/Dnn3/i77//xowZM8p9jMmTJ2Ps2LEAgMWLF+Pzzz/H0aNHMXjw4Eo/t9rGHiQiIiIyEhQUZHQ7MzMTs2fPRqtWreDg4AAbGxucP3/+nj1I7du3N/xsbW0NOzs7JCQk1EjN1Y09SERERNXIUqlAxMJBJnvs6lByNtrs2bMREhKCTz/9FAEBAbC0tMRjjz2G/Pz8u95PycuFyGQy6PX6aqmxpjEgERERVSOZTFbhw1ymplKpKnRZjwMHDmDy5MkYNWoUANGjFBkZWcPVmRYPsRERETVQfn5+OHLkCCIjI5GUlFRu706zZs2wceNGhIeH49SpUxg3blyd6QmqKrMISF999RX8/Pyg0WjQrVs3HD16tNxt+/XrZ5iOWPzfsGHDarFiIiKium/27NlQKBRo3bo1XF1dyx1TtHTpUjg6OqJnz54YPnw4Bg0ahM6dO9dytbXL5H2A69evx6xZs7By5Up069YNy5Ytw6BBg3Dx4kW4ubmV2n7jxo1GxzyTk5PRoUMHPP7447VZNhERUZ3XvHlzHDp0yGjZ5MmTS23n5+eHXbt2GS2bPn260e2Sh9zKOuVAampqleo0BZP3IC1duhRTp07FlClT0Lp1a6xcuRJWVlZYtWpVmds7OTnBw8PD8C8kJARWVlYMSERERFRtTNqDlJ+fj7CwMMyZM8ewTC6XIzg4uFSiLc8PP/yAJ598stzrv+Tl5SEvL89wOz09HQCg1Wqh1Wrvo3q6X4WvP9vBPLA9zAfbwnxUpC20Wi0kSYJer6/343Jqml6vhyRJ0Gq1UCiMZ+TV9t+DSQNSUlISdDod3N3djZa7u7sbTlZ1N0ePHsXZs2fxww8/lLvNkiVLsGDBglLLd+/eDSsrq8oXTdUuJCTE1CVQMWwP88G2MB93awsLCwt4eHggMzPzntPe6e7y8/ORk5ODffv2oaCgwGhddnZ2rdZi8jFI9+OHH35Au3bt0LVr13K3mTNnDmbNmmW4nZ6eDm9vb/Tv3x/Ozs61USaVQ6vVIiQkBAMHDix1rgyqfWwP88G2MB8VaYvc3FzExMTAxsYGGo2mliusX3Jzc2FpaYk+ffqUei2Tk5NrtRaTBiQXFxcoFArEx8cbLY+Pjzec9rw8WVlZWLduHRYuXHjX7dRqNdRqdanlSqWSbzxmgm1hXtge5oNtYT7u1hY6nQ4ymQxyuRxyucmH9tZpcrkcMpmszNe7tv8WTNqSKpUKgYGBCA0NNSzT6/UIDQ1Fjx497rrvhg0bkJeXh6eeeqqmyyQiIqIGxuSH2GbNmoVJkyYhKCgIXbt2xbJly5CVlYUpU6YAACZOnAgvLy8sWbLEaL8ffvgBI0eO5GEyIiIiqnYmD0hjxoxBYmIi5s2bh7i4OHTs2BHbt283DNyOjo4u1WV58eJF7N+/H//++68pSiYiIqJ6zuQBCQBmzJiBGTNmlLluz549pZa1aNGizBNQEREREVUHjiYjIiKiKvHz88OyZcsMt2UyGTZt2lTu9pGRkZDJZAgPD6/x2u6XWfQgERERUd0XGxsLR0dHU5dRLRiQiIiIqFrc6xQ9dQkPsRERETVA3377LRo1alTq8igjRozA008/jatXr2LEiBFwd3eHjY0NunTpgp07d971PkseYjt69Cg6deoEjUaDoKAgnDx5siaeSo1gQCIiIqpOkgTkZ5nmXyUmMD3++ONITk7G7t27DctSUlKwfft2jB8/HpmZmRg6dChCQ0Nx8uRJDB48GMOHD0d0dHSF7j8zMxMPP/wwWrdujbCwMLz33nuYPXt2pV9OU+EhNiIiouqkzQYWNzLNY799C1CVffH2khwdHTFkyBCsXbsWAwYMAAD8/vvvcHFxQf/+/SGXy9GhQwfD9osWLcKff/6Jv//+u9yZ58WtXbsWer0eP/zwAzQaDdq0aYMbN27gxRdfrNpzq2XsQSIiImqgxo8fjz/++AN5eXkAgDVr1uDJJ5+EXC5HZmYmZs+ejVatWsHBwQE2NjY4f/58hXuQzp8/j/bt2xtdU+1eV8kwJ+xBIiIiqk5KK9GTY6rHroThw4dDkiRs2bIFXbp0wX///YfPPvsMADB79myEhITg008/RUBAACwtLfHYY48hPz+/Jio3OwxIRERE1Ukmq/BhLlPTaDQYPXo01qxZgytXrqBFixbo3LkzAODAgQOYPHkyRo0aBUCMKYqMjKzwfbdq1Qo///wzcnNzDb1Ihw8frvbnUFN4iI2IiKgBGz9+PLZs2YJVq1Zh/PjxhuXNmjXDxo0bER4ejlOnTmHcuHGlZrzdzbhx4yCTyTB16lRERERg69at+PTTT2viKdQIBiQiIqIG7MEHH4STkxMuXryIcePGGZYvXboUjo6O6NmzJ4YPH45BgwYZepcqwsbGBv/88w/OnDmDTp06Ye7cufjoo49q4inUCB5iIyIiasDkcjlu3So9ZsrPzw+7du0yWjZ9+nSj2yUPuZW8Tmr37t1LXVakrlxLlT1IRERERCUwIBERERGVwIBEREREVAIDEhEREVEJDEhERETVoK4MPjZn5vQaMiARERHdB6VSCQDIzs42cSV1X+FZuhUKhdHyjFwtMnK1tVoLp/kTERHdB4VCAQcHByQkJAAArKysIJPJTFxV3aPX65GYmAgrKytAJseZG2m4GJ+BLadvYe+lRBTk1m4AZUAiIiK6Tx4eHgBgCElUMZIkQS8BuVod9JKE9Fwdlh5ORUT8WVOXxoBERER0v2QyGTw9PeHm5gattnYPBdU1kiRhy5lYrNhzFblanWG5Tg8kZetQcGcYkq3aAq087RDk54jHAhtDVZAN72W1VycDEhERUTVRKBSlxs+Q6CE6fSMNy3ZewqFryShvLPYjHRrhgWYu6N3MBa42algoioZKJyfn1VK1AgMSERERVcqVhAxcSciERqmAlcoC/i7WcLVV49DVZGw7G4uu/k7Q6SUkZebj6PVk7L6YiPwC4wvdWshl+GxMR7TytENTV2uzG7fFgERERET3lFegw9YzsQiLuo21R6KhL9YLJJcB1ioLZOQVAAD+71BUqf1lMqCbvxMGtHTHA81d0MLd1uxCUXEMSERERFRKVl4B8gr0cLJW4WpiJl777RTCY1IN6y3kMjR3t0VajhY3U3MM4aiQu50abRrZo5WnLYa280Rzd1soFXXn7EIMSERERGSQlVeATeE38fH2i0jL0cLDToPb2fnIK9BDZSHHk1280dHbAcPae0JtIcZbxafn4mpiJm5naRHc2g0qhdyse4cqggGJiIionssv0EMvScgr0ON//15EYkYe9JIElYUCTwQ1RoFewo3bOYhKysKPByOhK3b8LC49FwDQo4kzPn6sPbydrErdv7udBu52mlp7PrWBAYmIiKiOyM4vwKX4TEiShHO30rHnYiIik7OQnqOFXCZDM3cbnI9NR1JmPrr6O8HNVo2d5+Oh1UlGoae4f07dKrXMWqXAxJ5+uJKQiVytDi/2a4ru/s6Qy+t2r1BlMCARERGZkauJmTgemQKNUoFu/s44EX0b/i7W2H42Dt//dw1Z+bpy9y3s7QGAo9dTyt1ubFcfNHe3wbXELPx96hbScsS5m2zUFvjo0fYY2s6jzh8iu18MSERERLXsSkIGvtl7DWk5WjzT2x95BXp4OVpi1f7rWHMk+p77O1gpYSGXIdDXEU7Wagxs7Yb8AglxaTmITctFUmY+HK2UuHE7B+m5Wswe1AI7I+Jx+FoyJvTwxahOjQ33NW94a2Tn6aBRyaHTS7BSMRoADEhERET3JSuvALdSc+DrbA2VhRxRyVnYeiYOey8l4JUBzdGjqTMkScJf4bfw4bYLKNDrkZSZb9j/34j4Uvdpo7ZAZrFZYbYaC7jbaTC0rQem9Q+ARln5k1F29nEsc7lSIYe9Vd2ZXVZbGJCIiIgqQK+XkJSVh7RsLS4nZOLg1SRE3ErHyZhUw5mhW3na4XxsumGfw9cOo4mrNSyVCpy7lW50f5ZKBXLuXGpDqZChQC+hbSN7PN+3CR5u3wip2fk4fC0Zbb3s0dix9MBoqlkMSERERGWQJAnrj9/AV6cUmH00BFpdOdfHKKZ4OCp0LTELgDiZopO1Go929sKw9p5o52WP9NwCnIpJRc+mzgBgdGkNBysVBrf1rKZnQ5XFgERERA3a6gPXcSzqNnoHuGB4h0b4IvQyNp+OhYVChqjkbAAyAMbhyN/FGv1auKKJqw2audnAy8ES64/FoKWnLQLcbAxnib6dlY9tZ+MQczsbjwc2RhNXG6P7sbdUok9z19p7slRhDEhERNQgFOj0yCvQo0AnITolG34uVlix5yq+3nMVALDldCzmbDxjtI9MBgzy0mNAt/bIyNPjoTbukMtk8LDTlJryPntQi1KP6WitwrhuPjX3pKjGMCAREVG9k56rhUImg06SsOt8AmauD6/wvrYaC0zrF4C2XnZwt1Hi/NG9GNqxEZRKZc0VTGaHAYmIiOq060lZ+D0sBjdu5+Dg1WQkZuRVav/3hrfGxB5+kCDOI+Rhp4HiTu+QVqvF+RqomcwfAxIREZm9zLwCxKXlwkqlwLHIFDhYqRASEYfQ8wmITcu9674WcjFD7OH2nlg4oq1hCv0vh6PQvrE9+rVwM2zr5WBZ00+F6ggGJCIiMisFOj1i03KRV6DHhbh0XIzLwA/7ryP7LmeQtlIp8HB7T3Rv4oyriZmIScnBk1280cjBEj5OVqXGCzlZqPDygGY1/VSoDmNAIiIis3H6Rirm/nkWZ26m3XU7S6UCNhoLeDlY4ouxndDY0bLBXxqDqhcDEhERVbuMXC1ORqeiubsttDo9EjPzYKcRl8fQSxLUSgWuJmQi9Hw8GjlYwtPBEoeuJmPdsWjDSRcVcjFbzEqlwPAOjTCmizf2XkpEh8YOaOFha9onSPUeAxIREd0XrU6PrWdiYauxQM+mLli59yp+2H8dGbkF9965DA+2dMPcYa3gYaeBtdr4Y+qJIO/qKJnonhiQiIioSrQ6PdYdi8GXuy4jPr1yM8cAQGUhh6uNGnpJglwmg5+LFUZ1aozRnbxKjRkiqm0MSEREVCZJkhCXngudXsKJ6FTsvpCAfJ0eW07HoomrNVKy8pGarS21n4edBq8PaoGRnbyQV6CDpVKBzLwCJGXmQ6eXYK1WwEplAbWFvEoXXSWqDQxIREQNnCRJuJmagwKdBBdbNQ5cScIr604iV6svd5/C64s5W6swqpMXJvbww9HIFPg4WaGLn6NhwLSVSnzM2GqUsNXwRItUdzAgERE1ILlaHZIy89DY0Qp5BTqER6fi/S3n7zlrrNCDLd2gtpCjq78TWrjboqu/k+ECqz7OvOI81R8MSERE9YhOL0GSJOglYNeFeBy6moz49Dx08XfC0HYeGPvtYUQmZyPAzQZpOdpyzzrd0sMWj3RsBF8na6TnatHIwRJ9mrlwKj01GAxIRER12OX4DLz6WzjO3kxHSw9bXIjLKHO77efisGhzhOH2lYRMw89tGtlh+ZOd4Gqrxt/hN9G6kT0CfR1rvHYic8aARERUR/x8OAqr9l9HU1dr2FkqEZOSjWORtw3rS4YjlYUcvQNccOBKEgAgr0APJ2sVBrXxQAt3G9hZKjG4rYdhnBAATOjhVyvPhcjcMSAREZk5vV7CtaQsfLrjItJytLielFXutm0a2eHlAc3Qzd8JGqXCMEtMp5eQnJUHRysVlHfGDBFR+RiQiIhMQJIkRKdk4/SNNGwIuwFbjQUeC2yMiFvpiE3NxuVrcjhdS8HOC4n46VCU0b7P92mCi/EZCPJ1xLD2jeDvYg1ADMBWW8jLHCekkMvgZqupledGVB8wIBER1QJJkpCRVwBLpQI7I+Lx2c5LuBSfabTNltOxxW7JceTH40brewe4YMnodvB2Knu2GM8pRFR9GJCIiGpIfHoulAo5LJUKPPndYZyKSTVar1TI4GytRlJmHvxcrJGZWwC1Uo7mbjY4fjUet/NlsNVYYFw3H7T2tMPQdp48PEZUSxiQiIjukyRJ2HomDlcSMpGak49zN9ORkJGLyORsaJTyMk+4OKStBz4c3R72VqVPnqjVavH731th3aQzegS4wdVWXRtPg4iKYUAiIqqEwnMHWasVOHo9BXlaPf4+dQv778wUK6l4OJr6gD8ik7PRzM0GbwxuedfHsbIQIUqp5NmniUyBAYmIqAxanR6bT99CdHIOlBYynIhKxfWkTFxLyoIklb2Pi40afZq74EZKDs7HpsPL0RJPdfeFQi7DiI6NjKbTE5F5418rETVo/3coEhuO34CnvQbzhrfGyehUnL0pZpalZOXfdd8mrtawt1TC38UaT3X3RWcf45MrSpLEM08T1VEMSETUYMSkZONmag4SM/Lw7b5r0Or0hpMrnrmZhn8j4kvtY6u2gLXaAh29HZBXoMMTQd54sJUbEtLz4OVgCbm8/ADEcERUdzEgEVG9lavVYWnIJfg5W2Pl3quITskuc7vgVu6ITsnCpfhMqC3kGN3ZC5ZKCzzRpTFaetiV2RNU3lR7IqofGJCIqN76/r9r+HbfNaNlMhkgSYC1SoHHg7zRt4Ur+rdwQ0auFr+H3UCvABc0d7ctsQ97gogaGgYkIqrTdHoJKVn5cLVVIy1HCwu5DLFpOfhh/3X8ejTGaNv3R7bF0HaeiE7JRofG9kbBx1ajxJRe/rVdPhGZKQYkIqpTJElCRGw6rFUWOBlzG1/suoJriVlo4mKN68mlZ5iN7uyF0Z0aQydJ6NvcFQDgZK0yQeVEVJcwIBFRnaDV6fHTwUj8ceImzseml1p/rdgFXK1VCjT3sMX0fgEIbu1em2USUT3BgEREZkmSJCRn5WPyj0chSUCOVodriaWvYt+3uSsmdPfF5YRMOFkr0b+lG1xt1Bw3RET3hQGJiExu65lYLNt5CdZqCwxt6wlvJyss23nJMAW/kL2lEq8GN8Ogth6QJMBCUXSFevYUEVF1YkAiolqn1enxe9gN2KgtkJKVj/l/nzOsOxmdWmr7QF9H9GjijKe6+8LDXlOLlRJRQ8WARES1Jio5C78ejUHo+XhcTsg0Wudhp8GEHr74Zu9VpOcWoF8LV7w1pCXUFgr4u1ibqGIiaqgYkIioRlyIS8f1xCy09bKHh70GW07HYuHmiFKX77BSKfDsA03wanAzyGQyPNXdF5fiMxDk68hxRERkMiYPSF999RU++eQTxMXFoUOHDvjiiy/QtWvXcrdPTU3F3LlzsXHjRqSkpMDX1xfLli3D0KFDa7FqIrqbf07dwsvrTpZ5UdfGjpYY3ckLY7r6wNVGDYVcBkWxy3XYWyrRxc+pFqslIirNpAFp/fr1mDVrFlauXIlu3bph2bJlGDRoEC5evAg3N7dS2+fn52PgwIFwc3PD77//Di8vL0RFRcHBwaH2iyciI1vPxGLLmVj8dykR6bkFAETYSc/VGoLSU9198NaQVrBRm/y7GRHRXZn0XWrp0qWYOnUqpkyZAgBYuXIltmzZglWrVuGtt94qtf2qVauQkpKCgwcPQqlUAgD8/Pxqs2SiBkmr0+PAlSQ0dbVBXHoutpyORVJmHtJytGjsaFnqjNUA0MzNBv+81Bs6vYQbt3PQ2NES1gxGRFRHmOzdKj8/H2FhYZgzZ45hmVwuR3BwMA4dOlTmPn///Td69OiB6dOn46+//oKrqyvGjRuHN998EwqFosx98vLykJeXZ7idni5OMKfVaqHVaqvxGVFlFb7+bAfzULw9olKy8d1/17H3UhJ0etH9k5iZf7fdAQADW7nh8UAvtPeyg6OVCnLooZADTZw1ACS2dQXxb8N8sC3MR223gckCUlJSEnQ6Hdzdjc9d4u7ujgsXLpS5z7Vr17Br1y6MHz8eW7duxZUrVzBt2jRotVrMnz+/zH2WLFmCBQsWlFq+e/duWFnxatzmICQkxNQlNFiSBKTkAan5QIZWhl23FHj9yC7k68sfHG2lkOCkAfQScCu7aLvhPjoMsL+FnKu3cORqbVRf//Fvw3ywLUwvOzu7Vh+vTvV36/V6uLm54dtvv4VCoUBgYCBu3ryJTz75pNyANGfOHMyaNctwOz09Hd7e3ujfvz+cnZ1rq3Qqg1arRUhICAYOHGg4ZEo1o0CnR45Wh+SsfGw8cQvhN9IQczsHqdlaZOYVlLmPt6MlJvXwQTM3GyRn5aNHEyfYqi2gVhb11mp1esgAWCjktfRMGgb+bZgPtoX5SE5OrtXHM1lAcnFxgUKhQHx8vNHy+Ph4eHh4lLmPp6cnlEql0eG0Vq1aIS4uDvn5+VCpSl+AUq1WQ61Wl1quVCr5y24m2BY1Q5IkFOglnIpJxQu/nEBSZl652zpbq5BboIO9ogBT+rVECw979LlzYde7YbPVLP5tmA+2henV9utvsoCkUqkQGBiI0NBQjBw5EoDoIQoNDcWMGTPK3KdXr15Yu3Yt9Ho95HLxjfXSpUvw9PQsMxwRNUSSJGH3xQSs3HsNR6+nlFrfvrE9mrvbIiUrHz5OVhjd2QvtGztAq9Vi69atGNrDlx8ERNTgmfQQ26xZszBp0iQEBQWha9euWLZsGbKysgyz2iZOnAgvLy8sWbIEAPDiiy/iyy+/xCuvvIKXXnoJly9fxuLFi/Hyyy+b8mkQmYVcrQ4bjsdgU/gthEXdNlrXyccB304IglIhg4MVv0wQEd2LSQPSmDFjkJiYiHnz5iEuLg4dO3bE9u3bDQO3o6OjDT1FAODt7Y0dO3bg1VdfRfv27eHl5YVXXnkFb775pqmeApFJRdxKx8mY2/j5UFSpC7s6W6vQt7kr2je2x5NdfaBRlj3Tk4iISjP5IO0ZM2aUe0htz549pZb16NEDhw8fruGqiMzfyejbGPX1wVLLpz7gj0c6eKFdY3sTVEVEVD+YPCARUcXp9BLSc7T49Vg0Pt5+0bDc38UaPk5WeHlAMwT6OpqwQiKi+oEBicjMXY7PwDubziIs6jYK9KUvbvbHiz0Q6MtrlxERVScGJCIzlpSZh/HfH0FCRukp+o90aISPHm0PSxXHFhERVTcGJCIzUaDTIyI2Hf4u1jgVk4Yvd1/G8UjRa6RUyDD1gSaw0VhAkoA2jezwQDNXKOTln/GaiIiqjgGJyMQkSUKuVo85G09jU/itUuudrFX45ZluaN3IzgTVERE1TAxIRLUs9Hw8Np64CY1SgcaOlgiJiEdEbHqp7Vp52mHOkJYI8nOElYp/qkREtYnvukQ1IFerg4VcZnSNshu3s/H2n2ex71JimfvYqC3w8oAADGnriUYOlpABkPMQGhGRSTAgEVUTSZJwPjYDuy8mYHnoZeQX6GFvqUSBTo+mbjY4ezMNxSehTerhi8w8HZq52+Dh9p5wsVHzZI5ERGaCAYmoGkiShMVbz+O7/64bLU/L0QIATt9IMyxTyGVYNbkL+lbgYrBERGQaDEhE9+lUTCqmrTmBm6k5hmWLRrRBJx9HpOdqodcDZ2+lQWMhx5guPpyWT0RUBzAgEVVBWo4Wh68l44Mt5xGdkm1Y/nQvfzzftwnc7TRG2/du5lLbJRIR0X1gQCKqoP87FInfw27gzM00SCVOaO3tZInHA70xo38AB1YTEdUDDEhE5UhIz4WzjRp6ScK47w7jWOTtMrf7cHQ7PBbY2GjGGhER1W0MSETFRNxKR16BDgeuJOF/IZdgb6lEarbWsN7TXoMmrtawVFpg7rBWcLZRwU6jNGHFRERUExiQqEHT6vT49Wg0wqNTkZyVj70lzlFUPBy9PKAZXhnQjJf3ICJqABiQqN7Lzi+AxkKB1BwtLsSlw9vRCt5OVkjKzMPzP4chLKrsQ2eWSgWaulkjT6vHiI6N8GK/AIYjIqIGggGJ6rULcekY+dUB2KgtkJ2vQ3a+DgBgq7ZAvk6PvAK9YdvHAhtjdCcvXE/OQnx6Hl56MABKjisiImqQGJCoXsor0CExIw8fbDmPXK0eudp8o/UZeQUAAEcrJb6dGITWnnawVos/h54BnJJPRNTQMSBRvfT6htP4+9Qtw+0BLd3wWGBjDG7rgcsJmXh1fTji0nLx7cQgBPo6mrBSIiIyRwxIVO8kZuQZhaPn+zTBnKGtDLebu9tiy8sPQK+XeM4iIiIqEwMS1Rvbz8bh16PROBaZAgCwkMtwbG4wHK1VZW7PcEREROVhQKJ64ci1ZLzwS5jhtqutGj9N6VpuOCIiIrobBiSq8w5eTcK4744Ybg9t54H5w9uUuh4aERFRRTEgUZ12+kYqZqw9CQBQyGU4PGcAXG3VJq6KiIjqOgYkqlOORaYgLOo2fg+7gSsJmUbrfpgUxHBERETVggGJzNbeS4lYGnIJ0clZ8HW2Rp/mrvg89HKp7bo3ccKCR9qihYetCaokIqL6iAGJzEp2fgGe/PYwTt9Ig9pCbjjT9e3sVITHpJba/p1hrfDsA01quUoiIqrvGJDIbKTlaLHg73M4fSMNAAzhqH1je8MyAHiotTu+Ht8ZFrwMCBER1RAGJDK55Mw8/H4yEr8cjkZceq5h+diuPlg4og2UCjkkSUJkcjbScrTo0NgeMhnPYURERDWHAYlM6kyKDK98tNdw28lahcWj2mJwW0+j7WQyGfxdrGu7PCIiaqAYkMgkcrU6/BN+C99fVBiW9W/hireGtOJgayIiMjkGJDKJV9eHY9vZOMPt0Nf6oqmrjQkrIiIiKsKARLXqZPRtvLzuJGJScgAAnZ31mPNod4YjIiIyKwxIVOMkSYIkAbfScjD++yPIztcBAEZ19EQ/yxh09HYwbYFEREQlMCBRjVpzJAqfh15GfHqeYVlzdxuM6OiFJzo3wsE9MSasjoiIqGwMSFTtriRk4pfDUQiJiMfN1ByjdSqFHF+P74wAN1totVoTVUhERHR3DEh0X/R6CTpJgvLOSRsPXEnC+O+PGNbLZMCE7r5o4WELB0sVmrvbIMCNs9SIiMi8MSBRlaRk5WPWb+HYczER1ioFglu7w06jxC9HogAAnvYavPZQC/Rr4QoXG15AloiI6hYGJKoQvV7Cws0R+PvULej0EvIL9MjRisHWWfk6/BV+y7BtExdrrHgqkOczIiKiOosBie4pK68AH2+/gJ8ORZW5/s3BLXHwahIik7PwdC9/TOrhB7mclwIhIqK6iwGJStl+NhY7zsWjubstMvO0+PvULcN5i8Z29YGztQr+Ltbo3tQZdhoL2GqUeLFfUxNXTUREVH0YkMhIVl4BXvjlRKnlTtYqzB/eGiM6epmgKiIiotrFgERGtpyONfxso7ZAIwcN+rd0w9O9/OFupzFhZURERLWHAYkM4tNz8cm/FwEArw9qgen9A0xcERERkWkwIBEA4FJ8Bh76bB8AMUV/bFcfE1dERERkOnJTF0Cmdyk+AyO/OmC4/dX4znCyVpmwIiIiItNiQCLM2XgG2fk6yGTAr1O7o7OPo6lLIiIiMikeYmvAbtzOxoy1JxEekwq5DNj3Rn80drQydVlEREQmd18BKSsrC7/99huuXLkCT09PjB07Fs7OztVVG9WgzLwCTPnxGC4nZAIAglu5MxwRERHdUamA1Lp1a+zfvx9OTk6IiYlBnz59cPv2bTRv3hxXr17FokWLcPjwYfj7+9dUvVRNvt13DZcTMuFio8L7I9uhXwtXU5dERERkNio1BunChQsoKCgAAMyZMweNGjVCVFQUjh49iqioKLRv3x5z586tkUKp+vyw/zo+D70MAJg7rBUGt/WARqkwcVVERETmo8qDtA8dOoT33nsP9vb2AAAbGxssWLAA+/fvr7biqPp9s/cqFm2OAAA82rkxRnTgmbGJiIhKqvQYJJlMXIQ0NzcXnp6eRuu8vLyQmJhYPZVRtUjP1WLun2dx7HoKsvILkJEregDbednjg1FteVFZIiKiMlQ6IA0YMAAWFhZIT0/HxYsX0bZtW8O6qKgoDtI2I1qdHjPWnsS+S6VD63cTg3hYjYiIqByVCkjz5883um1jY2N0+59//sEDDzxw/1VRtdh8+pYhHAW42WBga3dk5xXg9cEtYaPmGR6IiIjKc18BqaRPPvnkvoqh6nH0ego+2XEB526lAwCm92+K1we1NHFVREREdQe7EeqR7/+7hve3nDda1tjREpN6+pmmICIiojqqSrPYIiMjMXnyZHh6esLS0hLt2rXDzz//XN21USVEJWeVCkfvDGuFHTP7wM1WY6KqiIiI6qZK9yAdOnQIo0aNwnPPPYcDBw7A09MTYWFhmDZtGvLz8/HMM8/URJ1UDkmS8OvRGHyx67Jh2eJR7fB4UGMoFbzUHhERUVVUKiClpKRg9OjRWLVqFYYOHWpY3rt3b6xbtw5DhgzBM888gyeffBKff/453Nzcqr1gEv48eQNrDkejqasN1h+PAQB4O1li3XM94OVgaeLqiIiI6rZKBaQvvvgC/fv3x9ChQ9G2bVtkZ2cbrb9x4wYSExPh7u6OhQsX4ssvv6zWYhs6SZLw08FIbD0bh6PXUwAAx6NuAwDGdvXGO8Naw5qz04iIiO5bpY7BbN68GePGjQMAvPbaa9BoNHj//ffx2Wefwd/fH2+99RacnZ0xY8YMrF+/vkYKbqjyC/RYsu0C3vsnwhCOCo3u5IUPRrZjOCIiIqomlfpEjYqKQpMmTQCI3qQVK1agb9++AIA+ffrAx8cH7777Lpo1a4a0tDTExcXBw8Oj+qtugOb/fQ6/Ho023F49pQuautrgWGQKhndoxDNiExERVaNKBSRLS0ukpIjei4SEBMjlRR1QMpkM2dnZyMrKglKphF6vh4UFezSqw7lbaUbh6MBbDxrGGXk7WZmqLCIionqrUofYOnTogLCwMAAwzGRbv349/vnnHzz66KPo2bMnnJ2dceLECbi4uMDFxaVGim5Ivtt3DcM+L7oA8PhuPhyETUREVMMqFZDGjx+PL7/8EjqdDv/73/8wbtw4LF26FPPmzUPr1q2xadMmAOLw25NPPlkT9TYoer2E1QcjDbfXPNsNH4xqZ7qCiIiIGohKHQN74oknsGLFCrz44ov45ptv8O677+Ldd9812uaHH35AaGgoTp06Va2FNkRrj0bjZmoO7DQWODo3mBeXJSIiqiWV6kGSyWT4448/cO7cOfTp0wfbtm1Damoq8vLycPz4cUyePBkLFizAli1beHitGqw9IsYdvTygGcMRERFRLar0KGpnZ2fs27cP33//PT744AOcOXMGOp0OAQEBGDlyJE6fPg0HB4caKLXhkCQJn+28jIjYdCjkMozu3NjUJRERETUoVboWhUKhwPPPP4/9+/cjLS0NmZmZCA8Px3vvvVelcPTVV1/Bz88PGo0G3bp1w9GjR8vddvXq1ZDJZEb/NJr6da2xX4/G4PNQcemQmQOawclaZeKKiIiIGhaTX6xr/fr1mDVrFubPn48TJ06gQ4cOGDRoEBISEsrdx87ODrGxsYZ/UVFRtVhxzdsUfhMAMKN/AF4a0MzE1RARETU8VTpRUadOnSCTlT4xYWFvTkBAACZPnoz+/fvf876WLl2KqVOnYsqUKQCAlStXYsuWLVi1ahXeeuutMveRyWT19gSUKVn5OBktLh/yaCAPrREREZlClQLS4MGDsWLFCrRr1w5du3YFABw7dgynT5/G5MmTERERgeDgYGzcuBEjRowo937y8/MRFhaGOXPmGJbJ5XIEBwfj0KFD5e6XmZkJX19f6PV6dO7cGYsXL0abNm3K3DYvLw95eXmG2+np6QAArVYLrVZbqeddG77efRlanYSW7jbwslOaZY3VpfC51efnWJewPcwH28J8sC3MR223QZUCUlJSEl577bVSU/zff/99REVF4d9//8X8+fOxaNGiuwakpKQk6HQ6uLu7Gy13d3fHhQsXytynRYsWWLVqFdq3b4+0tDR8+umn6NmzJ86dO4fGjUv3uCxZsgQLFiwotXz37t2wsjKvs1BnaoEfwxQAZOhml4Zt27aZuqRaERISYuoSqBi2h/lgW5gPtoXpZWdn1+rjySRJkiq7k729PcLCwhAQEGC0/MqVKwgMDERaWhouXLiALl26ICMjo9z7uXXrFry8vHDw4EH06NHDsPyNN97A3r17ceTIkXvWotVq0apVK4wdOxaLFi0qtb6sHiRvb2/ExsbC2dm5Ik+3ViRn5ePhLw8iKTMfchlw5K3+cLBSmrqsGqXVahESEoKBAwdCqazfz7UuYHuYD7aF+WBbmI/k5GR4enoiLS0NdnZ2Nf54VepB0mg0OHjwYKmAdPDgQcOMMr1ef8/ZZS4uLlAoFIiPjzdaHh8fX+ExRkqlEp06dcKVK1fKXK9Wq6FWq8vcz5x+2dcfj0RSZj5UFnIsGtEGrvbm1btVk8ytLRo6tof5YFuYD7aF6dX261+lgPTSSy/hhRdeQFhYGLp06QJAjEH6/vvv8fbbbwMAduzYgY4dO971flQqFQIDAxEaGoqRI0cCEMEqNDQUM2bMqFAtOp0OZ86cwdChQ6vyVMzGyRgxMPvdYa0wpouPiashIiJq2KoUkN555x34+/vjyy+/xM8//wxAjA367rvvMG7cOADACy+8gBdffPGe9zVr1ixMmjQJQUFB6Nq1K5YtW4asrCzDrLaJEyfCy8sLS5YsAQAsXLgQ3bt3R0BAAFJTU/HJJ58gKioKzz77bFWeillIy9Ziz8VEAEDrRjXfbUhERER3V6WABIgL144fP77c9ZaWFbvi/JgxY5CYmIh58+YhLi4OHTt2xPbt2w0Dt6OjoyGXF52u6fbt25g6dSri4uLg6OiIwMBAHDx4EK1bt67qUzGpnw9F4t2/zgEAFHIZmrvbmrgiIiIiqlJAOnbsGPR6Pbp162a0/MiRI1AoFAgKCqrU/c2YMaPcQ2p79uwxuv3ZZ5/hs88+q9T9m6uNJ24YwhEAvPRgAGw1PMZNRERkalU6k/b06dMRExNTavnNmzcxffr0+y6qIbiZmoM3/zhtuD2+mw9mBjc3YUVERERUqEo9SBEREejcuXOp5Z06dUJERMR9F1XfSZKExVvPQ6uT4Gmvwc/PdEOAm42pyyIiIqI7qtSDpFarS03NB4DY2FhYWFR5WFODseH4DWw5HQsLuQxfjO3EcERERGRmqhSQHnroIcyZMwdpaWmGZampqXj77bcxcODAaiuuvtp7WcxYe7FfUwT5OZm4GiIiIiqpSt09n376Kfr06QNfX1906tQJABAeHg53d3fDtH8qmyRJ2HdnSn8XhiMiIiKzVKWA5OXlhdOnT2PNmjU4deoULC0tMWXKFIwdO5ZnGr2Hj3dcREZeAQCe84iIiMhcVXnAkLW1NZ577rnqrKXe+yv8JlbsuQoAeK5PE7jYlL4EChEREZlehQPS33//XeE7feSRR6pUTH3358mbAAAXGxXeHNzSxNUQERFReSockAqvlVZIJpNBkiSj24V0Ot39V1bPRCVn4eDVZADAz890g0Iuu8ceREREZCoVnsWm1+sN//7991907NgR27ZtQ2pqKlJTU7F161Z07twZ27dvr8l66yStTo+nfjiC/AI9Ovk4oKUHLydCRERkzqo0BmnmzJlYuXIlevfubVg2aNAgWFlZ4bnnnsP58+errcC6TqeXELgoBOm5BbBRW+CLsZ2MetuIiIjI/FTpPEhXr16Fg4NDqeX29vaIjIy8z5Lql82nbyE9V8xam9jDF40drUxcEREREd1LlQJSly5dMGvWLKOzacfHx+P1119H165dq624uk6SJMOstQeauWDWQF5rjYiIqC6oUkBatWoVYmNj4ePjg4CAAAQEBMDb2xs3b97E999/X9011lkfbruAC3EZAIB3hrWGhaJKLzcRERHVsiqNQQoICMDp06exc+dOw3ijVq1aITg4mONr7sjKK8CqA9cBANP7N0ULDswmIiKqM6p8oshdu3Zh9+7dSEhIgF6vR3h4OH799VcAooepoUrJykeuVocrCZnQ6iR4OVji9UE85xEREVFdUqWAtGDBAixcuBBBQUHw9PRkr9EduVodHv78P9xKyzUs69nU2YQVERERUVVUKSCtXLkSq1evxoQJE6q7njpt8+lYo3DkYqPGK8HNTFgRERERVUWVAlJ+fj569uxZ3bXUeX+F3zT8/GBLN7w9tCWn9RMREdVBVZpW9eyzz2Lt2rXVXUudlpyZZ7iUyJ7Z/bBqchcEuHFgNhERUV1UpR6k3NxcfPvtt9i5cyfat28PpVJptH7p0qXVUlxdEno+ATq9hLZedvBzsTZ1OURERHQfqhSQTp8+jY4dOwIAzp49a7SuoQ7YPn0zFQDQK8DFtIUQERHRfatSQNq9e3d111GnHYtMwS+HowEArTzsTFwNERER3S+e2rkaLN5adHHeVp4MSERERHUdA9J9Cou6jZPRqQCAp3v584zZRERE9UCVz6RNwIo9V/HR9gsAgCeCGmPe8NYmroiIiIiqA3uQqkinl7A89JLh9nN9mpqwGiIiIqpO7EGqopu3c5Cr1QMAds/uB39O7SciIqo32INURVcSMwAALT1sGY6IiIjqGQakKjp4RZw1m7PWiIiI6h8GpCrQ6vTYdOe6a8PaeZq4GiIiIqpuDEhVsPdiIpIy8+Fio0LfFq6mLoeIiIiqGQNSFfwedgMAMLKjF5QKvoRERET1DWexVYIkSXj999PYfi4OAPBoYGMTV0REREQ1gd0flfDf5SRD71GbRnYcoE1ERFRPMSBVwrHIFMPPS0a3M2ElREREVJMYkCrhYpw499H84a3RvrGDaYshIiKiGsOAVEE5+TqciL4NAGjhzgvSEhER1WcMSBX0x4kbSMrMRyN7DTr7Opq6HCIiIqpBDEgVkJiRh0WbIwAAE3v6QaNUmLgiIiIiqkmc5n8PVxIyELx0HwBALuOZs4mIiBoC9iDdw9YzcYaff5zSFd5OViashoiIiGoDA9I9nL2ZBgCY3NMPfZvzsiJEREQNAQPSXWTlFeDIdXHuoyFtPUxcDREREdUWBqS7CImIR1qOFr7OVgjyczJ1OURERFRLGJDu4tSNVADAgy3doJDLTFsMERER1RoGpLs4dzMdANDOy97ElRAREVFtYkAqR2ZeAY7eufZaWwYkIiKiBoUBqRxDlu8z/NzExdqElRAREVFtY0AqgyRJiEnJASBODmmh4MtERETUkPCTvwwpWfmGn3+d2t2ElRAREZEpMCCVIEkSolOyAQAedhp0a+Js4oqIiIiotvFabMXEpGRj6Of/wVYtXhYfXlaEiIioQWJAKmbdsWhk5BYgI7cAANDM3cbEFREREZEp8BBbMZZKhdHtlh62JqqEiIiITIkB6Y4Nx2Pw6b+XjJa1b+xgmmKIiIjIpHiIDYBOL+H1308bbns5WOLdh1uhfWOeIJKIiKghYkACEHEr3ej2G4NbYHBbTxNVQ0RERKbGQ2wAImLTjG572GlMVAkRERGZAwYkANeSsoxuB/o6mqgSIiIiMgcMSAAi7wSkNo3ssPf1fry0CBERUQPHJADgxm1x3bXXHmoOX2demJaIiKihY0ACkJajBQA4WKlMXAkRERGZAwYkAOl3ApKdRmniSoiIiMgcNPiAJEkSMvPEpUXsNDzrARERETEgIStfB70kfrZlDxIRERGhAZ8o8ljkbXRSWkMviXRkIZdBo2zweZGIiIjQgAPSc7+chFx9ESvGdwYA2FkqIZPJTFwVERERmQOz6DL56quv4OfnB41Gg27duuHo0aMV2m/dunWQyWQYOXJklR/7xTUnAIjrsREREREBZhCQ1q9fj1mzZmH+/Pk4ceIEOnTogEGDBiEhIeGu+0VGRmL27Nl44IEHqqUOFxtO8SciIiLB5AFp6dKlmDp1KqZMmYLWrVtj5cqVsLKywqpVq8rdR6fTYfz48ViwYAGaNGlSLXVM6xdQLfdDREREdZ9JA1J+fj7CwsIQHBxsWCaXyxEcHIxDhw6Vu9/ChQvh5uaGZ5555r5rGNXJC99PDMKjgY3v+76IiIiofjDpIO2kpCTodDq4u7sbLXd3d8eFCxfK3Gf//v344YcfEB4eXqHHyMvLQ15enuF2enq64ecxQV54f0QbAIBWq61k9XS/Cl9zvvbmge1hPtgW5oNtYT5quw3q1Cy2jIwMTJgwAd999x1cXFwqtM+SJUuwYMGCMtfF3YjB1q1R1VkiVUFISIipS6Bi2B7mg21hPtgWppednV2rj2fSgOTi4gKFQoH4+Hij5fHx8fDw8Ci1/dWrVxEZGYnhw4cblun1egCAhYUFLl68iKZNmxrtM2fOHMyaNctwOz09Hd7e3gCA1s2bYujAZtX2fKhytFotQkJCMHDgQCiVPEmnqbE9zAfbwnywLcxHcnJyrT6eSQOSSqVCYGAgQkNDDVP19Xo9QkNDMWPGjFLbt2zZEmfOnDFa9s477yAjIwPLly83BJ/i1Go11Gp1mY9vY6niL7wZUCqVbAczwvYwH2wL88G2ML3afv1Nfoht1qxZmDRpEoKCgtC1a1csW7YMWVlZmDJlCgBg4sSJ8PLywpIlS6DRaNC2bVuj/R0cHACg1PKKkPPEkERERFQGkwekMWPGIDExEfPmzUNcXBw6duyI7du3GwZuR0dHQy6vmcl22fkFNXK/REREVLeZPCABwIwZM8o8pAYAe/bsueu+q1evrvLjZuQyIBEREVFpJj9RpCm19LA1dQlERERkhhp0QHqMJ4ckIiKiMjTYgDSlpy8sFA326RMREdFdNNiEoLFosE+diIiI7qHBpgRLlcLUJRAREZGZarABSaNssE+diIiI7qHBpgRLJXuQiIiIqGwNNiBpGJCIiIioHA02IHk5WJq6BCIiIjJTDTYgtfLkSSKJiIiobA02IBERERGVhwGJiIiIqAQGJCIiIqISGJCIiIiISmBAIiIiIiqhYQckbQ7w1wzg4jZTV0JERERmpGEHpINfAid/Bn59EshNN3U1REREZCYabkCSJODAsqLbnzQFEi+ZrBwiIiIyHw02IMliDgP5mUULdPlA6ALTFURERERmo+EGpLhTpRcmsQeJiIiIGnBAgoW69LLbUbVfBxEREZmdhhuQslNKL9PlAXmZpZcTERFRg9JgA5Li8Jdlr0g4X7uFEBERkdlpsAGpXOk3TV0BERERmRgDUklZiaaugIiIiEyMAUljb3ybAYmIiKjBszB1ASY1bCnQfgxwbTdwOQQ48ROw9yPgZhgQ9DTQcpipKyQiIjKNxItA/Fkxw9vaBbB2A5IuAq2GA05NxDaSBJz8BbhxDOj2vFiuUAMyGXDrJCC3AJybAslXgNQYQCYHmj4IKDXGj5VwAbgaCnSeBKisxUQqa2ej9fITm2rtqQMNPSB5dwXUNqKxk68WLb+yUzQsAxIREZmrC1uAnFSg3eOAhQqIOwNc/heIPACkRgMaO8CpKeDXC2j1CGDlJPbLTQPObAB0BUDX5wC5XPws6cQpcLS5wM+jgOiDZT9uyDygzxuAXgvs/6xo+YmfKla3nRfQeaIIQ7GngOwkYPscIC8dOPenCFXRh4HOE4CBCwGNA7BhEhQ3ancSVcMOSNZuRT87eBuvy06u3VqIiMiYTgvseBtwawV4BYleCfvGgKWjqSurOkkCEi8AeRnA2Y1A80Gix6RxF/H8bhwXz/vkL0BCBNBnNuDXG8iIB66EAB3GiqBzaj3w53PiPkMXAP3mAJtnln68m2HAmd+AbW+Jx7pxHEi/UbR+70dAm1EimORnAlbOQEbsvZ/Hvo8r+cRlgGcHcd/pN4E9S8S/km4cK/r5xP8Bp9aJoTAmGP7SYAOSBJn4RSjUeiTw0C2RrPd9IpblZYoepvoiNVr0jLV6RPwhEhFVliSJf/JiQ1hTrov3zkYd77GfHpAr7n3/x74Xh2Ui/gLCVhuvl1sATfqJwzVpMUDjIODhZSI0pN0A0mOBC5tFL0W7x4DMBCD5MtBx/L0fOz9LfBA7+t19u4oqyBPvubp8UZdzgAgz1/cWbXNkRdHPGgcgN9X4PtaNM759YLmoL+ZI0bLM+KJwpFCLQGXlBORnAzZuoqcpKwGI2FS6xpwU4PgPRbeLh6MeM4BBH9zZ7jYQdRCw0AB7PgTSb4meH9cWwIPvAk36i9tnfgeOfgu4tgSCpohatDmApROgsBCv8YHlIpgBAGQAJKDLVNGuf78kPpsVShEQdfmGcKTr+SqAheW/3tVMJkmSVGuPZgbS09Nhb2+P1AV+sJ93veyNPvQRf+zTDotvLrVFkoBtbwJqW2DAu9V//58EiF+0MWuAVg9X//1XklarxdatWzF06FAolUpTl9Pg3bM9bp4Qb4p2nuINz6+3eNPUFYg32b9fFh9Aw5cDLs0q9qBn/wAu7QD6vF7xfRqA+/7biI8Abh4HOj5lHGTKotcDF/4BfHuLMR+SBMSGAzYeACTArpE4ZPPrWCAvTexj1xjo95boCchNB8J+BLTZ4gOu6/Ni3Er0IcC2kegtSb8pgorGHnhhP2DvZVxD+q2iAPXXdOMAUZ2GLRU9N80HA24tjdfFngLWjRehyytIBAOf7tCmJ+DamtcQ0LQpFAXZ4ktzzm0RCp0DxH1ZOwP/vgsoVIBne6D9kyIIbJgk7q8qlFbiNb0rGfDwUuDfeUB+BmDvDTwTIv5GiyvIE6El6bJoD5fmwIPvALfCgZM/A9f2iEDZ/nFArhSfQZ0nipBSnsLoUNUv21lJQOR/4vXLTQNsPcRyvV78zkqSGIe0cx4Q8Tfw8GdIbtQfLi4uSEtLg52dXdUetxIabkD6JBD2s4+XvdHK3uJY7kMfAF2eLT2YrKYknAe+7i5+fjWi9JvI/YjcD6wuNqZq3u2i484K03QkMiCZl3LbIz9L/O7cOllijzvf/Epybgb0fEmM4bN2Kb0+LxPYv1SMlYg7I5Y5NQGe3yc+2Bt3ufeHenHxEeJbsrWLOHzg4CNuA+JN9tIO8Q0757Y4dJF4XnxQD1xUdg9xfrb4wHbyv3ePQ2VkpwCn1wOO/oBPt7seJirVFhX5MMpKFj0leZnA2sdF2Gg+BBj76933O/5jUe+DtZvoaSiuST8RkPTaCj3NCgl+T7R9wgUx7qQiIWLyFuP3sIeXAef/FrXp8ir3+I06AZM2A1d3AalRgG9P4KdHjC9gDgDubUXYqyy5svzXy7UV8Oh3ImDlZ4peo/wsYPcHwO3rIpw9/qP4Pc7LEEHi7B/iMJx/H2DDFDFQGgCmHwNcm4tQcfu6CDlV+bySJPM+qqDXAXIFkpOTGZBqUmFAuv31Q3B4cUfZG60bL7poC007Irr8spNqtkfp1Drgz+fFzyNXAh3H3v99Jl8Vz2XPR4A2q2j5I18CHu2An4aLrue2jwK/TQA6PSW+WZS05TVxbHji34Clw/3XBQakWpWXKT7slZblbqLVarF1y2YMa2YBC58u4hvdif8TXd5VZe0qPmSaDxZv5AnnxTfZ25Hl7xP0NPDwZ+Wvz04BDn0FnP1dfNONPyfCgEuLog+O4AVAy4eBP54RvSF30/V5MVBVZSUCzMEvxd+6lUtRHb9NEP/3eR3o93ZRgNPrxCxYlS3gFVj+l42MOODHoUDKnckgFpbAsE/F4QrXluJ5OPoaNjf628hLBb7rLw4tDVwItH6k9P1LErBqkPFhl0KP/Qi0HQ0kXRF/74GTgX5vFu23oqc4lFERLR8WYeLkGiDhnPE697biMH5eetEymUIM/K2sxl1EiPHrDbQeUbT88Epx+G3k12KSTaH4COD6PsC9tejVKcgTv783jolDW9rsssfnlOTaEuj5shh4nHzZaJVkYQlZQU7RAltPEboLcu9+n9OOiHFTkk7U2PRBEXbKkpcpBknfrecmMxEIeRcICBaHEBsQBqQaZghIq8fBYdKasjfa/jZw+Kui254dxRth/Bng0R9q7pdyy2zg2HfiZ58ewMS/yr6obmWsGiy6uivjxYOAe5ui27npwId3BrEP+VhM5byXi9vF4by2o8t9M2BAqoDiPQdXd4s35fQbwLFVQJdnAO9uoss88YJ4c28cVPo+bp0UvwcyBTDpH9H9btdIvBnn3DZMUNDm5+PWykfhm7JP7GfvbfzNvsUwoHGgCCTJV8QhiUIuzcU3/Mj/gNBF4tvsPcnEgNOMW6KLvzjnZuKDrs0o8WERfVj8HqttgagDFX75ymShEeMaJH3V9tc4iC8XD7wGbHsDSLpUtO7ZXeI1ijkGRO4Tbaa2Ay5uucedysTfSueJ4n3gzoez3isI8psleroHzBN/X7mp4rHtfUQoKT52xc6r6KoASmvR5slXitb79xXPQa8rGgPz6A+i1uiDRTOT7BqL3z2dFnh6W9HUbkD0ehTkifEtSkvxvihJwK73xevbf67ozZAkcUhWJheDjLfPEb9XVi4iONi4iUNf1q7iUK2l412DfJUlXQGu7xHjaM5vLrvXacImoGl/8XNWMrB/KaTjPyLC9WE0n/wllDmJwJ7FYrxNu8fE6xe6QPxNqG1FMFcoge+Dxesf9Iw4BEbVggGphhUGpJQNL8PxseVlb3TwC+DfMnpRADEK//l91V+YJAHL2gNp0cbLx20QwSw7RXx7Ley9CV0oZjGM/VUc8y5Lzm3g4yZFHwQtHwZGrgA+bXb3bz2eHYHn9og3vz1LgPC1Rd3urUcCT5SYynnpX9GlD4hvfXkZRW/G1m7AhD8Bj7alHoYB6R5y04Bv+wEqG/ENOPmK+LAryCn7w12hAsZvEO1+eoP4hi2TA0dWlr6Ejm+voqDh6A+M+AoF6bGw2PhM2bVM2QZ4dy/qOZEk8eEg6cRA2uaDxZTiQqkxwL9zxbrirJzFYYLer4q/JUDUu36COOSiUJU+xFOmYof3HHxED9CRb0v//RR6fLX43S08jFCQL865cmC58RcI15bivtqPAdY8VvkvF4B4PcsLiEHPABe3Fg2EVagrf3joblqPAAbMFwOcMxOAz9qIsHIvD74j3l8KFU4T7/CkuK3Lv/8va8XlZ4vfzdoavlCSXi8Ou8adFkH8wHIR8ntML3WoSZuXg63b/63c+1TObXFYrP2T9Wuij4kxINUwQ0DasgiOQ8sJQXFngW8eELO9ki4Zdz9rHIA3Iyt3vDYvQ3RJt7sTIs5tFPfbaYJ4I7v+n5gJ8HlH0Y3e9lHR1Q8Yjwno+pw4p4VPN/HBCdz9G0rYauCfV8Qx76f+EF3OcoWYWvr7lNLb935VfMhos8T5KWzcimb0FTfkE6DTeBGctNninBj3UsaAdwakEpIuA0e/A/q+IcbN/DWt9h5bbQcpPwsySQd98yGQd3kW+G2i+F14+l/xO1dVOaniw9BCI75d3+1vJy9DnLQ1+pA4FAeI8RzNB4mAn5Mixm4EBIvDL3FnRNBSWYkejdQYMfh225uiZ+ORz0XPWZO+5T+mXgdc3Ca+NLQaXhQEctOB8DWit8W9tViWGi3GToUuKuqxefBdMavoj3LCpWsrETD8+wADFwAxR4Gd74nDd+5txXOO/M9of93gTyD9OxcW+jvh5sF3RG/bZ23KfgxAvG+M/s543NTBL8Rj6QvE7VbDxWt3co3oWbR2Ea/jI1/UTK9NPcD3KfPBgFTDCgNS8u4VcOr3Qvkb5qaLLtOfhos3r+JmXSg9S+BuQuaJbyh3U/iN3jlA9N581a38C+faeACZceJne29g5pnS024lCfimj/iG9ND7YtBscRe2AFdCxYm4Uq6Lb9dyuXgzLX7ir8po0l+cPyrutAiX/d8uGnTeajgw5hejzUu98ej14pu1vZfokk84L0JVfXrjvr5PfHi3GCI+WAvDQsIF4Ot7hBDPjmJQ5+1I4PGfxAfbhS1ijJnKRtx34e8FIAYi63UixD76PXD6N+DS9nuWWPDI17DoPF4cNki/BTQbWOWne1/yMsXfnk938zvvTWaC+AJi3xjoeGca9pFvxZgXSS/qHbhQ/O5W9MvUha3A3g+BFsOg7TULW7duxbAOHrBw8BS9ZIDolQhfK2YKxp4WvXQp10RAe+lE2YPKC/JEyJTJjGsx94G5ZoIByXzUdkBqsOdBKnN2TXGaMl5815ZirMfN44Dd8Io9TuLFe4cjoOhwh1MTEcyeDQWWtix72+IfgmkxwH//Aw5/LT4wn/g/cTju807iG65CLQZhl9RyWNGZwht1KlpeOECxuBZDge7TxNiB+DNlFCQTA067PCs+kG+GiW/1FmoRmq7tBs7/I3pIik/lliTICr/ZAsDB5SKgdZkKXN4hvq27tACeDSl9zTxTuhwinkvQlNLhLTOx6Hfr1gnRe6CyAtJuAke/KfpdSIgQA067TgW8OgO7F5d+HOcAcdgj+Yr4gOw88c54juyicV2dxot/gHiMM7+JD8tmg8TjFuSL0OnoC7QcLgYxuzQHIBPjVjQOoidh62wgNhwSZJB8e4n7c29jPBattqltRJA0RzZuoqevuG7PiX9V1XKo+AcAWjEDSvLqDBT/UG77qPgHiHDWcmjRzL3yZtyVd2iM4YjorhpsQJIaBVZsQ/vGRT/79hIB6fp/okekzDuWgL9niLOgPrxMnFCrkIOPmMHQpL84HFDWIZTCQZB2nqIr/MpOMeOl9Qjg9DrjbVU2okdh1yJxO+Iv4PenxflqCrv/e71cdHr5irByEoPDT/x8ZyafDBj8ofiAnbhJBLnfJoptWwwT01G1OUVjo+QK49klT20Evu0repW+vDOAuN3jQJN+sNj2Jh7OzwHy/xJhonBAcOFAdUB8oG+eJQ6VyC3KfrPPTBC9MjFHge4vlO4tu5fkq+KQZ/PB9/7Q0OvEa5yXDtw4Ksa2FCo+C7EitFnAgWWllzfqDIz+tuzzAslk5c+AsfcSh0mLs1AVzY6yUImBuYZ1d4Kcdxfg+b3Q5mRi55Y/EWxXjaeXoJpX1sB8IrpvDTYgldlDVJYB88QhjS7Pig//4z8YH3LLyxBd2NYu4oJ+y4sNmDac2VQlZg/5dDe+b7WN6PreOLXo/BvtnihaP3y5GCvQpK/Yt/erRYdh3NuK6burhxqfgv3sH0U/e3cTM0kqq0k/8S87RYSfwvMxWbuIoNZ+jJhWO/obEVjuNnhTLhdB8fsHi5ad2QCc2QAZxFBbnP+r7H3H/wGseVRM5z73pxgQbOUsxrM89L4YQBpzTIzdSI0S+/z7jvintgOm7jIOGTFHRU9W50mAS4BYlhojZpzkpNx9hmJGnDhUeub3omnL5/4UhzsDJ905dPZG2fsWZ9cYeOBVcbjrVriYuRR9SPyO9Xu7aPq1KVioka+s+W5rIqK6oOEGpIqyawQ8fWfcRlaS+D8hQhxKsXEVJxdLuSrOdXF8Vdn30fax0uEIKDq/xyOfA3/NAEatFFOEC9k3Nv7AdGsJPPmrGHPQYaw4e+u0I+JEZoXjpQqDlkwuToR3P93o5fU8jf62cvfj1VnUe3GrmJl1N4273umZ+QloFiwOByVdKgolhdfI+/P5u/fW5KUD+z4VIU6vA3bOFwNWAeDg58DTO0RPzYZJIhwBwD8zxaET/z7itiSJgbsFueIs0ZfLOG/WkRXGlwooyaWFCMiD3hcngCuc0dLl2aJtdAVi9lXxKdRERGRSDEiVYe0CuLURJ0j7NECcL+jWCbHu2HdivFGh4tOoywpHxRUfV3AvhWMUDDU5F83Qee0CAJnoUVIojQ8PmpJMJsIfIMba7P0I6PQUCnwfQMHmN6C2d4XsxYOiJ0qSxOHHwiDR7nFxhtl7sfMSr+HBz4uWnV53Zyq5VBSOCm19/c5ZnCVxSn+1nRjb9dNwMU3bs73oFcq5bbxfjxnikFzgJHGywuK9idZu4gy5Vs7isOCAd4vC1t0oLBiOiIjMDANSZXm2LzqD7IqeRcv/+1/RzxP/EtO0CwOSV+faqU1te+d/Mz7vxgOviUHhTQdAkmQIua7A4EEPQVl4mE4mM67/gdfE7K1NL4pDnE9tFL1BV3aK9XZe4mrXjTqLMBT0tDjh3Nc9RK/MjjnGj//0v+KMw3Gni5Y99L7ozfukqbh9+3rZ57Hx7l504UZAjAErDEg9Zoj7Keyxezakyi8RERGZHgNSZXV/ETj1a/nrZQrxgS5JYhpw20fFeCESLNRFM5O0WujlKtGDUx65Amj+EPDG1aJlI74W56LxCiw6P00hJ/8723whDnlG7heH5VxaiBN8KjWiTc7+Lrbz7SXGJCksxP+XtosTyJVlaIlzQvn1Bpo9JGbbPfAaZwUREdUjDEiV5dkBGPcbsPbOYOqhn4qp2rveF+clGb5czOhq2h94+xY/NGuCrbs4f9PdFA40z8sAoo8AjToWnbV39HdA75liTFnhZQUAMRascBr9mQ1A0wGihyriT8C/nzicWZxMJs5cTURE9Q4DUlU0ewjoME4MHm5/JyiVdYFXhiPTU9uKwd7FyeXG092LK5xGHzi5aFlFx4cREVG9wYBUFTIZMOouM5eIiIioTpPfexMiIiKihoUBiYiIiKgEBiQiIiKiEhiQiIiIiEpgQCIiIiIqgQGJiIiIqAQGJCIiIqISGJCIiIiISmBAIiIiIiqBAYmIiIioBAYkIiIiohIYkIiIiIhKYEAiIiIiKoEBiYiIiKgEBiQiIiKiEhiQiIiIiEpgQCIiIiIqgQGJiIiIqAQGJCIiIqISGJCIiIiISmBAIiIiIiqBAYmIiIioBLMISF999RX8/Pyg0WjQrVs3HD16tNxtN27ciKCgIDg4OMDa2hodO3bEzz//XIvVEhERUX1n8oC0fv16zJo1C/Pnz8eJEyfQoUMHDBo0CAkJCWVu7+TkhLlz5+LQoUM4ffo0pkyZgilTpmDHjh21XDkRERHVVyYPSEuXLsXUqVMxZcoUtG7dGitXroSVlRVWrVpV5vb9+vXDqFGj0KpVKzRt2hSvvPIK2rdvj/3799dy5URERFRfWZjywfPz8xEWFoY5c+YYlsnlcgQHB+PQoUP33F+SJOzatQsXL17ERx99VOY2eXl5yMvLM9xOT08HAGi1Wmi12vt8BnQ/Cl9/toN5YHuYD7aF+WBbmI/abgOTBqSkpCTodDq4u7sbLXd3d8eFCxfK3S8tLQ1eXl7Iy8uDQqHA119/jYEDB5a57ZIlS7BgwYJSy3fv3g0rK6v7ewJULUJCQkxdAhXD9jAfbAvzwbYwvezs7Fp9PJMGpKqytbVFeHg4MjMzERoailmzZqFJkybo169fqW3nzJmDWbNmGW6np6fD29sb/fv3h7Ozcy1WTSVptVqEhIRg4MCBUCqVpi6nwWN7mA+2hflgW5iP5OTkWn08kwYkFxcXKBQKxMfHGy2Pj4+Hh4dHufvJ5XIEBAQAADp27Ijz589jyZIlZQYktVoNtVpdarlSqeQvu5lgW5gXtof5YFuYD7aF6dX262/SQdoqlQqBgYEIDQ01LNPr9QgNDUWPHj0qfD96vd5onBERERHR/TD5IbZZs2Zh0qRJCAoKQteuXbFs2TJkZWVhypQpAICJEyfCy8sLS5YsASDGFAUFBaFp06bIy8vD1q1b8fPPP2PFihWmfBpERERUj5g8II0ZMwaJiYmYN28e4uLi0LFjR2zfvt0wcDs6OhpyeVFHV1ZWFqZNm4YbN27A0tISLVu2xC+//IIxY8aY6ikQERFRPWPygAQAM2bMwIwZM8pct2fPHqPb77//Pt5///1aqIqIiIgaKpOfKJKIiIjI3DAgEREREZXAgERERERUAgMSERERUQkMSEREREQlMCARERERlcCARERERFQCAxIRERFRCQxIRERERCUwIBERERGVwIBEREREVAIDEhEREVEJDEhEREREJTAgEREREZXAgERERERUAgMSERERUQkMSEREREQlWJi6gNomSRIAICMjA0ql0sTVNGxarRbZ2dlIT09nW5gBtof5YFuYD7aF+cjIyABQ9Dle0xpcQEpOTgYA+Pv7m7gSIiIiqqzk5GTY29vX+OM0uIDk5OQEAIiOjq6VF5jKl56eDm9vb8TExMDOzs7U5TR4bA/zwbYwH2wL85GWlgYfHx/D53hNa3ABSS4Xw67s7e35y24m7Ozs2BZmhO1hPtgW5oNtYT4KP8dr/HFq5VGIiIiI6hAGJCIiIqISGlxAUqvVmD9/PtRqtalLafDYFuaF7WE+2Bbmg21hPmq7LWRSbc2XIyIiIqojGlwPEhEREdG9MCARERERlcCARERERFQCAxIRERFRCQ0uIH311Vfw8/ODRqNBt27dcPToUVOXVK8sWbIEXbp0ga2tLdzc3DBy5EhcvHjRaJvc3FxMnz4dzs7OsLGxwaOPPor4+HijbaKjozFs2DBYWVnBzc0Nr7/+OgoKCmrzqdQ7H374IWQyGWbOnGlYxraoXTdv3sRTTz0FZ2dnWFpaol27djh+/LhhvSRJmDdvHjw9PWFpaYng4GBcvnzZ6D5SUlIwfvx42NnZwcHBAc888wwyMzNr+6nUaTqdDu+++y78/f1haWmJpk2bYtGiRUbX+GJb1Ix9+/Zh+PDhaNSoEWQyGTZt2mS0vrpe99OnT+OBBx6ARqOBt7c3Pv7448oXKzUg69atk1QqlbRq1Srp3Llz0tSpUyUHBwcpPj7e1KXVG4MGDZJ+/PFH6ezZs1J4eLg0dOhQycfHR8rMzDRs88ILL0je3t5SaGiodPz4cal79+5Sz549DesLCgqktm3bSsHBwdLJkyelrVu3Si4uLtKcOXNM8ZTqhaNHj0p+fn5S+/btpVdeecWwnG1Re1JSUiRfX19p8uTJ0pEjR6Rr165JO3bskK5cuWLY5sMPP5Ts7e2lTZs2SadOnZIeeeQRyd/fX8rJyTFsM3jwYKlDhw7S4cOHpf/++08KCAiQxo4da4qnVGd98MEHkrOzs7R582bp+vXr0oYNGyQbGxtp+fLlhm3YFjVj69at0ty5c6WNGzdKAKQ///zTaH11vO5paWmSu7u7NH78eOns2bPSr7/+KllaWkrffPNNpWptUAGpa9eu0vTp0w23dTqd1KhRI2nJkiUmrKp+S0hIkABIe/fulSRJklJTUyWlUilt2LDBsM358+clANKhQ4ckSRJ/QHK5XIqLizNss2LFCsnOzk7Ky8ur3SdQD2RkZEjNmjWTQkJCpL59+xoCEtuidr355ptS7969y12v1+slDw8P6ZNPPjEsS01NldRqtfTrr79KkiRJEREREgDp2LFjhm22bdsmyWQy6ebNmzVXfD0zbNgw6emnnzZaNnr0aGn8+PGSJLEtakvJgFRdr/vXX38tOTo6Gr1Hvfnmm1KLFi0qVV+DOcSWn5+PsLAwBAcHG5bJ5XIEBwfj0KFDJqysfktLSwNQdJHgsLAwaLVao3Zo2bIlfHx8DO1w6NAhtGvXDu7u7oZtBg0ahPT0dJw7d64Wq68fpk+fjmHDhhm95gDborb9/fffCAoKwuOPPw43Nzd06tQJ3333nWH99evXERcXZ9Qe9vb26Natm1F7ODg4ICgoyLBNcHAw5HI5jhw5UntPpo7r2bMnQkNDcenSJQDAqVOnsH//fgwZMgQA28JUqut1P3ToEPr06QOVSmXYZtCgQbh48SJu375d4XoazMVqk5KSoNPpjN7oAcDd3R0XLlwwUVX1m16vx8yZM9GrVy+0bdsWABAXFweVSgUHBwejbd3d3REXF2fYpqx2KlxHFbdu3TqcOHECx44dK7WObVG7rl27hhUrVmDWrFl4++23cezYMbz88stQqVSYNGmS4fUs6/Uu3h5ubm5G6y0sLODk5MT2qIS33noL6enpaNmyJRQKBXQ6HT744AOMHz8eANgWJlJdr3tcXBz8/f1L3UfhOkdHxwrV02ACEtW+6dOn4+zZs9i/f7+pS2mQYmJi8MorryAkJAQajcbU5TR4er0eQUFBWLx4MQCgU6dOOHv2LFauXIlJkyaZuLqG5bfffsOaNWuwdu1atGnTBuHh4Zg5cyYaNWrEtiCDBnOIzcXFBQqFotQMnfj4eHh4eJioqvprxowZ2Lx5M3bv3o3GjRsblnt4eCA/Px+pqalG2xdvBw8PjzLbqXAdVUxYWBgSEhLQuXNnWFhYwMLCAnv37sXnn38OCwsLuLu7sy1qkaenJ1q3bm20rFWrVoiOjgZQ9Hre7T3Kw8MDCQkJRusLCgqQkpLC9qiE119/HW+99RaefPJJtGvXDhMmTMCrr76KJUuWAGBbmEp1ve7V9b7VYAKSSqVCYGAgQkNDDcv0ej1CQ0PRo0cPE1ZWv0iShBkzZuDPP//Erl27SnVzBgYGQqlUGrXDxYsXER0dbWiHHj164MyZM0Z/BCEhIbCzsyv1AUPlGzBgAM6cOYPw8HDDv6CgIIwfP97wM9ui9vTq1avUKS8uXboEX19fAIC/vz88PDyM2iM9PR1Hjhwxao/U1FSEhYUZttm1axf0ej26detWC8+ifsjOzoZcbvzxp1AooNfrAbAtTKW6XvcePXpg37590Gq1hm1CQkLQokWLCh9eA9Dwpvmr1Wpp9erVUkREhPTcc89JDg4ORjN06P68+OKLkr29vbRnzx4pNjbW8C87O9uwzQsvvCD5+PhIu3btko4fPy716NFD6tGjh2F94dTyhx56SAoPD5e2b98uubq6cmp5NSg+i02S2Ba16ejRo5KFhYX0wQcfSJcvX5bWrFkjWVlZSb/88othmw8//FBycHCQ/vrrL+n06dPSiBEjypzi3KlTJ+nIkSPS/v37pWbNmnFqeSVNmjRJ8vLyMkzz37hxo+Ti4iK98cYbhm3YFjUjIyNDOnnypHTy5EkJgLR06VLp5MmTUlRUlCRJ1fO6p6amSu7u7tKECROks2fPSuvWrZOsrKw4zf9evvjiC8nHx0dSqVRS165dpcOHD5u6pHoFQJn/fvzxR8M2OTk50rRp0yRHR0fJyspKGjVqlBQbG2t0P5GRkdKQIUMkS0tLycXFRXrttdckrVZby8+m/ikZkNgWteuff/6R2rZtK6nVaqlly5bSt99+a7Rer9dL7777ruTu7i6p1WppwIAB0sWLF422SU5OlsaOHSvZ2NhIdnZ20pQpU6SMjIzafBp1Xnp6uvTKK69IPj4+kkajkZo0aSLNnTvXaFo426Jm7N69u8zPiEmTJkmSVH2v+6lTp6TevXtLarVa8vLykj788MNK1yqTpGKnDiUiIiKihjMGiYiIiKiiGJCIiIiISmBAIiIiIiqBAYmIiIioBAYkIiIiohIYkIiIiIhKYEAiIiIiKoEBiYgaBD8/PyxbtszUZRBRHcGARETVbvLkyRg5ciQAoF+/fpg5c2atPfbq1avh4OBQavmxY8fw3HPP1VodRFS3WZi6ACKiisjPz4dKpary/q6urtVYDRHVd+xBIqIaM3nyZOzduxfLly+HTCaDTCZDZGQkAODs2bMYMmQIbGxs4O7ujgkTJiApKcmwb79+/TBjxgzMnDkTLi4uGDRoEABg6dKlaNeuHaytreHt7Y1p06YhMzMTALBnzx5MmTIFaWlphsd77733AJQ+xBYdHY0RI0bAxsYGdnZ2eOKJJxAfH29Y/95776Fjx474+eef4efnB3t7ezz55JPIyMgwbPP777+jXbt2sLS0hLOzM4KDg5GVlVVDryYR1SYGJCKqMcuXL0ePHj0wdepUxMbGIjY2Ft7e3khNTcWDDz6ITp064fjx49i+fTvi4+PxxBNPGO3/008/QaVS4cCBA1i5ciUAQC6X4/PPP8e5c+fw008/YdeuXXjjjTcAAD179sSyZctgZ2dneLzZs2eXqkuv12PEiBFISUnB3r17ERISgmvXrmHMmDFG2129ehWbNm3C5s2bsXnzZuzduxcffvghACA2NhZjx47F008/jfPnz2PPnj0YPXo0eHlLovqBh9iIqMbY29tDpVLBysoKHh4ehuVffvklOnXqhMWLFxuWrVq1Ct7e3rh06RKaN28OAGjWrBk+/vhjo/ssPp7Jz88P77//Pl544QV8/fXXUKlUsLe3h0wmM3q8kkJDQ3HmzBlcv34d3t7eAID/+7//Q5s2bXDs2DF06dIFgAhSq1evhq2tLQBgwoQJCA0NxQcffIDY2FgUFBRg9OjR8PX1BQC0a9fuPl4tIjIn7EEiolp36tQp7N69GzY2NoZ/LVu2BCB6bQoFBgaW2nfnzp0YMGAAvLy8YGtriwkTJiA5ORnZ2dkVfvzz58/D29vbEI4AoHXr1nBwcMD58+cNy/z8/AzhCAA8PT2RkJAAAOjQoQMGDBiAdu3a4fHHH8d3332H27dvV/xFICKzxoBERLUuMzMTw4cPR3h4uNG/y5cvo0+fPobtrK2tjfaLjIzEww8/jPbt2+OPP/5AWFgYvvrqKwBiEHd1UyqVRrdlMhn0ej0AQKFQICQkBNu2bUPr1q3xxRdfoEWLFrh+/Xq110FEtY8BiYhqlEqlgk6nM1rWuXNnnDt3Dn5+fggICDD6VzIUFRcWFga9Xo///e9/6N69O5o3b45bt27d8/FKatWqFWJiYhATE2NYFhERgdTUVLRu3brCz00mk6FXr15YsGABTp48CZVKhT///LPC+xOR+WJAIqIa5efnhyNHjiAyMhJJSUnQ6/WYPn06UlJSMHbsWBw7dgxXr17Fjh07MGXKlLuGm4CAAGi1WnzxxRe4du0afv75Z8Pg7eKPl5mZidDQUCQlJZV56C04OBjt2rXD+PHjceLECRw9ehQTJ05E3759ERQUVKHndeTIESxevBjHjx9HdHQ0Nm7ciMTERLRq1apyLxARmSUGJCKqUbNnz4ZCoUDr1q3h6uqK6OhoNGrUCAcOHIBOp8NDDz2Edu3aYebMmXBwcIBcXv7bUocOHbB06VJ89NFHaNu2LdasWYMlS5YYbdOzZ0+88MILGDNmDFxdXUsN8gZEz89ff/0FR0dH9OnTB8HBwWjSpAnWr19f4edlZ2eHffv2YejQoWjevDneeecd/O9//8OQIUMq/uIQkdmSSZyTSkRERGSEPUhEREREJTAgEREREZXAgERERERUAgMSERERUQkMSEREREQlMCARERERlcCARERERFQCAxIRERFRCQxIRERERCUwIBERERGVwIBEREREVAIDEhEREVEJ/w9d+ER2pRqIhQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Default metric period is 5 because NDCG is/are not implemented for GPU\n",
-      "Metric NDCG:type=Base is not implemented on GPU. Will use CPU for metric computation, this could significantly affect learning time\n",
-      "Metric NDCG:type=Base is not implemented on GPU. Will use CPU for metric computation, this could significantly affect learning time\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0:\ttest: 0.8974597\tbest: 0.8974597 (0)\ttotal: 827ms\tremaining: 20m 40s\n",
-      "bestTest = 0.9205225358\n",
-      "bestIteration = 1054\n",
-      "Shrink model to first 1055 iterations.\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1938,13 +1817,13 @@
     "gc.collect()\n",
     "\n",
     "use_pca = False\n",
-    "type = 'cat'\n",
+    "type = 'light'\n",
     "# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n",
     "# light_params['feature_contri'] = feature_contri\n",
     "# print(f'feature_contri: {feature_contri}')\n",
     "model, scaler, pca = train_model(train_data\n",
     "                                 .dropna(subset=['label']).groupby('trade_date', group_keys=False)\n",
-    "                                 .apply(lambda x: x.nsmallest(1500, 'total_mv'))\n",
+    "                                 .apply(lambda x: x.nsmallest(3000, 'total_mv'))\n",
     "                                 .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
     "                                 .merge(index_data, on='trade_date', how='left'), \n",
     "                                 feature_columns, type=type, target_column='label')\n"
@@ -1962,7 +1841,7 @@
    },
    "outputs": [],
    "source": [
-    "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1000, 'total_mv'))\n",
+    "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(3000, '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",
@@ -1989,12 +1868,12 @@
     "save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(5, '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"
+    "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "1f3c1331",
    "metadata": {},
    "outputs": [
@@ -2006,34 +1885,34 @@
       "成功连接到 Redis 服务器: 140.143.91.66:6389，数据库 0\n",
       "DataFrame 已使用 Pickle 序列化并写入 Redis，键为 'save_df'\n",
       "从 Redis 读取到的 Pickle 序列化数据 (前 20 字节):\n",
-      "b'\\x80\\x04\\x95|\\x02\\x01\\x00\\x00\\x00\\x00\\x00\\x8c\\x11pandas.'\n",
+      "b'\\x80\\x04\\x95\\xbf\\x04\\x01\\x00\\x00\\x00\\x00\\x00\\x8c\\x11pandas.'\n",
       "\n",
       "从 Redis 加载的 DataFrame (使用 Pickle):\n",
       "      index    ts_code trade_date   open  close   high    low        vol  \\\n",
-      "4        30  603900.SH 2023-01-03  10.60  10.71  10.81  10.57   21646.00   \n",
-      "3        34  002871.SZ 2023-01-03  35.94  36.71  36.91  35.78   14276.69   \n",
-      "2        32  600356.SH 2023-01-03  15.64  15.88  15.94  15.56   25058.00   \n",
-      "1        57  605500.SH 2023-01-03  15.60  15.77  15.87  15.52    5322.20   \n",
-      "0        18  600235.SH 2023-01-03  12.23  12.41  12.50  12.23   35791.00   \n",
+      "4        25  002802.SZ 2023-01-03  21.53  22.14  22.35  21.31   15974.43   \n",
+      "3        20  600235.SH 2023-01-03  12.23  12.41  12.50  12.23   35791.00   \n",
+      "2        13  002691.SZ 2023-01-03   9.13   9.32   9.37   9.13   35566.80   \n",
+      "1        35  603779.SH 2023-01-03   9.74   9.88  10.16   9.69   78641.45   \n",
+      "0       131  600228.SH 2023-01-03  15.68  15.75  15.79  15.60   17076.00   \n",
       "...     ...        ...        ...    ...    ...    ...    ...        ...   \n",
-      "2879  57583  600526.SH 2025-05-23  18.69  18.50  18.85  18.50   59499.00   \n",
-      "2878  57510  002687.SZ 2025-05-23  30.82  30.54  31.17  30.41   79880.43   \n",
-      "2877  57513  601996.SH 2025-05-23  10.51  10.36  10.56  10.31  142710.02   \n",
-      "2876  57517  603797.SH 2025-05-23   9.06   8.93   9.09   8.91   53532.27   \n",
-      "2875  57562  600512.SH 2025-05-23  14.77  14.63  14.90  14.63  174083.00   \n",
+      "2904  87098  000931.SZ 2025-05-30   8.02   7.96   8.13   7.92  170963.38   \n",
+      "2903  87001  605122.SH 2025-05-30  16.86  16.73  17.11  16.68   20146.40   \n",
+      "2902  87041  002942.SZ 2025-05-30  24.10  23.95  24.34  23.84   10821.00   \n",
+      "2901  87122  603170.SH 2025-05-30  14.69  14.34  14.83  14.32   26536.80   \n",
+      "2900  86987  603177.SH 2025-05-30   8.72   8.55   8.77   8.49   25875.00   \n",
       "\n",
       "      pct_chg     amount  ...  000905.SH_up_ratio_20d  399006.SZ_up_ratio_20d  \\\n",
-      "4        1.04  14051.784  ...                    0.30                     0.4   \n",
-      "3        1.75  18967.667  ...                    0.30                     0.4   \n",
-      "2        1.66  18552.683  ...                    0.30                     0.4   \n",
-      "1        1.61   5417.565  ...                    0.30                     0.4   \n",
-      "0        0.73  20011.747  ...                    0.30                     0.4   \n",
+      "4        3.12  23729.134  ...                     0.3                    0.40   \n",
+      "3        0.73  20011.747  ...                     0.3                    0.40   \n",
+      "2        2.08  19174.296  ...                     0.3                    0.40   \n",
+      "1        1.65  53043.254  ...                     0.3                    0.40   \n",
+      "0        1.42  12366.664  ...                     0.3                    0.40   \n",
       "...       ...        ...  ...                     ...                     ...   \n",
-      "2879    -0.80  28469.420  ...                    0.65                     0.5   \n",
-      "2878    -1.36  35739.005  ...                    0.65                     0.5   \n",
-      "2877    -1.43  30081.794  ...                    0.65                     0.5   \n",
-      "2876    -1.00  22364.522  ...                    0.65                     0.5   \n",
-      "2875    -0.95  39001.845  ...                    0.65                     0.5   \n",
+      "2904    -0.38  89186.130  ...                     0.6                    0.45   \n",
+      "2903    -1.18  23723.010  ...                     0.6                    0.45   \n",
+      "2902    -1.07  18204.020  ...                     0.6                    0.45   \n",
+      "2901    -2.45  36665.356  ...                     0.6                    0.45   \n",
+      "2900    -1.38  21916.553  ...                     0.6                    0.45   \n",
       "\n",
       "      000852.SH_volatility  000905.SH_volatility  399006.SZ_volatility  \\\n",
       "4                 1.036997              0.828596              0.935322   \n",
@@ -2042,11 +1921,11 @@
       "1                 1.036997              0.828596              0.935322   \n",
       "0                 1.036997              0.828596              0.935322   \n",
       "...                    ...                   ...                   ...   \n",
-      "2879              1.046977              0.774609              1.145066   \n",
-      "2878              1.046977              0.774609              1.145066   \n",
-      "2877              1.046977              0.774609              1.145066   \n",
-      "2876              1.046977              0.774609              1.145066   \n",
-      "2875              1.046977              0.774609              1.145066   \n",
+      "2904              1.089861              0.850444              1.195355   \n",
+      "2903              1.089861              0.850444              1.195355   \n",
+      "2902              1.089861              0.850444              1.195355   \n",
+      "2901              1.089861              0.850444              1.195355   \n",
+      "2900              1.089861              0.850444              1.195355   \n",
       "\n",
       "      000852.SH_volume_change_rate  000905.SH_volume_change_rate  \\\n",
       "4                         5.203088                     -0.750721   \n",
@@ -2055,26 +1934,26 @@
       "1                         5.203088                     -0.750721   \n",
       "0                         5.203088                     -0.750721   \n",
       "...                            ...                           ...   \n",
-      "2879                     -6.696588                    -13.318993   \n",
-      "2878                     -6.696588                    -13.318993   \n",
-      "2877                     -6.696588                    -13.318993   \n",
-      "2876                     -6.696588                    -13.318993   \n",
-      "2875                     -6.696588                    -13.318993   \n",
+      "2904                     -2.039466                    -12.002493   \n",
+      "2903                     -2.039466                    -12.002493   \n",
+      "2902                     -2.039466                    -12.002493   \n",
+      "2901                     -2.039466                    -12.002493   \n",
+      "2900                     -2.039466                    -12.002493   \n",
       "\n",
       "      399006.SZ_volume_change_rate     score  score_ranks  \n",
-      "4                         8.827360  0.458319        996.0  \n",
-      "3                         8.827360  0.460070        997.0  \n",
-      "2                         8.827360  0.483001        998.0  \n",
-      "1                         8.827360  0.484087        999.0  \n",
-      "0                         8.827360  0.514384       1000.0  \n",
+      "4                         8.827360  0.391351       1496.0  \n",
+      "3                         8.827360  0.410662       1497.0  \n",
+      "2                         8.827360  0.470817       1498.0  \n",
+      "1                         8.827360  0.567032       1499.0  \n",
+      "0                         8.827360  0.596280       1500.0  \n",
       "...                            ...       ...          ...  \n",
-      "2879                     -3.090311  0.889261        996.0  \n",
-      "2878                     -3.090311  0.901987        997.0  \n",
-      "2877                     -3.090311  0.904930        998.0  \n",
-      "2876                     -3.090311  0.925288        999.0  \n",
-      "2875                     -3.090311  0.945425       1000.0  \n",
+      "2904                      5.078672  0.707379       1490.0  \n",
+      "2903                      5.078672  0.769014       1491.0  \n",
+      "2902                      5.078672  0.874366       1492.0  \n",
+      "2901                      5.078672  0.975826       1493.0  \n",
+      "2900                      5.078672  1.072646       1494.0  \n",
       "\n",
-      "[2880 rows x 241 columns]\n",
+      "[2905 rows x 241 columns]\n",
       "\n",
       "验证成功：原始 DataFrame 和从 Redis 加载的 DataFrame 一致。\n",
       "\n",
@@ -2140,7 +2019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -2160,7 +2039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "bceabd1f",
    "metadata": {},
    "outputs": [
@@ -2168,10 +2047,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\n",
-      "\n",
-      "NDCG 结果\n",
-      "{'ndcg@1': 0.7346938775510204, 'ndcg@3': 0.6621840218042779, 'ndcg@5': 0.6564225278582314}\n"
+      "警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "`np.asfarray` was removed in the NumPy 2.0 release. Use `np.asarray` with a proper dtype instead.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[105]\u001b[39m\u001b[32m, line 52\u001b[39m\n\u001b[32m     48\u001b[39m     avg_ndcg = {k: np.mean(v) \u001b[38;5;28;01mif\u001b[39;00m v \u001b[38;5;28;01melse\u001b[39;00m np.nan \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m ndcg_scores.items()}\n\u001b[32m     49\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m avg_ndcg\n\u001b[32m---> \u001b[39m\u001b[32m52\u001b[39m ndcg_results_single_group = \u001b[43mcalculate_ndcg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscore_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscore_col\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mscore\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_col\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mlabel\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk_values\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m5\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup_id\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m     53\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mNDCG 结果\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     54\u001b[39m \u001b[38;5;28mprint\u001b[39m(ndcg_results_single_group)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[105]\u001b[39m\u001b[32m, line 40\u001b[39m, in \u001b[36mcalculate_ndcg\u001b[39m\u001b[34m(df, score_col, label_col, group_id, k_values)\u001b[39m\n\u001b[32m     38\u001b[39m     relevant_labels = group_df[label_col].values\n\u001b[32m     39\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m k_values:\n\u001b[32m---> \u001b[39m\u001b[32m40\u001b[39m         ndcg_scores[\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33mndcg@\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m].append(\u001b[43mndcg_at_k\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrelevant_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m     41\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m     42\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m _, group_df \u001b[38;5;129;01min\u001b[39;00m df.groupby(group_id):\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[105]\u001b[39m\u001b[32m, line 27\u001b[39m, in \u001b[36mcalculate_ndcg.<locals>.ndcg_at_k\u001b[39m\u001b[34m(r, k)\u001b[39m\n\u001b[32m     26\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mndcg_at_k\u001b[39m(r, k):\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m     dcg_max = \u001b[43mdcg_at_k\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreverse\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     28\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dcg_max:\n\u001b[32m     29\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[32m0.\u001b[39m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[105]\u001b[39m\u001b[32m, line 23\u001b[39m, in \u001b[36mcalculate_ndcg.<locals>.dcg_at_k\u001b[39m\u001b[34m(r, k)\u001b[39m\n\u001b[32m     22\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdcg_at_k\u001b[39m(r, k):\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m     r = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43masfarray\u001b[49m(r)[:k] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(r) > \u001b[32m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m np.zeros(k)\n\u001b[32m     24\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m np.sum(r / np.log2(np.arange(\u001b[32m2\u001b[39m, r.size + \u001b[32m2\u001b[39m)))\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/numpy/__init__.py:400\u001b[39m, in \u001b[36m__getattr__\u001b[39m\u001b[34m(attr)\u001b[39m\n\u001b[32m    397\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(__former_attrs__[attr], name=\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m    399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __expired_attributes__:\n\u001b[32m--> \u001b[39m\u001b[32m400\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[32m    401\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m`np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m` was removed in the NumPy 2.0 release. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    402\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m__expired_attributes__[attr]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m,\n\u001b[32m    403\u001b[39m         name=\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    404\u001b[39m     )\n\u001b[32m    406\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attr == \u001b[33m\"\u001b[39m\u001b[33mchararray\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m    407\u001b[39m     warnings.warn(\n\u001b[32m    408\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33m`np.chararray` is deprecated and will be removed from \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    409\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mthe main namespace in the future. Use an array with a string \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    410\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mor bytes dtype instead.\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel=\u001b[32m2\u001b[39m)\n",
+      "\u001b[31mAttributeError\u001b[39m: `np.asfarray` was removed in the NumPy 2.0 release. Use `np.asarray` with a proper dtype instead."
      ]
     }
    ],
@@ -2234,7 +2125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "44f64679",
    "metadata": {},
    "outputs": [
@@ -2331,7 +2222,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "new_trader",
+   "display_name": "stock",
    "language": "python",
    "name": "python3"
   },
@@ -2345,7 +2236,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,
diff --git a/main/train/Regression2.ipynb b/main/train/Regression2.ipynb
index b2304ef..07bf910 100644
--- a/main/train/Regression2.ipynb
+++ b/main/train/Regression2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "79a7758178bafdd3",
    "metadata": {
     "ExecuteTime": {
@@ -18,7 +18,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "e:\\PyProject\\NewStock\\main\\train\n"
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n",
+      "/mnt/d/PyProject/NewStock\n"
      ]
     }
    ],
@@ -29,7 +31,7 @@
     "import gc\n",
     "import os\n",
     "import sys\n",
-    "sys.path.append('../../')\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",
@@ -44,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -68,7 +70,7 @@
       "cyq perf\n",
       "left merge on ['ts_code', 'trade_date']\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 8665405 entries, 0 to 8665404\n",
+      "RangeIndex: 8692146 entries, 0 to 8692145\n",
       "Data columns (total 32 columns):\n",
       " #   Column         Dtype         \n",
       "---  ------         -----         \n",
@@ -114,26 +116,26 @@
     "from main.utils.utils import read_and_merge_h5_data\n",
     "\n",
     "print('daily data')\n",
-    "df = read_and_merge_h5_data('../../data/daily_data.h5', key='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', 'pct_chg'],\n",
     "                            df=None)\n",
     "\n",
     "print('daily basic')\n",
-    "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='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('../../data/stk_limit.h5', key='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('../../data/money_flow.h5', key='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('../../data/cyq_perf.h5', key='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",
@@ -143,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -162,7 +164,7 @@
    ],
    "source": [
     "print('industry')\n",
-    "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\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",
@@ -211,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -304,14 +306,14 @@
     "\n",
     "\n",
     "# 使用函数\n",
-    "h5_filename = '../../data/index_data.h5'\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": 5,
+   "execution_count": 7,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -327,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -389,12 +391,12 @@
     "    return industry_data\n",
     "\n",
     "\n",
-    "industry_df = read_industry_data('../../data/sw_daily.h5')\n"
+    "industry_df = read_industry_data('/mnt/d/PyProject/NewStock/data/sw_daily.h5')\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -422,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -432,16 +434,16 @@
    },
    "outputs": [],
    "source": [
-    "fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n",
+    "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'],\n",
     "                            df=None)\n",
-    "cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\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('../../data/balancesheet.h5', key='balancesheet',\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('../../data/top_list.h5', key='top_list',\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",
@@ -450,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -580,9 +582,9 @@
       "Calculating cs_rank_size...\n",
       "Finished cs_rank_size.\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 4539678 entries, 0 to 4539677\n",
+      "RangeIndex: 4554725 entries, 0 to 4554724\n",
       "Columns: 178 entries, ts_code to cs_rank_size\n",
-      "dtypes: bool(10), datetime64[ns](1), float64(162), int32(3), object(2)\n",
+      "dtypes: bool(10), datetime64[ns](1), float64(162), int64(3), object(2)\n",
       "memory usage: 5.7+ GB\n",
       "None\n",
       "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', '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', '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"
@@ -682,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "b87b938028afa206",
    "metadata": {
     "ExecuteTime": {
@@ -720,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "f4f16d63ad18d1bc",
    "metadata": {
     "ExecuteTime": {
@@ -966,7 +968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "40e6b68a91b30c79",
    "metadata": {
     "ExecuteTime": {
@@ -1293,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 15,
    "id": "47c12bb34062ae7a",
    "metadata": {
     "ExecuteTime": {
@@ -1341,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "8f4dc587",
    "metadata": {},
    "outputs": [
@@ -1349,85 +1351,72 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "          ts_code trade_date   open  close   high    low        vol  pct_chg  \\\n",
-      "976171  002193.SZ 2019-01-02  18.02  17.82  18.21  17.73   22513.62    -1.00   \n",
-      "976172  002193.SZ 2019-01-03  17.82  17.78  17.94  17.65   19523.21    -0.22   \n",
-      "976173  002193.SZ 2019-01-04  17.47  18.04  18.10  17.34   28094.69     1.46   \n",
-      "976174  002193.SZ 2019-01-07  18.17  18.31  18.39  18.00   23866.88     1.50   \n",
-      "976175  002193.SZ 2019-01-08  18.25  18.52  18.66  18.13   22853.46     1.15   \n",
-      "...           ...        ...    ...    ...    ...    ...        ...      ...   \n",
-      "977704  002193.SZ 2025-05-19  11.74  11.95  11.97  11.61   94249.32     2.49   \n",
-      "977705  002193.SZ 2025-05-20  11.99  12.20  12.22  11.84  103040.57     2.09   \n",
-      "977706  002193.SZ 2025-05-21  12.20  11.97  12.28  11.82   83112.00    -1.89   \n",
-      "977707  002193.SZ 2025-05-22  11.95  11.80  12.17  11.72   88811.00    -1.42   \n",
-      "977708  002193.SZ 2025-05-23  11.74  11.51  11.90  11.51   93013.00    -2.46   \n",
+      "          ts_code trade_date   open  close   high    low       vol  pct_chg  \\\n",
+      "979441  002193.SZ 2019-01-02  18.02  17.82  18.21  17.73  22513.62    -1.00   \n",
+      "979442  002193.SZ 2019-01-03  17.82  17.78  17.94  17.65  19523.21    -0.22   \n",
+      "979443  002193.SZ 2019-01-04  17.47  18.04  18.10  17.34  28094.69     1.46   \n",
+      "979444  002193.SZ 2019-01-07  18.17  18.31  18.39  18.00  23866.88     1.50   \n",
+      "979445  002193.SZ 2019-01-08  18.25  18.52  18.66  18.13  22853.46     1.15   \n",
+      "...           ...        ...    ...    ...    ...    ...       ...      ...   \n",
+      "980979  002193.SZ 2025-05-26  11.57  11.70  11.76  11.45  59545.98     1.65   \n",
+      "980980  002193.SZ 2025-05-27  11.70  11.80  11.93  11.55  64820.00     0.85   \n",
+      "980981  002193.SZ 2025-05-28  11.82  11.66  11.95  11.55  59952.42    -1.19   \n",
+      "980982  002193.SZ 2025-05-29  11.68  11.86  11.93  11.55  87290.42     1.72   \n",
+      "980983  002193.SZ 2025-05-30  11.86  11.76  12.01  11.70  82645.94    -0.84   \n",
       "\n",
-      "        turnover_rate   pe_ttm  ...  cs_rank_lg_flow_price_concordance  \\\n",
-      "976171         0.9771  23.0129  ...                           0.621118   \n",
-      "976172         0.8473  22.9598  ...                           0.230095   \n",
-      "976173         1.2193  23.3049  ...                           0.507119   \n",
-      "976174         1.0358  23.6499  ...                           0.315078   \n",
-      "976175         0.9918  23.9154  ...                           0.248631   \n",
-      "...               ...      ...  ...                                ...   \n",
-      "977704         3.6012      NaN  ...                           0.224402   \n",
-      "977705         3.9371      NaN  ...                           0.124834   \n",
-      "977706         3.1757      NaN  ...                           0.803720   \n",
-      "977707         3.3934      NaN  ...                           0.667220   \n",
-      "977708         3.5540      NaN  ...                           0.308867   \n",
+      "        turnover_rate   pe_ttm  ...  cs_rank_turnover_per_winner  \\\n",
+      "979441         0.9771  23.0129  ...                     0.990869   \n",
+      "979442         0.8473  22.9598  ...                     0.934283   \n",
+      "979443         1.2193  23.3049  ...                     0.925182   \n",
+      "979444         1.0358  23.6499  ...                     0.843796   \n",
+      "979445         0.9918  23.9154  ...                     0.834672   \n",
+      "...               ...      ...  ...                          ...   \n",
+      "980979         2.2752      NaN  ...                     0.435548   \n",
+      "980980         2.4767      NaN  ...                     0.484214   \n",
+      "980981         2.2908      NaN  ...                     0.438352   \n",
+      "980982         3.3353      NaN  ...                     0.583250   \n",
+      "980983         3.1579      NaN  ...                     0.491362   \n",
       "\n",
-      "        cs_rank_turnover_per_winner  cs_rank_ind_cap_neutral_pe  \\\n",
-      "976171                     0.990869                         NaN   \n",
-      "976172                     0.934283                         NaN   \n",
-      "976173                     0.925182                         NaN   \n",
-      "976174                     0.843796                         NaN   \n",
-      "976175                     0.834672                         NaN   \n",
-      "...                             ...                         ...   \n",
-      "977704                     0.624335                         NaN   \n",
-      "977705                     0.641102                         NaN   \n",
-      "977706                     0.624709                         NaN   \n",
-      "977707                     0.563268                         NaN   \n",
-      "977708                     0.661242                         NaN   \n",
+      "        cs_rank_ind_cap_neutral_pe  cs_rank_volume_ratio  \\\n",
+      "979441                         NaN              0.710344   \n",
+      "979442                         NaN              0.444444   \n",
+      "979443                         NaN              0.489226   \n",
+      "979444                         NaN              0.250000   \n",
+      "979445                         NaN              0.510588   \n",
+      "...                            ...                   ...   \n",
+      "980979                         NaN              0.284385   \n",
+      "980980                         NaN              0.421735   \n",
+      "980981                         NaN              0.466434   \n",
+      "980982                         NaN              0.671486   \n",
+      "980983                         NaN              0.684385   \n",
       "\n",
-      "        cs_rank_volume_ratio  cs_rank_elg_buy_sell_sm_ratio  \\\n",
-      "976171              0.710344                       0.341855   \n",
-      "976172              0.444444                       0.318912   \n",
-      "976173              0.489226                       0.260036   \n",
-      "976174              0.250000                       0.251095   \n",
-      "976175              0.510588                       0.286679   \n",
-      "...                      ...                            ...   \n",
-      "977704              0.234043                       0.397274   \n",
-      "977705              0.344124                       0.116534   \n",
-      "977706              0.221189                       0.126370   \n",
-      "977707              0.412155                       0.130521   \n",
-      "977708              0.534540                       0.134175   \n",
+      "        cs_rank_elg_buy_sell_sm_ratio  cs_rank_cost_dist_vol_ratio  \\\n",
+      "979441                       0.341855                     0.261603   \n",
+      "979442                       0.318912                     0.185342   \n",
+      "979443                       0.260036                     0.211959   \n",
+      "979444                       0.251095                     0.145266   \n",
+      "979445                       0.286679                     0.202299   \n",
+      "...                               ...                          ...   \n",
+      "980979                       0.153987                     0.327907   \n",
+      "980980                       0.156198                     0.377202   \n",
+      "980981                       0.153373                     0.362579   \n",
+      "980982                       0.126122                     0.490861   \n",
+      "980983                       0.441528                     0.486047   \n",
       "\n",
-      "        cs_rank_cost_dist_vol_ratio  cs_rank_size  future_return  \\\n",
-      "976171                     0.261603      0.262235       0.033658   \n",
-      "976172                     0.185342      0.264695       0.029373   \n",
-      "976173                     0.211959      0.259489       0.025179   \n",
-      "976174                     0.145266      0.255474       0.014638   \n",
-      "976175                     0.202299      0.259854       0.003235   \n",
-      "...                             ...           ...            ...   \n",
-      "977704                     0.299535      0.031250            NaN   \n",
-      "977705                     0.339641      0.032205            NaN   \n",
-      "977706                     0.291597      0.030555            NaN   \n",
-      "977707                     0.372634      0.030555            NaN   \n",
-      "977708                     0.430090      0.027898            NaN   \n",
+      "        cs_rank_size  future_return  future_return_2  cat_up_limit  label  \n",
+      "979441      0.262235       0.033658         0.012270         False    8.0  \n",
+      "979442      0.264695       0.029373         0.029373         False    7.0  \n",
+      "979443      0.259489       0.025179         0.026260         False   10.0  \n",
+      "979444      0.255474       0.014638         0.006532         False   13.0  \n",
+      "979445      0.259854       0.003235        -0.011404         False    6.0  \n",
+      "...              ...            ...              ...           ...    ...  \n",
+      "980979      0.028904            NaN        -0.003425         False    NaN  \n",
+      "980980      0.028913            NaN         0.005072         False    NaN  \n",
+      "980981      0.028913            NaN         0.008540         False    NaN  \n",
+      "980982      0.029246            NaN              NaN         False    NaN  \n",
+      "980983      0.029568            NaN              NaN         False    NaN  \n",
       "\n",
-      "        cat_up_limit  label  \n",
-      "976171         False    8.0  \n",
-      "976172         False    7.0  \n",
-      "976173         False   10.0  \n",
-      "976174         False   13.0  \n",
-      "976175         False    6.0  \n",
-      "...              ...    ...  \n",
-      "977704         False    NaN  \n",
-      "977705         False    NaN  \n",
-      "977706         False    NaN  \n",
-      "977707         False    NaN  \n",
-      "977708         False    NaN  \n",
-      "\n",
-      "[1538 rows x 181 columns]\n"
+      "[1543 rows x 182 columns]\n"
      ]
     }
    ],
@@ -1437,7 +1426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "29221dde",
    "metadata": {},
    "outputs": [
@@ -1484,7 +1473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "03ee5daf",
    "metadata": {},
    "outputs": [],
@@ -1497,7 +1486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 19,
    "id": "b76ea08a",
    "metadata": {},
    "outputs": [
@@ -1518,7 +1507,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 132/132 [00:28<00:00,  4.57it/s]\n"
+      "MAD Filtering: 100%|██████████| 132/132 [00:14<00:00,  9.17it/s]\n"
      ]
     },
     {
@@ -1533,7 +1522,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 132/132 [00:24<00:00,  5.46it/s]\n"
+      "MAD Filtering: 100%|██████████| 132/132 [00:12<00:00, 10.64it/s]\n"
      ]
     },
     {
@@ -1573,13 +1562,13 @@
       "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', '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', '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']\n",
       "df最小日期: 2019-01-02\n",
-      "df最大日期: 2025-05-23\n",
-      "2057539\n",
+      "df最大日期: 2025-05-30\n",
+      "2057465\n",
       "train_data最小日期: 2020-01-02\n",
       "train_data最大日期: 2022-12-30\n",
-      "1766694\n",
+      "1781706\n",
       "test_data最小日期: 2023-01-03\n",
-      "test_data最大日期: 2025-05-23\n",
+      "test_data最大日期: 2025-05-30\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",
@@ -1715,7 +1704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 20,
    "id": "2e4b027e",
    "metadata": {},
    "outputs": [],
@@ -1765,7 +1754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 23,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1918,8 +1907,8 @@
     "            valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n",
     "            callbacks=callbacks\n",
     "        )\n",
-    "        from flaml import AutoML\n",
-    "        from sklearn.datasets import fetch_california_housing\n",
+    "        # from flaml import AutoML\n",
+    "        # from sklearn.datasets import fetch_california_housing\n",
     "\n",
     "        # # Initialize an AutoML instance\n",
     "        # model = AutoML()\n",
@@ -1952,7 +1941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 24,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1983,13 +1972,13 @@
       "原始样本数: 728000, 去除标签为空后样本数: 728000\n",
       "Training until validation scores don't improve for 100 rounds\n",
       "Early stopping, best iteration is:\n",
-      "[58]\ttrain's rmse: 0.0373704\tvalid's rmse: 0.040525\n",
+      "[67]\ttrain's rmse: 0.0373319\tvalid's rmse: 0.0405067\n",
       "Evaluated only: rmse\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1999,7 +1988,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2027,7 +2016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 25,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -2098,7 +2087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 26,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -2118,7 +2107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 27,
    "id": "bceabd1f",
    "metadata": {},
    "outputs": [],
@@ -2129,7 +2118,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "new_trader",
+   "display_name": "stock",
    "language": "python",
    "name": "python3"
   },
@@ -2143,7 +2132,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,
diff --git a/predictions_test.tsv b/predictions_test.tsv
index 6f2225d..32ae634 100644
--- a/predictions_test.tsv
+++ b/predictions_test.tsv
@@ -1,1163 +1,1163 @@
 trade_date,score,ts_code
-2023-01-03,0.3758635540778809,002629.SZ
-2023-01-03,0.4181404741879983,002494.SZ
-2023-01-04,0.3849058159025331,600455.SH
-2023-01-04,0.5499583829102009,000691.SZ
-2023-01-05,0.35432533044808634,000691.SZ
-2023-01-05,0.3568405921894787,600455.SH
-2023-01-06,0.3579089669119505,000691.SZ
-2023-01-06,0.35927488120741496,002848.SZ
-2023-01-09,0.34937377880419945,002848.SZ
-2023-01-09,0.4510804894374975,603499.SH
-2023-01-10,0.41782268793955385,002848.SZ
-2023-01-10,0.44992631915939657,603499.SH
-2023-01-11,0.2870205103729656,000691.SZ
-2023-01-11,0.3482100183337843,002848.SZ
-2023-01-12,0.30751485126050593,002848.SZ
-2023-01-12,0.3444243228036661,000023.SZ
-2023-01-13,0.3273606902439397,000691.SZ
-2023-01-13,0.35157127764866486,002848.SZ
-2023-01-16,0.30431901740439954,000691.SZ
-2023-01-16,0.3376059087607822,603316.SH
-2023-01-17,0.3131846964710339,002848.SZ
-2023-01-17,0.3537800087217845,603316.SH
-2023-01-18,0.35764777276248555,603316.SH
-2023-01-18,0.4162763566546959,002816.SZ
-2023-01-19,0.34172524264910736,000691.SZ
-2023-01-19,0.46479811110498515,600455.SH
-2023-01-20,0.3033269851044424,603030.SH
-2023-01-20,0.3335115016718172,600455.SH
-2023-01-30,0.3784537369692672,603030.SH
-2023-01-30,0.5003215481223962,002058.SZ
-2023-01-31,0.3939415646566767,600898.SH
-2023-01-31,0.5364575760357264,600768.SH
-2023-02-01,0.4792463413470344,002899.SZ
-2023-02-01,0.5236274593968457,600768.SH
-2023-02-02,0.4251048208318906,002830.SZ
-2023-02-02,0.4572846704375922,600768.SH
-2023-02-03,0.4283626950818823,002830.SZ
-2023-02-03,0.4902863214544424,600768.SH
-2023-02-06,0.42067340119019037,600355.SH
-2023-02-06,0.47047529853272035,002861.SZ
-2023-02-07,0.41742404228322083,600898.SH
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