diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json
index 77ce259..dae8ac9 100644
--- a/catboost_info/catboost_training.json
+++ b/catboost_info/catboost_training.json
@@ -1,832 +1,754 @@
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\ No newline at end of file
diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index 5b440bc..e22d595 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 29a165b..fca1eeb 100644
--- a/catboost_info/learn_error.tsv
+++ b/catboost_info/learn_error.tsv
@@ -1,829 +1,751 @@
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diff --git a/catboost_info/test/events.out.tfevents b/catboost_info/test/events.out.tfevents
index 1d19255..74ccb32 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 79bd556..4b691e7 100644
--- a/catboost_info/test_error.tsv
+++ b/catboost_info/test_error.tsv
@@ -1,829 +1,751 @@
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diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index 37ce9fe..0d49225 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,829 +1,751 @@
 iter	Passed	Remaining
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-1	276	207298
-2	439	219337
-3	561	209949
-4	719	215159
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diff --git a/main/factor/__pycache__/concept_factor.cpython-313.pyc b/main/factor/__pycache__/concept_factor.cpython-313.pyc
new file mode 100644
index 0000000..307b8d1
Binary files /dev/null and b/main/factor/__pycache__/concept_factor.cpython-313.pyc differ
diff --git a/main/factor/concept_factor.py b/main/factor/concept_factor.py
new file mode 100644
index 0000000..9df5eb4
--- /dev/null
+++ b/main/factor/concept_factor.py
@@ -0,0 +1,82 @@
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+def _prepare_concept_df(concept_dict: dict) -> pd.DataFrame:
+    """将 concept_dict 转换为长格式的 DataFrame。"""
+    records = []
+    for date_str, inner_dict in concept_dict.items():
+        trade_date = pd.to_datetime(date_str, format='%Y%m%d')
+        for concept_name, stock_list in inner_dict.items():
+            for ts_code in stock_list:
+                records.append((trade_date, concept_name, ts_code))
+    
+    if not records:
+        return pd.DataFrame(columns=['trade_date', 'concept_name', 'ts_code'])
+        
+    concept_df = pd.DataFrame(records, columns=['trade_date', 'concept_name', 'ts_code'])
+    concept_df = concept_df.drop_duplicates(subset=["trade_date", "ts_code"], keep="first")
+
+    return concept_df
+
+def generate_concept_factors(df: pd.DataFrame, concept_dict: dict) -> pd.DataFrame:
+    """
+    基于热门概念数据生成因子。
+
+    Args:
+        df (pd.DataFrame): 所有股票所有日期的数据，需包含 'ts_code', 'trade_date'
+                           以及用于聚合的列 (如 'pct_chg', 'turnover_rate')。
+        concept_dict (dict): 每日热门概念及其股票列表的字典。
+
+    Returns:
+        pd.DataFrame: 添加了概念相关因子的原始 DataFrame。
+    """
+    print("开始生成概念相关因子...")
+    
+    # 0. 准备工作，创建副本以避免修改原始df
+    df = df.copy()
+    df['trade_date'] = pd.to_datetime(df['trade_date'])
+
+    # 1. 将 concept_dict 转换为适合合并的 DataFrame
+    concept_df = _prepare_concept_df(concept_dict).sort_values(by=['trade_date'])
+    if concept_df.empty:
+        print("警告: concept_dict 为空或格式不正确，无法生成概念因子。")
+        return df
+
+    # 2. 将概念信息合并到主数据 df
+    df = pd.merge(df, concept_df, on=['trade_date', 'ts_code'], how='left')
+    # --- 因子计算 ---
+
+    # 因子 1: 是否属于当日热门概念
+    df['cat_hot_concept_stock'] = df['concept_name'].notna().astype(np.int8)
+
+    # 因子 2 & 3: 概念内的截面排序因子
+    # 创建一个掩码，只对热门概念股进行后续计算，以提高效率
+    hot_mask = df['concept_name'].notna()
+    
+    # 定义需要在概念内部进行截面排序的特征列表
+    # 确保这些列存在于你的 df 中
+    features_to_rank = ['pct_chg', 'turnover_rate', 'volume_ratio'] 
+    
+    # 筛选出 df 中实际存在的特征列
+    existing_features_to_rank = [f for f in features_to_rank if f in df.columns]
+    if not existing_features_to_rank:
+        print("警告: df 中缺少用于概念内排序的特征列，跳过相关因子计算。")
+    else:
+        print(f"开始计算概念内截面排序因子，基于: {existing_features_to_rank}")
+        
+        # 使用 groupby().rank() 高效计算截面排名
+        grouped = df[hot_mask].groupby(['trade_date', 'concept_name'])
+        
+        for feature in tqdm(existing_features_to_rank, desc="Ranking Features in Concepts"):
+            # 计算百分比排名 (0到1之间)，值越大表示排名越靠前
+            rank_col_name = f'concept_rank_{feature}'
+            df[rank_col_name] = grouped[feature].rank(pct=True)
+
+    # --- 清理 & 返回 ---
+    # `concept_name` 列包含了有用的信息，可以选择保留或删除
+    # 这里我们选择保留，以便后续分析。如果不需要，可以取消下面这行注释。
+    df.drop(columns=['concept_name'], inplace=True)
+    
+    print("概念相关因子生成完毕。")
+    return df
\ No newline at end of file
diff --git a/main/factor/factor.csv b/main/factor/factor.csv
deleted file mode 100644
index 1cd1241..0000000
--- a/main/factor/factor.csv
+++ /dev/null
@@ -1,63 +0,0 @@
-序号	因子名称 (Factor Name / Column Name)	因子类别 (Factor Category)	简要说明
-1	pe_ttm	价值类因子 (Value)	市盈率 TTM
-2	return_5, return_20	动量类因子 (Momentum)	过去5日/20日收益率
-3	act_factor1 to act_factor4	动量类 / 技术类因子 (Momentum / Technical)	基于不同周期EMA斜率计算的动量/趋势因子
-4	std_return_5, std_return_90, std_return_90_2	波动率类因子 (Volatility)	不同窗口期或延迟窗口期的滚动收益率标准差
-5	upside_vol, downside_vol	波动率类因子 (Volatility)	N日滚动上/下行波动率
-6	vol_ratio	波动率类因子 (Volatility)	上行波动率 / 下行波动率
-7	std_return_5 / std_return_90	波动率类因子 (Volatility)	短期波动率 / 长期波动率 比率
-8	std_return_90 - std_return_90_2	波动率类因子 (Volatility)	长期波动率与其10日前值的差值（波动变化）
-9	volatility (来自指数计算)	波动率类 / 市场因子 (Volatility / Market)	指数（或个股）的20日滚动收益率标准差
-10	log(circ_mv) (或 log_circ_mv)	市值类因子 (Size)	流通市值的对数值
-11	cs_rank_size	市值类因子 (Size)	对数流通市值的截面排序
-12	vol	流动性类因子 (Liquidity)	成交量 (通常需要与其他指标结合或处理)
-13	turnover_rate	流动性类因子 (Liquidity)	换手率
-14	volume_ratio	流动性类因子 (Liquidity)	量比
-15	turnover_deviation	流动性类因子 (Liquidity)	换手率与其3日滚动均值的标准差倍数偏离
-16	cat_turnover_spike	流动性类 / 分类因子 (Liquidity / Categorical)	换手率是否显著高于近期均值
-17	volume_change_rate	流动性类因子 (Liquidity)	短期滚动成交量均值 / 长期滚动成交量均值 - 1
-18	cat_volume_breakout	流动性类 / 分类因子 (Liquidity / Categorical)	当日成交量是否大于过去5日最大成交量
-19	avg_volume_ratio	流动性类因子 (Liquidity)	3日滚动量比均值
-20	cat_volume_ratio_breakout	流动性类 / 分类因子 (Liquidity / Categorical)	当日量比是否大于过去5日最大量比
-21	vol_spike (Rolling Mean Vol)	流动性类因子 (Liquidity)	20日滚动成交量均值
-22	vol_std_5	流动性类 / 波动率因子 (Liquidity / Volatility)	成交量日变化率的5日滚动标准差
-23	volume_growth	流动性类因子 (Liquidity)	20日成交量变化率
-24	turnover_std	流动性类 / 波动率因子 (Liquidity / Volatility)	换手率的20日滚动标准差
-25	flow_lg_elg_intensity	资金流 / 流动性类因子 (Money Flow / Liquidity)	（大单+超大单）净买入量 / 总成交量
-26	flow_divergence_diff, flow_divergence_ratio	资金流 / 情绪类因子 (Money Flow / Sentiment)	散户与主力资金流的差异或比率
-27	lg_elg_buy_prop	资金流 / 流动性类因子 (Money Flow / Liquidity)	（大单+超大单）买入量 / 总买入量
-28	flow_struct_buy_change	资金流 / 流动性类因子 (Money Flow / Liquidity)	主力买入占比的日变化
-29	flow_lg_elg_accel	资金流 / 动量类因子 (Money Flow / Momentum)	主力资金流加速度
-30	active_buy_volume_large/big/small	资金流 / 流动性类因子 (Money Flow / Liquidity)	不同规模主动买入量 / 净流入量
-31	buy_lg/elg_vol_minus_sell_lg/elg_vol	资金流 / 流动性类因子 (Money Flow / Liquidity)	不同规模净买入量 / 总净流入量
-32	cs_rank_net_lg_flow_val, cs_rank_elg_buy_ratio, cs_rank_lg_sm_flow_diverge, cs_rank_elg_buy_sell_sm_ratio	资金流 / 复合因子 (截面排序)	各种资金流指标的截面排序
-33	cs_rank_ind_adj_lg_flow	资金流 / 复合因子 (行业调整+截面排序)	行业调整后的大单净流入截面排序
-34	chip_concentration_range, chip_skewness, cost_support_15pct_change, weight_roc5, cost_stability, ctrl_strength, low_cost_dev, asymmetry, cost_conc_std_N, profit_pressure, underwater_resistance, cs_rank_rel_profit_margin, cs_rank_cost_breadth, cs_rank_dist_to_upper_cost	定位类因子 (Positioning) / 技术类	基于持仓成本分布 (cost_*, weight_avg) 计算的各种指标及其截面排序
-35	winner_rate, cs_rank_winner_rate	定位类因子 (Positioning) / 技术类	获利盘比例及其截面排序
-36	floating_chip_proxy, price_cost_divergence, high_cost_break_days, liquidity_risk, lock_factor, cost_atr_adj, smallcap_concentration, cat_golden_resonance	定位类因子 (Positioning) / 复合因子	结合持仓成本与其他信息（价格、成交、波动率、市值）的复合指标
-37	cat_winner_price_zone	定位类 / 分类因子 (Positioning / Categorical)	基于成本和获利盘划分的区域类别
-38	flow_chip_consistency, profit_taking_vs_absorb, vol_amp_loss, vol_drop_profit_cnt, cost_break_confirm_cnt, vol_wgt_hist_pos, cs_rank_vol_x_profit_margin, cs_rank_cost_dist_vol_ratio	定位类因子 (Positioning) / 复合因子	进一步结合定位、资金流、量价的复杂交互因子
-39	return_skew, return_kurtosis	技术类 / 统计特征 (Technical / Stats)	滚动收益率的偏度与峰度
-40	rsi_3	技术类 / 动量类因子 (Technical / Momentum)	3日相对强弱指数
-41	obv, maobv_6, obv-maobv_6	技术类 / 量价因子 (Technical / Volume)	能量潮及其均线、差离
-42	atr_14, atr_6	技术类 / 波动率类因子 (Technical / Volatility)	平均真实波幅
-43	log_close	技术类 / 量价因子 (Technical / Price)	收盘价对数
-44	up, down	技术类 / 量价因子 (Technical / Price Action)	标准化上影线、下影线长度
-45	alpha_22_improved, alpha_003, alpha_007, alpha_013	技术类 / Alpha因子 (Technical / Alpha)	WorldQuant Alpha 因子实现
-46	atr_norm_channel_pos	技术类 / 量价因子 (Technical / Price Action)	ATR 标准化的价格通道位置
-47	turnover_diff_skew	技术类 / 流动性类 (Technical / Liquidity)	换手率变化率的偏度
-48	pullback_strong_N_M	技术类 / 动量类因子 (Technical / Momentum)	近期强势股的回调幅度
-49	vol_adj_roc	技术类 / 复合因子 (动量+波动率)	波动率调整后的 N 日变化率
-50	ar, br, arbr	情绪类 / 技术类因子 (Sentiment / Technical)	ARBR 人气意愿指标
-51	up_ratio_20d (来自指数计算)	情绪类 / 市场因子 (Sentiment / Market)	指数（或个股）过去20天上涨天数比例
-52	cat_up_limit, cat_down_limit, up_limit_count_10d, down_limit_count_10d, consecutive_up_limit	事件驱动 / 市场状态因子 (Event / Market State)	涨跌停相关状态和计数
-53	momentum_factor, resonance_factor	复合因子 (量价) (Composite - P/V)	基于量、价、换手率等的简单复合
-54	cat_af2, cat_af3, cat_af4	复合因子 / 分类因子 (Composite / Cat.)	act_factor 之间的比较
-55	act_factor5, act_factor6	复合因子 (技术类) (Composite - Technical)	act_factor 1-4 的组合
-56	mv_volatility, mv_growth, mv_turnover_ratio, mv_adjusted_volume, mv_weighted_turnover, nonlinear_mv_volume, mv_volume_ratio, mv_momentum	复合因子 (市值+流动性/量价)	考虑了市值影响的量价、流动性或动量指标
-57	cap_neutral_cost_metric (占位符)	复合因子 / Alpha因子 (占位符)	市值行业中性化的成本指标（需实现）
-58	hurst_exponent_flow (占位符)	资金流 / 统计因子 (占位符)	资金流的 Hurst 指数（需实现）
-59	intraday_lg_flow_corr_N (占位符)	复合因子 (价格行为+资金流) (占位符)	日内趋势与大单流相关性（需实现）
-60	industry_* (来自 industry_df)	行业因子 (Industry)	对应行业的各种指标（如行业收益率、行业动量等）
-61	*_deviation (来自 create_deviation_within_dates)	复合因子 (相对行业)	个股因子相对于行业均值的偏离
-62	complex_factor_gplearn_1	复合因子 (GP生成)	DEAP/GP 找到的因子表达式 1
\ No newline at end of file
diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb
index 3a1694a..d108428 100644
--- a/main/train/Classify2.ipynb
+++ b/main/train/Classify2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "79a7758178bafdd3",
    "metadata": {
     "ExecuteTime": {
@@ -18,8 +18,6 @@
      "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"
      ]
     }
@@ -47,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "4a481c60",
    "metadata": {},
    "outputs": [],
@@ -59,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -73,7 +71,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "daily data\n",
+      "daily data\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "daily basic\n",
       "inner merge on ['ts_code', 'trade_date']\n",
       "stk limit\n",
@@ -159,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -227,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -327,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -343,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -410,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -438,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -470,7 +474,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
+   "id": "823a4a88",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ 成功从 Redis Hash 'concept_stocks_daily_lists_pickle' 读取 1794 条每日概念股票数据。\n"
+     ]
+    }
+   ],
+   "source": [
+    "import redis\n",
+    "import pickle\n",
+    "from datetime import date, datetime\n",
+    "\n",
+    "# --- 配置 Redis 连接 ---\n",
+    "REDIS_HOST = '140.143.91.66'\n",
+    "REDIS_PORT = 6389\n",
+    "REDIS_DB = 0\n",
+    "\n",
+    "# --- 定义 Redis 键名 ---\n",
+    "HASH_KEY = \"concept_stocks_daily_lists_pickle\" # 区分之前的 JSON 版本\n",
+    "MAX_DATE_KEY = \"concept_stocks_max_date_pickle\" # 区分之前的 JSON 版本\n",
+    "\n",
+    "concept_dict = {}\n",
+    "\n",
+    "# --- 连接 Redis ---\n",
+    "try:\n",
+    "    r = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, password='Redis520102')\n",
+    "\n",
+    "    all_data_from_redis = r.hgetall(HASH_KEY) # 返回的是字典，键是字节，值是字节\n",
+    "    \n",
+    "    if all_data_from_redis:\n",
+    "        for date_bytes, stocks_bytes in all_data_from_redis.items(): # 将变量名改为 date_bytes 更清晰\n",
+    "            try:\n",
+    "                # *** 修正点：将日期字节解码为字符串 ***\n",
+    "                date_str = date_bytes.decode('utf-8') \n",
+    "                date_obj = datetime.strptime(date_str, '%Y%m%d').date()\n",
+    "                \n",
+    "                stocks_list = pickle.loads(stocks_bytes)\n",
+    "                concept_dict[date_obj] = stocks_list\n",
+    "            except (ValueError, pickle.UnpicklingError) as e:\n",
+    "                print(f\"⚠️ 警告: 解析 Redis 数据时出错 (日期键: '{date_bytes.decode('utf-8', errors='ignore')}')，跳过此条数据: {e}\") # 打印警告时也解码一下\n",
+    "        print(f\"✅ 成功从 Redis Hash '{HASH_KEY}' 读取 {len(concept_dict)} 条每日概念股票数据。\")\n",
+    "    else:\n",
+    "        print(f\"ℹ️ Redis Hash '{HASH_KEY}' 中没有找到任何数据。\")\n",
+    "\n",
+    "except redis.exceptions.ConnectionError as e:\n",
+    "    print(f\"❌ 错误: 无法连接到 Redis 服务器，请检查 Redis 是否正在运行或连接配置: {e}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"❌ 从 Redis 读取数据时发生未知错误: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -483,10 +544,39 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "使用 'ann_date' 作为财务数据生效日期。\n",
-      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
-      "使用 'ann_date' 作为财务数据生效日期。\n",
-      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
+      "4554725\n",
+      "开始生成概念相关因子...\n",
+      "开始计算概念内截面排序因子，基于: ['pct_chg', 'turnover_rate', 'volume_ratio']\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Ranking Features in Concepts: 100%|██████████| 3/3 [00:00<00:00, 15.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "概念相关因子生成完毕。\n",
+      "4554725\n",
+      "开始计算股东增减持因子...\n",
+      "警告: 'in_de' 列中存在未映射的值，可能导致 _direction 列出现NaN。\n",
+      "股东增减持因子计算完成。\n",
+      "Calculating cat_senti_mom_vol_spike...\n",
+      "Finished cat_senti_mom_vol_spike.\n",
+      "Calculating cat_senti_pre_breakout...\n",
+      "Calculating atr_10 as it's missing...\n",
+      "Calculating atr_40 as it's missing...\n",
+      "Finished cat_senti_pre_breakout.\n",
+      "计算因子 ts_turnover_rate_acceleration_5_20\n",
+      "计算因子 ts_vol_sustain_10_30\n",
+      "计算因子 cs_amount_outlier_10\n",
+      "计算因子 ts_ff_to_total_turnover_ratio\n",
+      "计算因子 ts_price_volume_trend_coherence_5_20\n",
+      "计算因子 ts_ff_turnover_rate_surge_10\n",
       "使用 'ann_date' 作为财务数据生效日期。\n",
       "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
       "使用 'ann_date' 作为财务数据生效日期。\n",
@@ -509,29 +599,14 @@
       "错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n",
       "错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n",
       "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n",
-      "       'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n",
-      "       'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n",
-      "       'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n",
-      "       'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n",
-      "       'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n",
-      "       'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe',\n",
-      "       'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n",
-      "       'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20',\n",
-      "       'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol',\n",
-      "       'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n",
-      "       'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n",
-      "       'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n",
-      "       'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n",
-      "       'floating_chip_proxy', 'cost_support_15pct_change',\n",
-      "       'cat_winner_price_zone', 'flow_chip_consistency',\n",
-      "       'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n",
-      "       'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n",
-      "       '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n",
-      "       'return_skew', 'return_kurtosis', 'volume_change_rate',\n",
+      "       'amount', 'pct_chg', 'turnover_rate',\n",
+      "       ...\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",
+      "      dtype='object', length=104)\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",
@@ -606,61 +681,88 @@
       "Finished cs_rank_size.\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 4554725 entries, 0 to 4554724\n",
-      "Columns: 181 entries, ts_code to cs_rank_size\n",
-      "dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(2)\n",
-      "memory usage: 5.8+ GB\n",
+      "Columns: 198 entries, ts_code to cs_rank_size\n",
+      "dtypes: bool(10), datetime64[ns](1), float64(176), int64(6), int8(1), object(4)\n",
+      "memory usage: 6.4+ GB\n",
       "None\n",
-      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
+      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'cat_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'holder_direction_score_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
      ]
     }
    ],
    "source": [
-    "\n",
     "import numpy as np\n",
     "from main.factor.factor import *\n",
-    "from main.factor.money_factor import *\n",
+    "from main.factor.money_factor import * \n",
+    "from main.factor.concept_factor import * \n",
     "\n",
     "\n",
     "def filter_data(df):\n",
     "    # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
-    "    df = df[~df['is_st']]\n",
-    "    df = df[~df['ts_code'].str.endswith('BJ')]\n",
-    "    df = df[~df['ts_code'].str.startswith('30')]\n",
-    "    df = df[~df['ts_code'].str.startswith('68')]\n",
-    "    df = df[~df['ts_code'].str.startswith('8')]\n",
-    "    df = df[df['trade_date'] >= '2019-01-01']\n",
-    "    if 'in_date' in df.columns:\n",
-    "        df = df.drop(columns=['in_date'])\n",
+    "    df = df[~df[\"is_st\"]]\n",
+    "    df = df[~df[\"ts_code\"].str.endswith(\"BJ\")]\n",
+    "    df = df[~df[\"ts_code\"].str.startswith(\"30\")]\n",
+    "    df = df[~df[\"ts_code\"].str.startswith(\"68\")]\n",
+    "    df = df[~df[\"ts_code\"].str.startswith(\"8\")]\n",
+    "    df = df[df[\"trade_date\"] >= \"2019-01-01\"]\n",
+    "    if \"in_date\" in df.columns:\n",
+    "        df = df.drop(columns=[\"in_date\"])\n",
     "    df = df.reset_index(drop=True)\n",
     "    return df\n",
     "\n",
+    "\n",
     "gc.collect()\n",
     "\n",
     "df = filter_data(df)\n",
-    "df = df.sort_values(by=['ts_code', 'trade_date'])\n",
+    "df = df.sort_values(by=[\"ts_code\", \"trade_date\"])\n",
     "\n",
     "# df = price_minus_deduction_price(df, n=120)\n",
     "# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n",
     "# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n",
     "# df = cat_reason(df, top_list_df)\n",
     "# df = cat_is_on_top_list(df, top_list_df)\n",
+    "print(len(df))\n",
+    "df = generate_concept_factors(df, concept_dict)\n",
+    "print(len(df))\n",
     "\n",
-    "# df = ts_turnover_rate_acceleration_5_20(df)\n",
-    "# df = ts_vol_sustain_10_30(df)\n",
+    "df = holder_trade_factors(df, stk_holdertrade_df)\n",
+    "\n",
+    "df = cat_senti_mom_vol_spike(\n",
+    "    df,\n",
+    "    return_period=3,\n",
+    "    return_threshold=0.03,  # 近3日涨幅超3%\n",
+    "    volume_ratio_threshold=1.3,\n",
+    "    current_pct_chg_min=0.0,  # 当日必须收红\n",
+    "    current_pct_chg_max=0.05,\n",
+    ")  # 当日涨幅不宜过大\n",
+    "\n",
+    "df = cat_senti_pre_breakout(\n",
+    "    df,\n",
+    "    atr_short_N=10,\n",
+    "    atr_long_M=40,\n",
+    "    vol_atrophy_N=10,\n",
+    "    vol_atrophy_M=40,\n",
+    "    price_stab_N=5,\n",
+    "    price_stab_threshold=0.06,\n",
+    "    current_pct_chg_min_signal=0.002,\n",
+    "    current_pct_chg_max_signal=0.05,\n",
+    "    volume_ratio_signal_threshold=1.1,\n",
+    ")\n",
+    "\n",
+    "df = ts_turnover_rate_acceleration_5_20(df)\n",
+    "df = ts_vol_sustain_10_30(df)\n",
     "# df = cs_turnover_rate_relative_strength_20(df)\n",
-    "# df = cs_amount_outlier_10(df)\n",
-    "# df = holder_trade_factors(stk_holdertrade_df, df)\n",
-    "\n",
-    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n",
-    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n",
-    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roa')\n",
-    "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roe')\n",
+    "df = cs_amount_outlier_10(df)\n",
+    "df = ts_ff_to_total_turnover_ratio(df)\n",
+    "df = ts_price_volume_trend_coherence_5_20(df)\n",
+    "# df = ts_turnover_rate_trend_strength_5(df)\n",
+    "df = ts_ff_turnover_rate_surge_10(df)\n",
     "\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col=\"undist_profit_ps\")\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col=\"ocfps\")\n",
     "calculate_arbr(df, N=26)\n",
-    "df['log_circ_mv'] = np.log(df['circ_mv'])\n",
+    "df[\"log_circ_mv\"] = np.log(df[\"circ_mv\"])\n",
     "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n",
     "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n",
-    "\n",
     "df = turnover_rate_n(df, n=5)\n",
     "df = variance_n(df, n=20)\n",
     "df = bbi_ratio_factor(df)\n",
@@ -669,8 +771,10 @@
     "df, _ = get_rolling_factor(df)\n",
     "df, _ = get_simple_factor(df)\n",
     "\n",
-    "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n",
-    "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n",
+    "df = calculate_strong_inflow_signal(df)\n",
+    "\n",
+    "df = df.rename(columns={\"l1_code\": \"cat_l1_code\"})\n",
+    "df = df.rename(columns={\"l2_code\": \"cat_l2_code\"})\n",
     "\n",
     "lg_flow_mom_corr(df, N=20, M=60)\n",
     "lg_flow_accel(df)\n",
@@ -691,7 +795,7 @@
     "\n",
     "cs_rank_net_lg_flow_val(df)\n",
     "cs_rank_flow_divergence(df)\n",
-    "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n",
+    "cs_rank_industry_adj_lg_flow(df)  # Needs cat_l2_code\n",
     "cs_rank_elg_buy_ratio(df)\n",
     "cs_rank_rel_profit_margin(df)\n",
     "cs_rank_cost_breadth(df)\n",
@@ -699,16 +803,16 @@
     "cs_rank_winner_rate(df)\n",
     "cs_rank_intraday_range(df)\n",
     "cs_rank_close_pos_in_range(df)\n",
-    "cs_rank_opening_gap(df) # Needs pre_close\n",
-    "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n",
+    "cs_rank_opening_gap(df)  # Needs pre_close\n",
+    "cs_rank_pos_in_hist_range(df)  # Needs his_low, his_high\n",
     "cs_rank_vol_x_profit_margin(df)\n",
     "cs_rank_lg_flow_price_concordance(df)\n",
     "cs_rank_turnover_per_winner(df)\n",
-    "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n",
-    "cs_rank_volume_ratio(df) # Needs volume_ratio\n",
+    "cs_rank_ind_cap_neutral_pe(df)  # Placeholder - needs external libraries\n",
+    "cs_rank_volume_ratio(df)  # Needs volume_ratio\n",
     "cs_rank_elg_buy_sell_sm_ratio(df)\n",
-    "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n",
-    "cs_rank_size(df) # Needs circ_mv\n",
+    "cs_rank_cost_dist_vol_ratio(df)  # Needs volume_ratio\n",
+    "cs_rank_size(df)  # Needs circ_mv\n",
     "\n",
     "\n",
     "# df = df.merge(index_data, on='trade_date', how='left')\n",
@@ -719,7 +823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "b87b938028afa206",
    "metadata": {
     "ExecuteTime": {
@@ -757,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "f4f16d63ad18d1bc",
    "metadata": {
     "ExecuteTime": {
@@ -983,7 +1087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "40e6b68a91b30c79",
    "metadata": {
     "ExecuteTime": {
@@ -1303,7 +1407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "47c12bb34062ae7a",
    "metadata": {
     "ExecuteTime": {
@@ -1337,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "29221dde",
    "metadata": {},
    "outputs": [
@@ -1345,7 +1449,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "191\n"
+      "209\n"
      ]
     }
    ],
@@ -1380,7 +1484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "id": "03ee5daf",
    "metadata": {},
    "outputs": [],
@@ -1393,7 +1497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "id": "b76ea08a",
    "metadata": {},
    "outputs": [
@@ -1405,7 +1509,7 @@
       "0  000001.SZ 2019-01-02    16.574219\n",
       "1  000001.SZ 2019-01-03    16.583965\n",
       "2  000001.SZ 2019-01-04    16.633371\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "去除极值\n",
       "开始截面 MAD 去极值处理 (k=3.0)...\n"
      ]
@@ -1414,7 +1518,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 131/131 [00:16<00:00,  7.78it/s]\n"
+      "MAD Filtering: 100%|██████████| 146/146 [00:19<00:00,  7.58it/s]\n"
      ]
     },
     {
@@ -1429,7 +1533,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 131/131 [00:11<00:00, 11.06it/s]\n"
+      "MAD Filtering: 100%|██████████| 146/146 [00:13<00:00, 10.96it/s]\n"
      ]
     },
     {
@@ -1467,7 +1571,7 @@
      "output_type": "stream",
      "text": [
       "截面 MAD 去极值处理完成。\n",
-      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "df最小日期: 2019-01-02\n",
       "df最大日期: 2025-05-30\n",
       "2057465\n",
@@ -1476,6 +1580,7 @@
       "1781706\n",
       "test_data最小日期: 2023-01-03\n",
       "test_data最大日期: 2025-05-30\n",
+      "cat_columns: ['cat_hot_concept_stock', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'cat_winner_price_zone', 'cat_is_positive', 'cat_volume_breakout', 'cat_turnover_spike', 'cat_volume_ratio_breakout', 'cat_vol_spike', 'cat_af2', 'cat_af3', 'cat_af4', 'cat_vol_break', 'cat_golden_resonance', 'cat_up_limit']\n",
       "     ts_code trade_date  log_circ_mv\n",
       "0  000001.SZ 2019-01-02    16.574219\n",
       "1  000001.SZ 2019-01-03    16.583965\n",
@@ -1602,6 +1707,7 @@
     "print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
     "\n",
     "cat_columns = [col for col in feature_columns if col.startswith('cat')]\n",
+    "print(f'cat_columns: {cat_columns}')\n",
     "for col in cat_columns:\n",
     "    train_data[col] = train_data[col].astype('category')\n",
     "    test_data[col] = test_data[col].astype('category')\n",
@@ -1611,7 +1717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 30,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1675,17 +1781,17 @@
     "        params = {\n",
     "            'loss_function': 'Logloss',  # 适用于二分类\n",
     "            'eval_metric': 'Logloss',  # 评估指标\n",
-    "            'iterations': 1500,\n",
+    "            'iterations': 750,\n",
     "            'learning_rate': 0.01,\n",
-    "            'depth': 10,  # 控制模型复杂度\n",
+    "            'depth': 8,  # 控制模型复杂度\n",
     "            'l2_leaf_reg': 50,  # L2 正则化\n",
     "            'verbose': 5000,\n",
-    "            'early_stopping_rounds': 300,\n",
+    "            # 'early_stopping_rounds': 300,\n",
     "            # 'od_type': 'Iter',  # Overfitting detector type\n",
     "            # 'od_wait': 300,     # Number of iterations to wait after the bes\n",
-    "            'one_hot_max_size': 50,\n",
+    "            # 'one_hot_max_size': 100,\n",
     "            'class_weights': [0.6, 1.2],\n",
-    "            'task_type': 'GPU',\n",
+    "            # 'task_type': 'GPU',\n",
     "            'has_time': True,\n",
     "            'random_seed': 7\n",
     "        }\n",
@@ -1752,7 +1858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 31,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1786,7 +1892,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "182bcb05602b45a586d931fa2bcb3dd1",
+       "model_id": "547eff15f2c842308d4306b1aa5e5705",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1801,16 +1907,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "0:\tlearn: 0.6887420\ttest: 0.6896418\tbest: 0.6896418 (0)\ttotal: 18ms\tremaining: 13.5s\n",
+      "749:\tlearn: 0.3565319\ttest: 0.5068883\tbest: 0.5054505 (576)\ttotal: 12.3s\tremaining: 0us\n",
+      "\n",
+      "bestTest = 0.5054505212\n",
+      "bestIteration = 576\n",
+      "\n",
+      "Shrink model to first 577 iterations.\n"
      ]
     }
    ],
@@ -1832,7 +1935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 32,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -1870,7 +1973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 33,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -1878,8 +1981,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "191\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "209\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "[]\n"
      ]
     }
@@ -1892,7 +1995,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 34,
+   "id": "31c4852d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model_path = 'my_catboost_model.cbm' # 通常以 .cbm 为扩展名\n",
+    "# # model.save_model(model_path)\n",
+    "# # print(f\"模型已保存到: {model_path}\")\n",
+    "\n",
+    "# loaded_model = CatBoostClassifier() # 或 CatBoostRegressor 等，取决于你保存的模型类型\n",
+    "# loaded_model.load_model(model_path)\n",
+    "# print(f\"模型已从 {model_path} 加载成功。\")\n",
+    "\n",
+    "# if type == 'cat':\n",
+    "#     score_df['score'] = loaded_model.predict_proba(score_df[feature_columns])[:, 1]\n",
+    "# elif type == 'light':\n",
+    "#     score_df['score'] = loaded_model.predict(score_df[feature_columns])\n",
+    "# score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n",
+    "\n",
+    "# score_df = score_df.groupby('trade_date', group_keys=False).apply(\n",
+    "#     lambda x: x[x['score'] >= x['score'].quantile(0.90)] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n",
+    "# ).reset_index(drop=True) # drop=True 避免添加旧索引列\n",
+    "# # save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1, 'score')).reset_index()\n",
+    "# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index()\n",
+    "# save_df = save_df.sort_values(['trade_date', 'score'])\n",
+    "# save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
    "id": "e53b209a",
    "metadata": {},
    "outputs": [
@@ -1925,7 +2058,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 36,
    "id": "364e821a",
    "metadata": {},
    "outputs": [],
@@ -2009,7 +2142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 37,
    "id": "1f6e6336",
    "metadata": {},
    "outputs": [
@@ -2023,26 +2156,32 @@
       "1e+05-1e+05\n",
       "1e+05-2e+05\n",
       "2e+05-2e+05\n",
-      "2e+05-2e+05\n",
+      "2e+05-2e+05\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "2e+05-2e+05\n",
       "2e+05-3e+05\n",
       "3e+05-3e+05\n",
       "二分类评估指标:\n",
-      "accuracy: 0.6543\n",
-      "precision: 0.4629\n",
-      "recall: 0.1754\n",
-      "f1: 0.2544\n",
-      "roc_auc: 0.6206\n",
+      "accuracy: 0.6597\n",
+      "precision: 0.4824\n",
+      "recall: 0.0535\n",
+      "f1: 0.0963\n",
+      "roc_auc: 0.6190\n",
       "fpr: (array of length 7484)\n",
       "tpr: (array of length 7484)\n",
       "thresholds: (array of length 7484)\n",
-      "score_return_correlation: -0.0293\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"
+      "score_return_correlation: -0.0515\n",
+      "mv_roc_auc: {'6e+04-9e+04': np.float64(0.6170032241642627), '9e+04-1e+05': np.float64(0.5933723849372385), '1e+05-1e+05': np.float64(0.5784531090723751), '1e+05-2e+05': np.float64(0.5759209014471692), '2e+05-2e+05': np.float64(0.6110322483330337), '2e+05-3e+05': np.float64(0.6014283834583336), '3e+05-3e+05': np.float64(0.58325233281493)}\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -2078,7 +2217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 38,
    "id": "7e9023cc",
    "metadata": {},
    "outputs": [],
@@ -2278,7 +2417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 39,
    "id": "a0000d75",
    "metadata": {},
    "outputs": [
@@ -2288,7 +2427,7 @@
      "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 17123 行用于分析。\n",
+      "原始数据 17430 行，移除 NaN 后剩余 17125 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",
@@ -2524,7 +2663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 40,
    "id": "a436dba4",
    "metadata": {},
    "outputs": [
diff --git a/main/train/Rank2.ipynb b/main/train/Rank2.ipynb
index 785c257..c74bf37 100644
--- a/main/train/Rank2.ipynb
+++ b/main/train/Rank2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "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"
      ]
     }
@@ -44,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -58,56 +60,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "daily data\n",
-      "daily basic\n",
-      "inner merge on ['ts_code', 'trade_date']\n",
-      "stk limit\n",
-      "left merge on ['ts_code', 'trade_date']\n",
-      "money flow\n",
-      "left merge on ['ts_code', 'trade_date']\n",
-      "cyq perf\n",
-      "left merge on ['ts_code', 'trade_date']\n",
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 8692146 entries, 0 to 8692145\n",
-      "Data columns (total 33 columns):\n",
-      " #   Column         Dtype         \n",
-      "---  ------         -----         \n",
-      " 0   ts_code        object        \n",
-      " 1   trade_date     datetime64[ns]\n",
-      " 2   open           float64       \n",
-      " 3   close          float64       \n",
-      " 4   high           float64       \n",
-      " 5   low            float64       \n",
-      " 6   vol            float64       \n",
-      " 7   pct_chg        float64       \n",
-      " 8   amount         float64       \n",
-      " 9   turnover_rate  float64       \n",
-      " 10  pe_ttm         float64       \n",
-      " 11  circ_mv        float64       \n",
-      " 12  total_mv       float64       \n",
-      " 13  volume_ratio   float64       \n",
-      " 14  is_st          bool          \n",
-      " 15  up_limit       float64       \n",
-      " 16  down_limit     float64       \n",
-      " 17  buy_sm_vol     float64       \n",
-      " 18  sell_sm_vol    float64       \n",
-      " 19  buy_lg_vol     float64       \n",
-      " 20  sell_lg_vol    float64       \n",
-      " 21  buy_elg_vol    float64       \n",
-      " 22  sell_elg_vol   float64       \n",
-      " 23  net_mf_vol     float64       \n",
-      " 24  his_low        float64       \n",
-      " 25  his_high       float64       \n",
-      " 26  cost_5pct      float64       \n",
-      " 27  cost_15pct     float64       \n",
-      " 28  cost_50pct     float64       \n",
-      " 29  cost_85pct     float64       \n",
-      " 30  cost_95pct     float64       \n",
-      " 31  weight_avg     float64       \n",
-      " 32  winner_rate    float64       \n",
-      "dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
-      "memory usage: 2.1+ GB\n",
-      "None\n"
+      "daily data\n"
      ]
     }
    ],
@@ -144,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -212,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -312,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -328,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -395,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -423,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -455,7 +408,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
+   "id": "ba5935c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ 成功从 Redis Hash 'concept_stocks_daily_lists_pickle' 读取 1794 条每日概念股票数据。\n"
+     ]
+    }
+   ],
+   "source": [
+    "import redis\n",
+    "import pickle\n",
+    "from datetime import date, datetime\n",
+    "\n",
+    "# --- 配置 Redis 连接 ---\n",
+    "REDIS_HOST = '140.143.91.66'\n",
+    "REDIS_PORT = 6389\n",
+    "REDIS_DB = 0\n",
+    "\n",
+    "# --- 定义 Redis 键名 ---\n",
+    "HASH_KEY = \"concept_stocks_daily_lists_pickle\" # 区分之前的 JSON 版本\n",
+    "MAX_DATE_KEY = \"concept_stocks_max_date_pickle\" # 区分之前的 JSON 版本\n",
+    "\n",
+    "concept_dict = {}\n",
+    "\n",
+    "# --- 连接 Redis ---\n",
+    "try:\n",
+    "    r = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, password='Redis520102')\n",
+    "\n",
+    "    all_data_from_redis = r.hgetall(HASH_KEY) # 返回的是字典，键是字节，值是字节\n",
+    "    \n",
+    "    if all_data_from_redis:\n",
+    "        for date_bytes, stocks_bytes in all_data_from_redis.items(): # 将变量名改为 date_bytes 更清晰\n",
+    "            try:\n",
+    "                # *** 修正点：将日期字节解码为字符串 ***\n",
+    "                date_str = date_bytes.decode('utf-8') \n",
+    "                date_obj = datetime.strptime(date_str, '%Y%m%d').date()\n",
+    "                \n",
+    "                stocks_list = pickle.loads(stocks_bytes)\n",
+    "                concept_dict[date_obj] = stocks_list\n",
+    "            except (ValueError, pickle.UnpicklingError) as e:\n",
+    "                print(f\"⚠️ 警告: 解析 Redis 数据时出错 (日期键: '{date_bytes.decode('utf-8', errors='ignore')}')，跳过此条数据: {e}\") # 打印警告时也解码一下\n",
+    "        print(f\"✅ 成功从 Redis Hash '{HASH_KEY}' 读取 {len(concept_dict)} 条每日概念股票数据。\")\n",
+    "    else:\n",
+    "        print(f\"ℹ️ Redis Hash '{HASH_KEY}' 中没有找到任何数据。\")\n",
+    "\n",
+    "except redis.exceptions.ConnectionError as e:\n",
+    "    print(f\"❌ 错误: 无法连接到 Redis 服务器，请检查 Redis 是否正在运行或连接配置: {e}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"❌ 从 Redis 读取数据时发生未知错误: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -468,6 +478,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "4554725\n",
+      "开始生成概念相关因子...\n",
+      "开始计算概念内截面排序因子，基于: ['pct_chg', 'turnover_rate', 'volume_ratio']\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Ranking Features in Concepts: 100%|██████████| 3/3 [00:00<00:00, 15.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "概念相关因子生成完毕。\n",
+      "4554725\n",
       "开始计算股东增减持因子...\n",
       "警告: 'in_de' 列中存在未映射的值，可能导致 _direction 列出现NaN。\n",
       "股东增减持因子计算完成。\n",
@@ -505,35 +533,12 @@
       "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', 'holder_net_change_sum_10d',\n",
-      "       'holder_increase_days_10d', 'holder_decrease_days_10d',\n",
-      "       'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d',\n",
-      "       'holder_direction_score_10d', '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",
+      "       'pct_chg', 'amount', 'turnover_rate',\n",
+      "       ...\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",
+      "      dtype='object', length=104)\n",
       "Calculating senti_strong_inflow...\n",
       "Finished senti_strong_inflow.\n",
       "Calculating lg_flow_mom_corr_20_60...\n",
@@ -610,11 +615,11 @@
       "Finished cs_rank_size.\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 4554725 entries, 0 to 4554724\n",
-      "Columns: 194 entries, ts_code to cs_rank_size\n",
-      "dtypes: bool(10), datetime64[ns](1), float64(173), int64(6), object(4)\n",
-      "memory usage: 6.3+ GB\n",
+      "Columns: 198 entries, ts_code to cs_rank_size\n",
+      "dtypes: bool(10), datetime64[ns](1), float64(176), int64(6), int8(1), object(4)\n",
+      "memory usage: 6.4+ GB\n",
       "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', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'holder_direction_score_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
+      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', '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', 'is_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'holder_direction_score_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
      ]
     }
    ],
@@ -622,6 +627,7 @@
     "import numpy as np\n",
     "from main.factor.factor import *\n",
     "from main.factor.money_factor import * \n",
+    "from main.factor.concept_factor import * \n",
     "\n",
     "\n",
     "def filter_data(df):\n",
@@ -648,6 +654,10 @@
     "# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n",
     "# df = cat_reason(df, top_list_df)\n",
     "# df = cat_is_on_top_list(df, top_list_df)\n",
+    "print(len(df))\n",
+    "df = generate_concept_factors(df, concept_dict)\n",
+    "print(len(df))\n",
+    "\n",
     "df = holder_trade_factors(df, stk_holdertrade_df)\n",
     "\n",
     "df = cat_senti_mom_vol_spike(\n",
@@ -747,7 +757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "b87b938028afa206",
    "metadata": {
     "ExecuteTime": {
@@ -785,580 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "f4f16d63ad18d1bc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-03T13:08:03.670700Z",
-     "start_time": "2025-04-03T13:08:03.665739Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import statsmodels.api as sm # 用于中性化回归\n",
-    "from tqdm import tqdm # 可选，用于显示进度条\n",
-    "\n",
-    "# --- 常量 ---\n",
-    "epsilon = 1e-10 # 防止除零\n",
-    "\n",
-    "# --- 1. 中位数去极值 (MAD) ---\n",
-    "\n",
-    "def cs_mad_filter(df: pd.DataFrame,\n",
-    "                  features: list,\n",
-    "                  k: float = 3.0,\n",
-    "                  scale_factor: float = 1.4826):\n",
-    "    \"\"\"\n",
-    "    对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n",
-    "\n",
-    "    方法: 对每日截面数据，计算 median 和 MAD，\n",
-    "          将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n",
-    "          替换为边界值 (Winsorization)。\n",
-    "          scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n",
-    "\n",
-    "    Args:\n",
-    "        df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n",
-    "        features (list): 需要处理的特征列名列表。\n",
-    "        k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n",
-    "        scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n",
-    "\n",
-    "    WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
-    "    \"\"\"\n",
-    "    print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n",
-    "    if not all(col in df.columns for col in features):\n",
-    "        missing = [col for col in features if col not in df.columns]\n",
-    "        print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n",
-    "        return\n",
-    "\n",
-    "    grouped = df.groupby('trade_date')\n",
-    "\n",
-    "    for col in tqdm(features, desc=\"MAD Filtering\"):\n",
-    "        try:\n",
-    "            # 计算截面中位数\n",
-    "            median = grouped[col].transform('median')\n",
-    "            # 计算截面 MAD (Median Absolute Deviation from Median)\n",
-    "            mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n",
-    "\n",
-    "            # 计算上下边界\n",
-    "            lower_bound = median - k * scale_factor * mad\n",
-    "            upper_bound = median + k * scale_factor * mad\n",
-    "\n",
-    "            # 原地应用 clip\n",
-    "            df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
-    "\n",
-    "        except KeyError:\n",
-    "            print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n",
-    "        except Exception as e:\n",
-    "            print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n",
-    "\n",
-    "    print(\"截面 MAD 去极值处理完成。\")\n",
-    "\n",
-    "\n",
-    "# --- 2. 行业市值中性化 ---\n",
-    "\n",
-    "def cs_neutralize_industry_cap(df: pd.DataFrame,\n",
-    "                               features: list,\n",
-    "                               industry_col: str = 'cat_l2_code',\n",
-    "                               market_cap_col: str = 'circ_mv'):\n",
-    "    \"\"\"\n",
-    "    对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n",
-    "    使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n",
-    "    将回归残差写回原特征列。\n",
-    "\n",
-    "    Args:\n",
-    "        df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列,\n",
-    "                           industry_col, market_cap_col。\n",
-    "        features (list): 需要处理的特征列名列表。\n",
-    "        industry_col (str): 行业分类列名。\n",
-    "        market_cap_col (str): 流通市值列名。\n",
-    "\n",
-    "    WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n",
-    "             计算量较大，可能耗时较长。\n",
-    "             需要安装 statsmodels 库 (pip install statsmodels)。\n",
-    "    \"\"\"\n",
-    "    print(\"开始截面行业市值中性化...\")\n",
-    "    required_cols = features + ['trade_date', industry_col, market_cap_col]\n",
-    "    if not all(col in df.columns for col in required_cols):\n",
-    "        missing = [col for col in required_cols if col not in df.columns]\n",
-    "        print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n",
-    "        return\n",
-    "\n",
-    "    # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n",
-    "    log_cap_col = '_log_market_cap'\n",
-    "    df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n",
-    "    # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n",
-    "    # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n",
-    "    # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理\n",
-    "\n",
-    "    dates = df['trade_date'].unique()\n",
-    "    all_residuals = [] # 用于收集所有日期的残差\n",
-    "\n",
-    "    for date in tqdm(dates, desc=\"Neutralizing\"):\n",
-    "        daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n",
-    "\n",
-    "        # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n",
-    "        X = daily_data[[log_cap_col]]\n",
-    "        X = sm.add_constant(X, prepend=True) # 添加常数项\n",
-    "        # 创建行业哑变量 (drop_first=True 避免共线性)\n",
-    "        industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n",
-    "        industry_dummies = industry_dummies.astype(int)\n",
-    "        X = pd.concat([X, industry_dummies], axis=1)\n",
-    "\n",
-    "        daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n",
-    "\n",
-    "        for col in features:\n",
-    "            Y = daily_data[col]\n",
-    "\n",
-    "            # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n",
-    "            valid_mask = Y.notna() & X.notna().all(axis=1)\n",
-    "            if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n",
-    "                print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n",
-    "                daily_residuals[col] = np.nan\n",
-    "                continue\n",
-    "\n",
-    "            Y_valid = Y[valid_mask]\n",
-    "            X_valid = X[valid_mask]\n",
-    "\n",
-    "            # 执行 OLS 回归\n",
-    "            try:\n",
-    "                model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n",
-    "                results = model.fit()\n",
-    "                # 将残差填回对应位置\n",
-    "                daily_residuals.loc[valid_mask, col] = results.resid\n",
-    "                daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n",
-    "            except Exception as e:\n",
-    "                print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n",
-    "                daily_residuals[col] = np.nan\n",
-    "                break\n",
-    "\n",
-    "        all_residuals.append(daily_residuals)\n",
-    "\n",
-    "    # 合并所有日期的残差结果\n",
-    "    if all_residuals:\n",
-    "        residuals_df = pd.concat(all_residuals)\n",
-    "        # 将残差结果更新回原始 df (原地修改)\n",
-    "        # 使用 update 比 merge 更适合基于索引的原地更新\n",
-    "        # 确保 residuals_df 的索引与 df 中对应部分一致\n",
-    "        df.update(residuals_df)\n",
-    "    else:\n",
-    "        print(\"没有有效的残差结果可以合并。\")\n",
-    "\n",
-    "\n",
-    "    # 清理临时列\n",
-    "    df.drop(columns=[log_cap_col], inplace=True)\n",
-    "    print(\"截面行业市值中性化完成。\")\n",
-    "\n",
-    "\n",
-    "# --- 3. Z-Score 标准化 ---\n",
-    "\n",
-    "def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n",
-    "    \"\"\"\n",
-    "    对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n",
-    "    方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n",
-    "\n",
-    "    Args:\n",
-    "        df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n",
-    "        features (list): 需要处理的特征列名列表。\n",
-    "        epsilon (float): 防止除以零的小常数。\n",
-    "\n",
-    "    WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
-    "    \"\"\"\n",
-    "    print(\"开始截面 Z-Score 标准化...\")\n",
-    "    if not all(col in df.columns for col in features):\n",
-    "        missing = [col for col in features if col not in df.columns]\n",
-    "        print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n",
-    "        return\n",
-    "\n",
-    "    grouped = df.groupby('trade_date')\n",
-    "\n",
-    "    for col in tqdm(features, desc=\"Standardizing\"):\n",
-    "        try:\n",
-    "            # 使用 transform 计算截面均值和标准差\n",
-    "            mean = grouped[col].transform('mean')\n",
-    "            std = grouped[col].transform('std')\n",
-    "\n",
-    "            # 计算 Z-Score 并原地赋值\n",
-    "            df[col] = (df[col] - mean) / (std + epsilon)\n",
-    "\n",
-    "        except KeyError:\n",
-    "            print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n",
-    "        except Exception as e:\n",
-    "            print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n",
-    "\n",
-    "    print(\"截面 Z-Score 标准化完成。\")\n",
-    "\n",
-    "def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n",
-    "    \"\"\"\n",
-    "    对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n",
-    "\n",
-    "    参数:\n",
-    "    df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。\n",
-    "    feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n",
-    "\n",
-    "    返回:\n",
-    "    pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n",
-    "    \"\"\"\n",
-    "    processed_df = df.copy() # 在副本上操作，保留原始数据\n",
-    "\n",
-    "    # 确保 trade_date 是 datetime 类型以便正确分组\n",
-    "    processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n",
-    "\n",
-    "    def _fill_daily_nan(group):\n",
-    "        # group 是某一个交易日的 DataFrame\n",
-    "\n",
-    "        # 遍历指定的特征列\n",
-    "        for feature_col in feature_columns:\n",
-    "            # 检查列是否存在于当前分组中\n",
-    "            if feature_col in group.columns:\n",
-    "                # 计算当日该特征的中位数\n",
-    "                median_val = group[feature_col].median()\n",
-    "\n",
-    "                # 使用当日中位数填充该特征列的 NaN 值\n",
-    "                # inplace=True 会直接修改 group DataFrame\n",
-    "                group[feature_col].fillna(median_val, inplace=True)\n",
-    "            # else:\n",
-    "            #     print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n",
-    "\n",
-    "        return group\n",
-    "\n",
-    "    # 按交易日期分组，并应用每日填充函数\n",
-    "    # group_keys=False 避免将分组键添加到结果索引中\n",
-    "    filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n",
-    "\n",
-    "    return filled_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "40e6b68a91b30c79",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-03T13:08:04.694262Z",
-     "start_time": "2025-04-03T13:08:03.694904Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "\n",
-    "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n",
-    "                                     log=True):\n",
-    "    if not (0 <= lower_percentile < upper_percentile <= 1):\n",
-    "        raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n",
-    "\n",
-    "    # Calculate lower and upper bounds based on percentiles\n",
-    "    lower_bound = label.quantile(lower_percentile)\n",
-    "    upper_bound = label.quantile(upper_percentile)\n",
-    "\n",
-    "    # Filter out values outside the bounds\n",
-    "    filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n",
-    "\n",
-    "    # Print the number of removed outliers\n",
-    "    if log:\n",
-    "        print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n",
-    "    return filtered_label\n",
-    "\n",
-    "\n",
-    "def calculate_risk_adjusted_target(df, days=5):\n",
-    "    df = df.sort_values(by=['ts_code', 'trade_date'])\n",
-    "\n",
-    "    df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n",
-    "    df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n",
-    "    df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n",
-    "\n",
-    "    df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n",
-    "        level=0, drop=True)\n",
-    "    sharpe_ratio = df['future_return'] * df['future_volatility']\n",
-    "    sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
-    "\n",
-    "    return sharpe_ratio\n",
-    "\n",
-    "\n",
-    "def calculate_score(df, days=5, lambda_param=1.0):\n",
-    "    def calculate_max_drawdown(prices):\n",
-    "        peak = prices.iloc[0]  # 初始化峰值\n",
-    "        max_drawdown = 0  # 初始化最大回撤\n",
-    "\n",
-    "        for price in prices:\n",
-    "            if price > peak:\n",
-    "                peak = price  # 更新峰值\n",
-    "            else:\n",
-    "                drawdown = (peak - price) / peak  # 计算当前回撤\n",
-    "                max_drawdown = max(max_drawdown, drawdown)  # 更新最大回撤\n",
-    "\n",
-    "        return max_drawdown\n",
-    "\n",
-    "    def compute_stock_score(stock_df):\n",
-    "        stock_df = stock_df.sort_values(by=['trade_date'])\n",
-    "        future_return = stock_df['future_return']\n",
-    "        # 使用已有的 pct_chg 字段计算波动率\n",
-    "        volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n",
-    "        max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n",
-    "        score = future_return - lambda_param * max_drawdown\n",
-    "        return score\n",
-    "\n",
-    "    # # 确保 DataFrame 按照股票代码和交易日期排序\n",
-    "    # df = df.sort_values(by=['ts_code', 'trade_date'])\n",
-    "\n",
-    "    # 对每个股票分别计算 score\n",
-    "    df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n",
-    "\n",
-    "    return df['score']\n",
-    "\n",
-    "\n",
-    "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n",
-    "    numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n",
-    "    if not numeric_features:\n",
-    "        raise ValueError(\"No numeric features found in the provided data.\")\n",
-    "\n",
-    "    corr_matrix = df[numeric_features].corr().abs()\n",
-    "    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
-    "    to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
-    "    remaining_features = [col for col in feature_columns if col not in to_drop\n",
-    "                          or 'act' in col or 'af' in col]\n",
-    "    return remaining_features\n",
-    "\n",
-    "\n",
-    "def cross_sectional_standardization(df, features):\n",
-    "    df_sorted = df.sort_values(by='trade_date')  # 按时间排序\n",
-    "    df_standardized = df_sorted.copy()\n",
-    "\n",
-    "    for date in df_sorted['trade_date'].unique():\n",
-    "        # 获取当前时间点的数据\n",
-    "        current_data = df_standardized[df_standardized['trade_date'] == date]\n",
-    "\n",
-    "        # 只对指定特征进行标准化\n",
-    "        scaler = StandardScaler()\n",
-    "        standardized_values = scaler.fit_transform(current_data[features])\n",
-    "\n",
-    "        # 将标准化结果重新赋值回去\n",
-    "        df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n",
-    "\n",
-    "    return df_standardized\n",
-    "\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "\n",
-    "def neutralize_manual_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n",
-    "    \"\"\"\n",
-    "    手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n",
-    "    对特征在行业内部进行市值中性化。\n",
-    "\n",
-    "    Args:\n",
-    "        df: 输入的 DataFrame，包含特征、行业分类和市值列。\n",
-    "        features: 需要进行中性化的特征列名列表。\n",
-    "        industry_col: 行业分类列的列名。\n",
-    "        mkt_cap_col: 市值列的列名。\n",
-    "\n",
-    "    Returns:\n",
-    "        中性化后的 DataFrame。\n",
-    "    \"\"\"\n",
-    "\n",
-    "    df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n",
-    "    df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n",
-    "    df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n",
-    "\n",
-    "    if df_cleaned.empty:\n",
-    "        print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n",
-    "        return df # 返回原始或空df，取决于清理前的状态\n",
-    "\n",
-    "    processed_df = df\n",
-    "\n",
-    "    for col in features:\n",
-    "        if col not in df_cleaned.columns:\n",
-    "            print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n",
-    "            # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（可能全是NaN）\n",
-    "            processed_df[col] = df[col] if col in df.columns else np.nan\n",
-    "            continue\n",
-    "\n",
-    "        # 跳过对控制变量本身进行中性化\n",
-    "        if col == mkt_cap_col or col == industry_col:\n",
-    "             print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n",
-    "             # 在结果 df 中也保持原样\n",
-    "             processed_df[col] = df[col] if col in df.columns else np.nan\n",
-    "             continue\n",
-    "\n",
-    "        residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n",
-    "\n",
-    "        # 在分组前处理特征列的 NaN，只对有因子值的行进行回归计算\n",
-    "        df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n",
-    "\n",
-    "        if not df_subset_factor.empty:\n",
-    "            for industry, group in df_subset_factor.groupby(industry_col):\n",
-    "                x = group[mkt_cap_col]  # 市值对数\n",
-    "                y = group[col]           # 因子值\n",
-    "\n",
-    "                # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n",
-    "                # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n",
-    "                if len(group) > 1 and np.var(x) > 1e-9:\n",
-    "                    try:\n",
-    "                        beta = np.cov(y, x)[0, 1] / np.var(x)\n",
-    "                        alpha = np.mean(y) - beta * np.mean(x)\n",
-    "\n",
-    "                        # 计算残差\n",
-    "                        resid = y - (alpha + beta * x)\n",
-    "\n",
-    "                        # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n",
-    "                        residual_series.loc[resid.index] = resid\n",
-    "\n",
-    "                    except Exception as e:\n",
-    "                         # 捕获可能的计算异常，例如np.cov或np.var因为极端数据报错\n",
-    "                         print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n",
-    "                         # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n",
-    "                         # 也可以选择保留原始值：residual_series.loc[group.index] = group[col]\n",
-    "\n",
-    "                else:\n",
-    "                    residual_series.loc[group.index] = group[col] # 保留原始因子值\n",
-    "            processed_df.loc[residual_series.index, col] = residual_series\n",
-    "\n",
-    "\n",
-    "        else:\n",
-    "             processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n",
-    "\n",
-    "    return processed_df\n",
-    "\n",
-    "\n",
-    "import gc\n",
-    "\n",
-    "gc.collect()\n",
-    "\n",
-    "\n",
-    "def mad_filter(df, features, n=3):\n",
-    "    for col in features:\n",
-    "        median = df[col].median()\n",
-    "        mad = np.median(np.abs(df[col] - median))\n",
-    "        upper = median + n * mad\n",
-    "        lower = median - n * mad\n",
-    "        df[col] = np.clip(df[col], lower, upper)  # 截断极值\n",
-    "    return df\n",
-    "\n",
-    "\n",
-    "def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n",
-    "    for col in features:\n",
-    "        # 按日期分组计算上下百分位数\n",
-    "        lower_bound = df.groupby('trade_date')[col].transform(\n",
-    "            lambda x: x.quantile(lower_percentile)\n",
-    "        )\n",
-    "        upper_bound = df.groupby('trade_date')[col].transform(\n",
-    "            lambda x: x.quantile(upper_percentile)\n",
-    "        )\n",
-    "        # 截断超出范围的值\n",
-    "        df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
-    "    return df\n",
-    "\n",
-    "\n",
-    "from scipy.stats import iqr\n",
-    "\n",
-    "\n",
-    "def iqr_filter(df, features):\n",
-    "    for col in features:\n",
-    "        df[col] = df.groupby('trade_date')[col].transform(\n",
-    "            lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n",
-    "        )\n",
-    "    return df\n",
-    "\n",
-    "\n",
-    "def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n",
-    "    df = df.copy()\n",
-    "    for col in features:\n",
-    "        # 计算 rolling 统计量，需要按日期进行 groupby\n",
-    "        rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n",
-    "        rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n",
-    "\n",
-    "        # 对数据进行裁剪\n",
-    "        df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n",
-    "    \n",
-    "    return df\n",
-    "\n",
-    "def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n",
-    "    \"\"\"\n",
-    "    计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n",
-    "\n",
-    "    Args:\n",
-    "        df: 包含特征列和目标列的 Pandas DataFrame。\n",
-    "        feature_columns: 包含所有待评估特征列名的列表。\n",
-    "        n: 希望选取的 RankIC 绝对值最高的特征数量。\n",
-    "        target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n",
-    "\n",
-    "    Returns:\n",
-    "        包含 RankIC 绝对值最高的 n 个特征列名的列表。\n",
-    "    \"\"\"\n",
-    "    numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
-    "    numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
-    "    if target_column not in df.columns:\n",
-    "        raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n",
-    "\n",
-    "    rankic_scores = {}\n",
-    "    for feature in numeric_columns:\n",
-    "        if feature not in df.columns:\n",
-    "            print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n",
-    "            continue\n",
-    "\n",
-    "        # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n",
-    "        # dropna() 是为了处理缺失值，确保相关性计算不失败\n",
-    "        valid_data = df[[feature, target_column]].dropna()\n",
-    "        if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n",
-    "             # 计算斯皮尔曼相关性\n",
-    "            correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n",
-    "            rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n",
-    "        else:\n",
-    "            rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n",
-    "\n",
-    "    # 将 RankIC 分数转换为 Series 便于排序\n",
-    "    rankic_series = pd.Series(rankic_scores)\n",
-    "\n",
-    "    # 按 RankIC 绝对值降序排序，选取前 n 个特征\n",
-    "    # handle case where n might be larger than available features\n",
-    "    n_actual = min(n, len(rankic_series))\n",
-    "    top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n",
-    "    top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n",
-    "    return top_features\n",
-    "\n",
-    "def create_deviation_within_dates(df, feature_columns):\n",
-    "    groupby_col = 'cat_l2_code'  # 使用 trade_date 进行分组\n",
-    "    new_columns = {}\n",
-    "    ret_feature_columns = feature_columns[:]\n",
-    "\n",
-    "    # 自动选择所有数值型特征\n",
-    "    num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n",
-    "\n",
-    "    # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n",
-    "    num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n",
-    "    num_features = [col for col in num_features if 'limit' not in col]\n",
-    "    num_features = [col for col in num_features if 'cyq' not in col]\n",
-    "\n",
-    "    # 遍历所有数值型特征\n",
-    "    for feature in num_features:\n",
-    "        if feature == 'trade_date':  # 不需要对 'trade_date' 计算偏差\n",
-    "            continue\n",
-    "\n",
-    "        # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n",
-    "        # deviation_col_name = f'deviation_mean_{feature}'\n",
-    "        # new_columns[deviation_col_name] = df[feature] - grouped_mean\n",
-    "        # ret_feature_columns.append(deviation_col_name)\n",
-    "\n",
-    "        grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n",
-    "        deviation_col_name = f'deviation_mean_{feature}'\n",
-    "        new_columns[deviation_col_name] = df[feature] - grouped_mean\n",
-    "        ret_feature_columns.append(deviation_col_name)\n",
-    "\n",
-    "    # 将新计算的偏差特征与原始 DataFrame 合并\n",
-    "    df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n",
-    "\n",
-    "    # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n",
-    "    #     df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n",
-    "\n",
-    "    return df, ret_feature_columns\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "47c12bb34062ae7a",
    "metadata": {
     "ExecuteTime": {
@@ -1403,7 +840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "29221dde",
    "metadata": {},
    "outputs": [
@@ -1411,7 +848,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "205\n"
+      "208\n"
      ]
     }
    ],
@@ -1432,6 +869,8 @@
     "feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n",
     "# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n",
     "# feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n",
+    "feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n",
+    "\n",
     "feature_columns = [col for col in feature_columns if 'code' not in col]\n",
     "feature_columns = [col for col in feature_columns if col not in origin_columns]\n",
     "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
@@ -1450,7 +889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "03ee5daf",
    "metadata": {},
    "outputs": [],
@@ -1463,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "b76ea08a",
    "metadata": {},
    "outputs": [
@@ -1475,7 +914,7 @@
       "0  000001.SZ 2019-01-02    16.574219\n",
       "1  000001.SZ 2019-01-03    16.583965\n",
       "2  000001.SZ 2019-01-04    16.633371\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', '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",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'is_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', '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"
      ]
@@ -1484,7 +923,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 144/144 [00:07<00:00, 19.10it/s]\n"
+      "MAD Filtering: 100%|██████████| 147/147 [00:09<00:00, 15.75it/s]\n"
      ]
     },
     {
@@ -1500,7 +939,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Standardizing: 100%|██████████| 144/144 [00:02<00:00, 58.47it/s]\n"
+      "Standardizing: 100%|██████████| 147/147 [00:02<00:00, 54.57it/s]\n"
      ]
     },
     {
@@ -1515,7 +954,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 144/144 [00:05<00:00, 25.72it/s]\n"
+      "MAD Filtering: 100%|██████████| 147/147 [00:05<00:00, 25.55it/s]\n"
      ]
     },
     {
@@ -1530,7 +969,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Standardizing: 100%|██████████| 144/144 [00:01<00:00, 81.71it/s]\n"
+      "Standardizing:  37%|███▋      | 54/147 [00:00<00:01, 81.06it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Standardizing: 100%|██████████| 147/147 [00:01<00:00, 80.38it/s]\n"
      ]
     },
     {
@@ -1568,7 +1014,7 @@
      "output_type": "stream",
      "text": [
       "截面 MAD 去极值处理完成。\n",
-      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', '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",
+      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'is_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "df最小日期: 2019-01-02\n",
       "df最大日期: 2025-05-30\n",
       "1091062\n",
@@ -1585,6 +1031,8 @@
     }
    ],
    "source": [
+    "from main.utils.data_process import *\n",
+    "\n",
     "split_date = '2023-01-01'\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",
@@ -1614,56 +1062,6 @@
     "# train_data, _ = create_deviation_within_dates(train_data, [col for col in feature_columns if col in train_data.columns])\n",
     "# test_data, _ = create_deviation_within_dates(test_data, [col for col in feature_columns if col in train_data.columns])\n",
     "\n",
-    "# feature_columns = [\n",
-    "#        'undist_profit_ps', \n",
-    "#        'AR_BR',\n",
-    "#        'pe_ttm',\n",
-    "#        'alpha_22_improved', \n",
-    "#        'alpha_003', \n",
-    "#        'alpha_007', \n",
-    "#        'alpha_013', \n",
-    "#        'cat_up_limit', \n",
-    "#        'cat_down_limit', \n",
-    "#        'up_limit_count_10d', \n",
-    "#        'down_limit_count_10d', \n",
-    "#        'consecutive_up_limit', \n",
-    "#        'vol_break', \n",
-    "#        'weight_roc5', \n",
-    "#        'price_cost_divergence', \n",
-    "#        'smallcap_concentration', \n",
-    "#        'cost_stability', \n",
-    "#        'high_cost_break_days', \n",
-    "#        'liquidity_risk', \n",
-    "#        'turnover_std', \n",
-    "#        'mv_volatility', \n",
-    "#        'volume_growth', \n",
-    "#        'mv_growth', \n",
-    "#        'lg_flow_mom_corr_20_60', \n",
-    "#        'lg_flow_accel', \n",
-    "#        'profit_pressure', \n",
-    "#        'underwater_resistance', \n",
-    "#        'cost_conc_std_20', \n",
-    "#        'profit_decay_20', \n",
-    "#        'vol_amp_loss_20', \n",
-    "#        'vol_drop_profit_cnt_5', \n",
-    "#        'lg_flow_vol_interact_20', \n",
-    "#        'cost_break_confirm_cnt_5', \n",
-    "#        'atr_norm_channel_pos_14', \n",
-    "#        'turnover_diff_skew_20', \n",
-    "#        'lg_sm_flow_diverge_20', \n",
-    "#        'pullback_strong_20_20', \n",
-    "#        'vol_wgt_hist_pos_20', \n",
-    "#        'vol_adj_roc_20',\n",
-    "#        'cashflow_to_ev_factor',\n",
-    "#        'ocfps',\n",
-    "#        'book_to_price_ratio',\n",
-    "#        'turnover_rate_mean_5',\n",
-    "#        'variance_20',\n",
-    "#        'bbi_ratio_factor'\n",
-    "# ]\n",
-    "# feature_columns = [col for col in feature_columns if col in train_data.columns]\n",
-    "# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
-    "\n",
     "numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
     "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
     "# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n",
@@ -1721,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1854,8 +1252,8 @@
     "            # '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",
+    "        feature_contri = [2 if 'concept' 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",
@@ -1920,7 +1318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1930,49 +1328,15 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train data size:  728000\n",
-      "          ts_code trade_date  log_circ_mv\n",
-      "0       600306.SH 2020-01-02    -1.625251\n",
-      "1       603269.SH 2020-01-02    -2.029142\n",
-      "2       002633.SZ 2020-01-02    -1.257360\n",
-      "3       603991.SH 2020-01-02    -2.284466\n",
-      "4       000691.SZ 2020-01-02    -1.401531\n",
-      "...           ...        ...          ...\n",
-      "727995  002578.SZ 2022-12-30     0.648406\n",
-      "727996  002766.SZ 2022-12-30     0.571533\n",
-      "727997  600495.SH 2022-12-30     0.802632\n",
-      "727998  002119.SZ 2022-12-30     0.751421\n",
-      "727999  000151.SZ 2022-12-30     0.371616\n",
-      "\n",
-      "[728000 rows x 3 columns]\n",
-      "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 300 rounds\n",
-      "Early stopping, best iteration is:\n",
-      "[22]\ttrain's ndcg@5: 0.428245\tvalid's ndcg@5: 0.432426\n"
+     "ename": "NameError",
+     "evalue": "name 'gc' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mgc\u001b[49m.collect()\n\u001b[32m      3\u001b[39m use_pca = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28mtype\u001b[39m = \u001b[33m'\u001b[39m\u001b[33mlight\u001b[39m\u001b[33m'\u001b[39m\n",
+      "\u001b[31mNameError\u001b[39m: name 'gc' is not defined"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -2002,7 +1366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -2044,7 +1408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "fed2d6c3",
    "metadata": {},
    "outputs": [
@@ -2062,7 +1426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "1f3c1331",
    "metadata": {},
    "outputs": [
@@ -2078,30 +1442,30 @@
       "\n",
       "从 Redis 加载的 DataFrame (使用 Pickle):\n",
       "      index    ts_code trade_date   open  close   high    low       vol  \\\n",
-      "4        36  002247.SZ 2023-01-03  16.15  16.80  16.87  16.09  0.514578   \n",
-      "3        26  002513.SZ 2023-01-03  15.41  15.77  15.95  15.41 -0.499029   \n",
-      "2        14  002629.SZ 2023-01-03  15.17  15.54  15.58  15.13  0.631716   \n",
-      "1         5  603030.SH 2023-01-03   8.08   8.32   8.32   8.05 -0.033641   \n",
-      "0         3  000691.SZ 2023-01-03  13.87  14.24  14.36  13.63 -0.030740   \n",
+      "4        25  600202.SH 2023-01-03  25.05  25.80  25.90  24.85 -0.393826   \n",
+      "3        30  603828.SH 2023-01-03  15.09  15.55  15.78  15.09 -0.100351   \n",
+      "2         0  600768.SH 2023-01-03  30.72  31.55  31.62  30.49 -0.912596   \n",
+      "1        10  600883.SH 2023-01-03  34.60  35.08  35.27  34.36 -0.868511   \n",
+      "0        37  002247.SZ 2023-01-03  16.15  16.80  16.87  16.09  0.514578   \n",
       "...     ...        ...        ...    ...    ...    ...    ...       ...   \n",
-      "2904  58031  002524.SZ 2025-05-30  19.49  19.40  20.27  19.31  0.455783   \n",
-      "2903  58034  002084.SZ 2025-05-30  11.88  11.68  11.88  11.58  0.610122   \n",
-      "2902  58029  600159.SH 2025-05-30  13.66  13.55  13.86  13.50 -0.170606   \n",
-      "2901  58035  002775.SZ 2025-05-30  17.84  17.55  17.84  17.41 -0.705858   \n",
-      "2900  58019  600408.SH 2025-05-30   8.64   8.51   8.64   8.43  0.684604   \n",
+      "2904  58029  603778.SH 2025-05-30  20.39  19.95  20.45  19.89 -0.489064   \n",
+      "2903  58040  002775.SZ 2025-05-30  17.84  17.55  17.84  17.41 -0.705858   \n",
+      "2902  58001  002652.SZ 2025-05-30  15.36  15.11  15.41  14.95 -0.183952   \n",
+      "2901  58041  002687.SZ 2025-05-30  31.23  30.75  31.23  30.61 -0.662998   \n",
+      "2900  58059  002753.SZ 2025-05-30  25.72  25.38  25.76  25.27 -0.865006   \n",
       "\n",
       "       pct_chg     amount  ...  000905.SH_up_ratio_20d  \\\n",
-      "4     0.715898  31834.487  ...                     0.3   \n",
-      "3     0.121763  25452.447  ...                     0.3   \n",
-      "2     0.299045  55379.071  ...                     0.3   \n",
-      "1     0.241548  22271.706  ...                     0.3   \n",
-      "0     0.279880  38602.205  ...                     0.3   \n",
+      "4     0.634444  28798.328  ...                     0.3   \n",
+      "3     0.624861  26149.008  ...                     0.3   \n",
+      "2     0.342168  15832.327  ...                     0.3   \n",
+      "1    -0.136973  14886.570  ...                     0.3   \n",
+      "0     0.715898  31834.487  ...                     0.3   \n",
       "...        ...        ...  ...                     ...   \n",
-      "2904  0.552905  76113.818  ...                     0.6   \n",
-      "2903 -0.070682  67595.352  ...                     0.6   \n",
-      "2902  0.005989  28573.069  ...                     0.6   \n",
-      "2901  0.241111  19025.635  ...                     0.6   \n",
-      "2900  0.788027  40827.689  ...                     0.6   \n",
+      "2904 -0.167798  23633.664  ...                     0.6   \n",
+      "2903  0.241111  19025.635  ...                     0.6   \n",
+      "2902 -0.045125  32385.927  ...                     0.6   \n",
+      "2901  0.210443  24919.298  ...                     0.6   \n",
+      "2900  0.031546  22718.273  ...                     0.6   \n",
       "\n",
       "      399006.SZ_up_ratio_20d  000852.SH_volatility  000905.SH_volatility  \\\n",
       "4                       0.40              1.036997              0.828596   \n",
@@ -2130,17 +1494,17 @@
       "2900              1.195355                     -2.039466   \n",
       "\n",
       "      000905.SH_volume_change_rate  399006.SZ_volume_change_rate     score  \\\n",
-      "4                        -0.750721                      8.827360  0.042645   \n",
-      "3                        -0.750721                      8.827360  0.047474   \n",
-      "2                        -0.750721                      8.827360  0.055433   \n",
-      "1                        -0.750721                      8.827360  0.061053   \n",
-      "0                        -0.750721                      8.827360  0.068349   \n",
+      "4                        -0.750721                      8.827360  0.141602   \n",
+      "3                        -0.750721                      8.827360  0.145938   \n",
+      "2                        -0.750721                      8.827360  0.169856   \n",
+      "1                        -0.750721                      8.827360  0.174660   \n",
+      "0                        -0.750721                      8.827360  0.200525   \n",
       "...                            ...                           ...       ...   \n",
-      "2904                    -12.002493                      5.078672  0.064251   \n",
-      "2903                    -12.002493                      5.078672  0.064517   \n",
-      "2902                    -12.002493                      5.078672  0.065030   \n",
-      "2901                    -12.002493                      5.078672  0.068848   \n",
-      "2900                    -12.002493                      5.078672  0.074780   \n",
+      "2904                    -12.002493                      5.078672  0.269821   \n",
+      "2903                    -12.002493                      5.078672  0.273371   \n",
+      "2902                    -12.002493                      5.078672  0.274304   \n",
+      "2901                    -12.002493                      5.078672  0.291169   \n",
+      "2900                    -12.002493                      5.078672  0.312279   \n",
       "\n",
       "      score_ranks  \n",
       "4           996.0  \n",
@@ -2155,7 +1519,7 @@
       "2901        999.0  \n",
       "2900       1000.0  \n",
       "\n",
-      "[2905 rows x 248 columns]\n",
+      "[2905 rows x 252 columns]\n",
       "\n",
       "验证成功：原始 DataFrame 和从 Redis 加载的 DataFrame 一致。\n",
       "\n",
@@ -2221,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -2229,8 +1593,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "205\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', '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"
+      "208\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'is_hot_concept_stock', 'concept_rank_pct_chg', 'concept_rank_turnover_rate', 'concept_rank_volume_ratio', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', '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"
      ]
     }
    ],
@@ -2241,7 +1605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "bceabd1f",
    "metadata": {},
    "outputs": [
@@ -2252,7 +1616,7 @@
       "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': np.float64(0.4489795918367347), 'ndcg@3': np.float64(0.40668217598446815), 'ndcg@5': np.float64(0.45584495629735)}\n"
+      "{'ndcg@1': np.float64(nan), 'ndcg@3': np.float64(nan), 'ndcg@5': np.float64(nan)}\n"
      ]
     }
    ],
@@ -2315,7 +1679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "44f64679",
    "metadata": {},
    "outputs": [
@@ -2401,7 +1765,7 @@
       "1633579      0.034231            NaN    NaN  \n",
       "1633580      0.033887            NaN    NaN  \n",
       "\n",
-      "[1553 rows x 196 columns]\n"
+      "[1553 rows x 200 columns]\n"
      ]
     }
    ],
diff --git a/my_catboost_model.cbm b/my_catboost_model.cbm
new file mode 100644
index 0000000..9a2ab79
Binary files /dev/null and b/my_catboost_model.cbm differ
diff --git a/predictions_test.tsv b/predictions_test.tsv
index 6ab0f48..a53203a 100644
--- a/predictions_test.tsv
+++ b/predictions_test.tsv
@@ -1,2906 +1,1163 @@
 trade_date,score,ts_code
-2023-01-03,0.04264513757161396,002247.SZ
-2023-01-03,0.047473879793379845,002513.SZ
-2023-01-03,0.055433418705804545,002629.SZ
-2023-01-03,0.061053204232209037,603030.SH
-2023-01-03,0.06834946223279599,000691.SZ
-2023-01-04,0.04944180779763693,002652.SZ
-2023-01-04,0.05101764675472167,000702.SZ
-2023-01-04,0.052308519226764674,002247.SZ
-2023-01-04,0.054430293533857546,600250.SH
-2023-01-04,0.06296916608880652,002629.SZ
-2023-01-05,0.049799080682322444,603021.SH
-2023-01-05,0.05444541751413591,603030.SH
-2023-01-05,0.05640454658386936,600250.SH
-2023-01-05,0.06136422395645191,002247.SZ
-2023-01-05,0.06186702549189658,002629.SZ
-2023-01-06,0.04899995617168002,002629.SZ
-2023-01-06,0.05358670281112124,002247.SZ
-2023-01-06,0.05720347721701177,603021.SH
-2023-01-06,0.06199820984701511,002652.SZ
-2023-01-06,0.07244040441746522,603030.SH
-2023-01-09,0.052446850561966796,002629.SZ
-2023-01-09,0.05568827403393317,600250.SH
-2023-01-09,0.05795124819985819,603030.SH
-2023-01-09,0.05842796373007265,603021.SH
-2023-01-09,0.06225045807858355,002247.SZ
-2023-01-10,0.04956641692162102,600250.SH
-2023-01-10,0.05416621073860915,002620.SZ
-2023-01-10,0.05421082871972414,002513.SZ
-2023-01-10,0.06016640270372842,603021.SH
-2023-01-10,0.060947412143261714,002629.SZ
-2023-01-11,0.04484704680100516,002629.SZ
-2023-01-11,0.0462047496065822,002663.SZ
-2023-01-11,0.06160223401528153,002513.SZ
-2023-01-11,0.06386304735439895,002247.SZ
-2023-01-11,0.06539045541890233,603021.SH
-2023-01-12,0.050717553670052366,002663.SZ
-2023-01-12,0.05188664861800753,600889.SH
-2023-01-12,0.0530997141388225,002629.SZ
-2023-01-12,0.0574694706126226,603021.SH
-2023-01-12,0.06667637473470708,002513.SZ
-2023-01-13,0.047435387719658904,002571.SZ
-2023-01-13,0.04934164641603143,603021.SH
-2023-01-13,0.05165412498687684,002513.SZ
-2023-01-13,0.05209769415087441,002629.SZ
-2023-01-13,0.058278158774065364,002620.SZ
-2023-01-16,0.046561838060479,002207.SZ
-2023-01-16,0.04849633882432157,002629.SZ
-2023-01-16,0.04904907249803364,002571.SZ
-2023-01-16,0.04953635797493052,603021.SH
-2023-01-16,0.0648569858189875,000702.SZ
-2023-01-17,0.04586716086768336,600202.SH
-2023-01-17,0.047014961113596314,000702.SZ
-2023-01-17,0.0524164349400763,002247.SZ
-2023-01-17,0.054415370215465116,002660.SZ
-2023-01-17,0.0560995897360317,603021.SH
-2023-01-18,0.0408635713924239,000586.SZ
-2023-01-18,0.042106229807758876,002620.SZ
-2023-01-18,0.055694953336546164,002247.SZ
-2023-01-18,0.056254269736928784,002513.SZ
-2023-01-18,0.05729444275529857,603021.SH
-2023-01-19,0.04492469230567693,002173.SZ
-2023-01-19,0.045023915678024974,000586.SZ
-2023-01-19,0.04656454952643082,002660.SZ
-2023-01-19,0.05096890342664264,600355.SH
-2023-01-19,0.055146648389248924,002513.SZ
-2023-01-20,0.05055920803829244,002247.SZ
-2023-01-20,0.05307394243821632,600355.SH
-2023-01-20,0.055297497612008556,002652.SZ
-2023-01-20,0.05573860318593531,603021.SH
-2023-01-20,0.06174766927721104,002513.SZ
-2023-01-30,0.04177134811785972,603021.SH
-2023-01-30,0.044530994966345265,002629.SZ
-2023-01-30,0.047935952750786846,000702.SZ
-2023-01-30,0.05025612172590263,002660.SZ
-2023-01-30,0.05378880116172613,002652.SZ
-2023-01-31,0.04999480687861859,603021.SH
-2023-01-31,0.05465004088305856,000702.SZ
-2023-01-31,0.05617413905685271,002247.SZ
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