diff --git a/main/factor/__pycache__/factor.cpython-311.pyc b/main/factor/__pycache__/factor.cpython-311.pyc
index c489e50..cdae86a 100644
Binary files a/main/factor/__pycache__/factor.cpython-311.pyc and b/main/factor/__pycache__/factor.cpython-311.pyc differ
diff --git a/main/factor/factor.py b/main/factor/factor.py
index 7d56f5a..e308a3c 100644
--- a/main/factor/factor.py
+++ b/main/factor/factor.py
@@ -2,9 +2,7 @@ import numpy as np
 import pandas as pd
 import talib
 
-from pandarallel import pandarallel
 
-pandarallel.initialize()
 
 def get_rolling_factor(df):
     old_columns = df.columns.tolist()[:]
@@ -405,7 +403,6 @@ def get_rolling_factor(df):
         ascending=True, pct=True
     )
 
-    
     df["vol_break"] = np.where(
         (df["close"] > df["cost_85pct"]) & (df["volume_ratio"] > 2), 1, 0
     )
@@ -2161,8 +2158,8 @@ def calculate_arbr(df: pd.DataFrame, N: int = 26):
 
     WARNING: 此函数会原地修改输入的 DataFrame 'df'。
     """
-    ar_col_name = 'AR'
-    br_col_name = 'BR'
+    ar_col_name = "AR"
+    br_col_name = "BR"
     print(f"开始计算因子: {ar_col_name}, {br_col_name} (原地修改)...")
 
     _temp_cols = []  # 记录中间列
@@ -2226,7 +2223,7 @@ def calculate_arbr(df: pd.DataFrame, N: int = 26):
             _safe_divide(sum_h_minus_pc_pos, sum_pc_minus_l_pos, default_val=np.nan)
             * 100
         )  # BR 通常乘以 100
-        df[f'{ar_col_name}_{br_col_name}'] = df[ar_col_name] - df[br_col_name]
+        df[f"{ar_col_name}_{br_col_name}"] = df[ar_col_name] - df[br_col_name]
 
         print(f"因子 {ar_col_name}, {br_col_name} 计算成功。")
 
@@ -2251,14 +2248,15 @@ def calculate_arbr(df: pd.DataFrame, N: int = 26):
             df.drop(columns=cols_to_drop, inplace=True)
         print(f"因子 {ar_col_name}, {br_col_name} 计算流程结束。")
 
+
 def add_financial_factor(
     main_df: pd.DataFrame,
     financial_df: pd.DataFrame,
-    factor_value_col: str, # 财务指标值所在的列
-    ts_code_col: str = 'ts_code',
-    trade_date_col: str = 'trade_date',
-    ann_date_col: str = 'ann_date', # 公告日期
-    f_ann_date_col: str = 'f_ann_date', # 实际公告日期 (优先使用)
+    factor_value_col: str,  # 财务指标值所在的列
+    ts_code_col: str = "ts_code",
+    trade_date_col: str = "trade_date",
+    ann_date_col: str = "ann_date",  # 公告日期
+    f_ann_date_col: str = "f_ann_date",  # 实际公告日期 (优先使用)
 ) -> pd.DataFrame:
     """
     将财务指标数据（如每股未分配利润）作为因子添加到主时间序列 DataFrame 中。
@@ -2295,47 +2293,60 @@ def add_financial_factor(
         effective_date_col = ann_date_col
         print(f"使用 '{ann_date_col}' 作为财务数据生效日期。")
     else:
-         raise ValueError(f"财务指标 DataFrame 必须包含列 '{f_ann_date_col}' 或 '{ann_date_col}' 作为数据生效日期")
+        raise ValueError(
+            f"财务指标 DataFrame 必须包含列 '{f_ann_date_col}' 或 '{ann_date_col}' 作为数据生效日期"
+        )
     required_financial_cols.append(effective_date_col)
 
     if not all(col in financial_df.columns for col in required_financial_cols):
-         raise ValueError(f"财务指标 DataFrame 必须包含列: {required_financial_cols}")
+        raise ValueError(f"财务指标 DataFrame 必须包含列: {required_financial_cols}")
 
     # --- 数据准备和清理 ---
     # 确保日期列是 datetime 类型
     # 使用 .copy() 避免 SettingWithCopyWarning
     main_df = main_df.copy()
     financial_df = financial_df.copy()
-    main_df[trade_date_col] = pd.to_datetime(main_df[trade_date_col], errors='coerce')
-    financial_df[effective_date_col] = pd.to_datetime(financial_df[effective_date_col], errors='coerce')
+    main_df[trade_date_col] = pd.to_datetime(main_df[trade_date_col], errors="coerce")
+    financial_df[effective_date_col] = pd.to_datetime(
+        financial_df[effective_date_col], errors="coerce"
+    )
 
     # 确保股票代码是字符串类型
     main_df[ts_code_col] = main_df[ts_code_col].astype(str)
     financial_df[ts_code_col] = financial_df[ts_code_col].astype(str)
 
     # 选取 financial_df 中需要合并的列
-    financial_data_subset = financial_df[[ts_code_col, effective_date_col, factor_value_col]].copy()
+    financial_data_subset = financial_df[
+        [ts_code_col, effective_date_col, factor_value_col]
+    ].copy()
 
     # *** 新增：处理右表合并键中的空值 ***
     initial_rows_financial = len(financial_data_subset)
-    financial_data_subset = financial_data_subset.dropna(subset=[ts_code_col, effective_date_col])
+    financial_data_subset = financial_data_subset.dropna(
+        subset=[ts_code_col, effective_date_col]
+    )
     rows_dropped = initial_rows_financial - len(financial_data_subset)
     if rows_dropped > 0:
-        print(f"警告: 从 financial_data_subset 中移除了 {rows_dropped} 行，因为其 '{ts_code_col}' 或 '{effective_date_col}' 列存在空值。")
+        print(
+            f"警告: 从 financial_data_subset 中移除了 {rows_dropped} 行，因为其 '{ts_code_col}' 或 '{effective_date_col}' 列存在空值。"
+        )
 
     if financial_data_subset.empty:
-         print(f"警告: 清理空值后 financial_data_subset 为空，无法添加因子 '{new_factor_col_name}'。将填充 NaN。")
-         main_df[new_factor_col_name] = np.nan
-         return main_df
-
+        print(
+            f"警告: 清理空值后 financial_data_subset 为空，无法添加因子 '{new_factor_col_name}'。将填充 NaN。"
+        )
+        main_df[new_factor_col_name] = np.nan
+        return main_df
 
     # *** 修改：修正排序顺序以满足 merge_asof 要求 ***
     # 先按 ts_code 排序，再按日期排序
     # main_df = main_df.sort_values(by=[ts_code_col, trade_date_col])
     # financial_data_subset = financial_data_subset.sort_values(by=[ts_code_col, effective_date_col])
     main_df = main_df.sort_values(by=[trade_date_col, ts_code_col])
-    financial_data_subset = financial_data_subset.sort_values(by=[effective_date_col, ts_code_col])
-    
+    financial_data_subset = financial_data_subset.sort_values(
+        by=[effective_date_col, ts_code_col]
+    )
+
     # --- 使用 merge_asof 计算因子 ---
     try:
         df_with_factor = pd.merge_asof(
@@ -2344,43 +2355,52 @@ def add_financial_factor(
             left_on=trade_date_col,
             right_on=effective_date_col,
             by=ts_code_col,
-            direction='backward'
+            direction="backward",
         )
     except Exception as e:
-         print(f"merge_asof 执行失败: {e}")
-         # 根据需要决定如何处理错误，这里填充 NaN
-         main_df[new_factor_col_name] = np.nan
-         return main_df
-
+        print(f"merge_asof 执行失败: {e}")
+        # 根据需要决定如何处理错误，这里填充 NaN
+        main_df[new_factor_col_name] = np.nan
+        return main_df
 
     # --- 清理与重命名 ---
     # 移除右表的日期列（如果它与左表日期列名称不同）
-    if effective_date_col in df_with_factor.columns and effective_date_col != trade_date_col:
-         df_with_factor = df_with_factor.drop(columns=[effective_date_col])
+    if (
+        effective_date_col in df_with_factor.columns
+        and effective_date_col != trade_date_col
+    ):
+        df_with_factor = df_with_factor.drop(columns=[effective_date_col])
 
     # 重命名新加入的因子列
     if factor_value_col != new_factor_col_name:
-         if factor_value_col in df_with_factor.columns:
-             df_with_factor = df_with_factor.rename(columns={factor_value_col: new_factor_col_name})
-         else:
-             # 这种情况理论上不应发生，因为 merge_asof 应该会把右表的非 key 列带过来
-             print(f"警告: 合并后未找到原始因子列 '{factor_value_col}'，无法重命名。")
-             # 如果 factor_value_col 已是目标名称，则无需重命名
-             if new_factor_col_name not in df_with_factor.columns:
-                 # 如果目标名称也不存在，则可能合并失败或列名有问题
-                 df_with_factor[new_factor_col_name] = np.nan
-
+        if factor_value_col in df_with_factor.columns:
+            df_with_factor = df_with_factor.rename(
+                columns={factor_value_col: new_factor_col_name}
+            )
+        else:
+            # 这种情况理论上不应发生，因为 merge_asof 应该会把右表的非 key 列带过来
+            print(f"警告: 合并后未找到原始因子列 '{factor_value_col}'，无法重命名。")
+            # 如果 factor_value_col 已是目标名称，则无需重命名
+            if new_factor_col_name not in df_with_factor.columns:
+                # 如果目标名称也不存在，则可能合并失败或列名有问题
+                df_with_factor[new_factor_col_name] = np.nan
 
     # 如果 factor_value_col 就是目标名称，确保该列存在
     elif new_factor_col_name not in df_with_factor.columns:
         print(f"警告: 合并后未找到目标因子列 '{new_factor_col_name}'。填充 NaN。")
         df_with_factor[new_factor_col_name] = np.nan
 
-
     return df_with_factor
 
 
-def calculate_cashflow_to_ev_factor(df: pd.DataFrame, cashflow_df: pd.DataFrame, balancesheet_df: pd.DataFrame, market_cap_col: str = 'total_mv', date_col: str = 'trade_date', ts_code_col: str = 'ts_code') -> pd.DataFrame:
+def calculate_cashflow_to_ev_factor(
+    df: pd.DataFrame,
+    cashflow_df: pd.DataFrame,
+    balancesheet_df: pd.DataFrame,
+    market_cap_col: str = "total_mv",
+    date_col: str = "trade_date",
+    ts_code_col: str = "ts_code",
+) -> pd.DataFrame:
     """
     计算经营活动产生的现金流量净额TTM / 企业价值因子。
     企业价值 = 司市值 + 负债合计 - 货币资金。
@@ -2400,18 +2420,18 @@ def calculate_cashflow_to_ev_factor(df: pd.DataFrame, cashflow_df: pd.DataFrame,
     Returns:
         pd.DataFrame: 添加了 'cashflow_to_ev_factor' 列的 DataFrame。
     """
-    df_factor = df.copy() # 创建副本以避免修改原始 DataFrame
+    df_factor = df.copy()  # 创建副本以避免修改原始 DataFrame
 
     # 0. 确保必要的市场市值列存在
     if market_cap_col not in df_factor.columns:
         print(f"错误: DataFrame 中缺少市值列 '{market_cap_col}'。无法计算因子。")
         # 添加一个空的因子列并返回
-        df_factor['cashflow_to_ev_factor'] = np.nan
+        df_factor["cashflow_to_ev_factor"] = np.nan
         return df_factor
 
     # 1. 获取经营活动产生的现金流量净额 (TTM - **注意这里的潜在不准确性**)
     # 如果 add_financial_factor 只获取单季度，这里的 n_cashflow_act 将不是 TTM
-    df_factor = add_financial_factor(df_factor, cashflow_df, 'n_cashflow_act')
+    df_factor = add_financial_factor(df_factor, cashflow_df, "n_cashflow_act")
     # 如果 add_financial_factor 能够正确处理 TTM，那么上面的调用是正确的。
     # 否则，您需要在 add_financial_factor 内部实现 TTM 逻辑，或者在调用 add_financial_factor
     # 获取多个季度数据后，在这里手动进行 TTM 求和。
@@ -2419,86 +2439,103 @@ def calculate_cashflow_to_ev_factor(df: pd.DataFrame, cashflow_df: pd.DataFrame,
     # 并命名为 ttm_n_cashflow_act 以示期望
 
     # 重新命名获取的现金流列以便后续计算
-    cashflow_col_name = 'n_cashflow_act' # 获取的列名
-    ttm_cashflow_col = 'ttm_n_cashflow_act' # 因子计算中使用的列名
+    cashflow_col_name = "n_cashflow_act"  # 获取的列名
+    ttm_cashflow_col = "ttm_n_cashflow_act"  # 因子计算中使用的列名
     if cashflow_col_name in df_factor.columns:
-         df_factor = df_factor.rename(columns={cashflow_col_name: ttm_cashflow_col})
+        df_factor = df_factor.rename(columns={cashflow_col_name: ttm_cashflow_col})
     else:
         # 如果 add_financial_factor 没成功添加列
         print(f"错误: add_financial_factor 未能成功添加 '{cashflow_col_name}' 列。")
-        df_factor['cashflow_to_ev_factor'] = np.nan
+        df_factor["cashflow_to_ev_factor"] = np.nan
         return df_factor
 
-
     # 2. 获取负债合计
-    df_factor = add_financial_factor(df_factor, balancesheet_df, 'total_liab')
-    liab_col_name = 'total_liab'
+    df_factor = add_financial_factor(df_factor, balancesheet_df, "total_liab")
+    liab_col_name = "total_liab"
     if liab_col_name not in df_factor.columns:
-         print(f"错误: add_financial_factor 未能成功添加 '{liab_col_name}' 列。")
-         df_factor['cashflow_to_ev_factor'] = np.nan
-         return df_factor
-
+        print(f"错误: add_financial_factor 未能成功添加 '{liab_col_name}' 列。")
+        df_factor["cashflow_to_ev_factor"] = np.nan
+        return df_factor
 
     # 3. 获取货币资金
-    df_factor = add_financial_factor(df_factor, balancesheet_df, 'money_cap')
-    money_col_name = 'money_cap'
+    df_factor = add_financial_factor(df_factor, balancesheet_df, "money_cap")
+    money_col_name = "money_cap"
     if money_col_name not in df_factor.columns:
-         print(f"错误: add_financial_factor 未能成功添加 '{money_col_name}' 列。")
-         df_factor['cashflow_to_ev_factor'] = np.nan
-         return df_factor
-
+        print(f"错误: add_financial_factor 未能成功添加 '{money_col_name}' 列。")
+        df_factor["cashflow_to_ev_factor"] = np.nan
+        return df_factor
 
     # 4. 计算企业价值 (Enterprise Value)
     # 确保参与计算的列是数值类型，并处理 NaN (NaN + X = NaN, NaN - X = NaN)
-    enterprise_value = df_factor[market_cap_col].astype(float) * 10000 + df_factor[liab_col_name].astype(float) - df_factor[money_col_name].astype(float)
+    enterprise_value = (
+        df_factor[market_cap_col].astype(float) * 10000
+        + df_factor[liab_col_name].astype(float)
+        - df_factor[money_col_name].astype(float)
+    )
 
     # 5. 计算最终因子：经营活动产生的现金流量净额TTM / 企业价值
     # 使用之前定义的安全除法
-    df_factor['cashflow_to_ev_factor'] = _safe_divide(df_factor[ttm_cashflow_col], enterprise_value)
+    df_factor["cashflow_to_ev_factor"] = _safe_divide(
+        df_factor[ttm_cashflow_col], enterprise_value
+    )
 
     # 6. 删除临时添加的财务数据列
     cols_to_drop = [ttm_cashflow_col, liab_col_name, money_col_name]
-    df_factor = df_factor.drop(columns=[col for col in cols_to_drop if col in df_factor.columns])
-
+    df_factor = df_factor.drop(
+        columns=[col for col in cols_to_drop if col in df_factor.columns]
+    )
 
     return df_factor
 
 
-
-def caculate_book_to_price_ratio(df: pd.DataFrame, fina_indicator_df: pd.DataFrame) -> pd.DataFrame:
-    if 'bps' not in df.columns:
-        df = add_financial_factor(df, fina_indicator_df, factor_value_col='bps')
-    df['book_to_price_ratio'] = df['bps'] / df['close']
-    df = df.drop(columns=['bps'])
+def caculate_book_to_price_ratio(
+    df: pd.DataFrame, fina_indicator_df: pd.DataFrame
+) -> pd.DataFrame:
+    if "bps" not in df.columns:
+        df = add_financial_factor(df, fina_indicator_df, factor_value_col="bps")
+    df["book_to_price_ratio"] = df["bps"] / df["close"]
+    df = df.drop(columns=["bps"])
     return df
 
 
 def turnover_rate_n(df: pd.DataFrame, n: int) -> pd.DataFrame:
-    df[f'turnover_rate_mean_{n}'] = df.groupby('ts_code', group_keys=False)['turnover_rate'].rolling(n).mean().reset_index(level=0, drop=True)
+    df[f"turnover_rate_mean_{n}"] = (
+        df.groupby("ts_code", group_keys=False)["turnover_rate"]
+        .rolling(n)
+        .mean()
+        .reset_index(level=0, drop=True)
+    )
     return df
 
+
 def variance_n(df: pd.DataFrame, n: int) -> pd.DataFrame:
-    df[f'variance_{n}'] = df.groupby('ts_code', group_keys=False)['pct_chg'].rolling(n).var().reset_index(level=0, drop=True)
+    df[f"variance_{n}"] = (
+        df.groupby("ts_code", group_keys=False)["pct_chg"]
+        .rolling(n)
+        .var()
+        .reset_index(level=0, drop=True)
+    )
 
     return df
 
+
 def bbi_ratio_factor(df: pd.DataFrame) -> pd.DataFrame:
     df_factor = df
-    
+
     # 确保数据按股票代码和日期排序，这对滚动计算非常重要
-    df_factor = df_factor.sort_values(by=['ts_code', 'trade_date'])
+    df_factor = df_factor.sort_values(by=["ts_code", "trade_date"])
 
     # 获取收盘价列
-    close_prices = df_factor['close']
+    close_prices = df_factor["close"]
 
     # 1. 根据 ts_code 分组计算各周期的简单移动平均线 (SMA)
-    grouped = df_factor.groupby('ts_code', group_keys=False)
+    grouped = df_factor.groupby("ts_code", group_keys=False)
 
     # 计算不同周期的 SMA，并使用 reset_index 展平索引
-    sma3 = grouped['close'].rolling(3).mean().reset_index(level=0, drop=True)
-    sma6 = grouped['close'].rolling(6).mean().reset_index(level=0, drop=True)
-    sma12 = grouped['close'].rolling(12).mean().reset_index(level=0, drop=True)
-    sma24 = grouped['close'].rolling(24).mean().reset_index(level=0, drop=True)
+    sma3 = grouped["close"].rolling(3).mean().reset_index(level=0, drop=True)
+    sma6 = grouped["close"].rolling(6).mean().reset_index(level=0, drop=True)
+    sma12 = grouped["close"].rolling(12).mean().reset_index(level=0, drop=True)
+    sma24 = grouped["close"].rolling(24).mean().reset_index(level=0, drop=True)
 
     # 2. 计算 BBI = (SMA3 + SMA6 + SMA12 + SMA24) / 4
     print("计算 BBI...")
@@ -2506,13 +2543,13 @@ def bbi_ratio_factor(df: pd.DataFrame) -> pd.DataFrame:
     bbi = (sma3 + sma6 + sma12 + sma24) / 4
 
     # 3. 计算最终因子 = BBI / 收盘价 (使用安全除法)
-    df_factor['bbi_ratio_factor'] = _safe_divide(bbi, close_prices)
+    df_factor["bbi_ratio_factor"] = _safe_divide(bbi, close_prices)
 
     return df_factor
 
 
 def limit_factor(df: pd.DataFrame) -> pd.DataFrame:
-    grouped = df.groupby('ts_code', group_keys=False)
+    grouped = df.groupby("ts_code", group_keys=False)
     df["cat_up_limit"] = (
         df["close"] == df["up_limit"]
     )  # 是否涨停（1表示涨停，0表示未涨停）
@@ -2558,6 +2595,7 @@ import numpy as np
 # 假设 df 已经加载并包含 'ts_code', 'trade_date', 'pct_chg' 列
 # 并且已经按照 'ts_code' 和 'trade_date' 进行了排序
 
+
 def daily_momentum_benchmark(df):
     """
     计算日级别动量基准 (Positive and Negative)，使用现有的 'pct_chg' 列。
@@ -2578,34 +2616,37 @@ def daily_momentum_benchmark(df):
     print("--- 计算日级别动量基准 (使用 pct_chg) ---")
 
     # 确保 pct_chg 列存在
-    if 'pct_chg' not in df.columns:
+    if "pct_chg" not in df.columns:
         print("错误: DataFrame中没有'pct_chg'列，无法计算日级别动量基准。")
         return df
 
     # 计算每日的全市场动量基准
     # 对于每一个交易日，计算所有股票中 pct_chg > 0 和 < 0 的平均值
     # 使用 trade_date 进行分组
-    daily_benchmarks = df.groupby('trade_date')['pct_chg'].agg(
-        daily_positive_benchmark = lambda x: x[x > 0].mean(), # 日级别上涨股票的平均涨跌幅
-        daily_negative_benchmark = lambda x: x[x < 0].mean()  # 日级别下跌股票的平均涨跌幅
-    ).reset_index()
-
-    # 将日级别动量基准合并回原始日线数据DataFrame
-    df = pd.merge(
-        df,
-        daily_benchmarks,
-        on='trade_date',
-        how='left'
+    daily_benchmarks = (
+        df.groupby("trade_date")["pct_chg"]
+        .agg(
+            daily_positive_benchmark=lambda x: x[
+                x > 0
+            ].mean(),  # 日级别上涨股票的平均涨跌幅
+            daily_negative_benchmark=lambda x: x[
+                x < 0
+            ].mean(),  # 日级别下跌股票的平均涨跌幅
+        )
+        .reset_index()
     )
 
-    # 对可能出现的NaN基准进行填充，这里用0填充表示没有对应的同向基准
-    df['daily_positive_benchmark'].fillna(0, inplace=True)
-    df['daily_negative_benchmark'].fillna(0, inplace=True)
+    # 将日级别动量基准合并回原始日线数据DataFrame
+    df = pd.merge(df, daily_benchmarks, on="trade_date", how="left")
 
+    # 对可能出现的NaN基准进行填充，这里用0填充表示没有对应的同向基准
+    df["daily_positive_benchmark"].fillna(0, inplace=True)
+    df["daily_negative_benchmark"].fillna(0, inplace=True)
 
     print("日级别动量基准计算完成 (使用 pct_chg)。")
     return df
 
+
 def daily_deviation(df):
     """
     计算日级别偏离度，使用现有的 'pct_chg' 列和计算出的日级别动量基准。
@@ -2625,22 +2666,24 @@ def daily_deviation(df):
 
     # 确保所需的列存在
     df = daily_momentum_benchmark(df)
-    required_cols = ['pct_chg', 'daily_positive_benchmark', 'daily_negative_benchmark']
+    required_cols = ["pct_chg", "daily_positive_benchmark", "daily_negative_benchmark"]
     if not all(col in df.columns for col in required_cols):
-        print(f"错误: 计算日级别偏离度需要以下列: {required_cols}。请先运行 daily_momentum_benchmark(df)。")
+        print(
+            f"错误: 计算日级别偏离度需要以下列: {required_cols}。请先运行 daily_momentum_benchmark(df)。"
+        )
         return df
 
     conditions = [
-        (df['pct_chg'] > 0) & (df['daily_positive_benchmark'] > 0),
-        (df['pct_chg'] < 0) & (df['daily_negative_benchmark'] < 0)
+        (df["pct_chg"] > 0) & (df["daily_positive_benchmark"] > 0),
+        (df["pct_chg"] < 0) & (df["daily_negative_benchmark"] < 0),
     ]
     choices = [
-        df['pct_chg'] - df['daily_positive_benchmark'],
-        df['pct_chg'] - df['daily_negative_benchmark']
+        df["pct_chg"] - df["daily_positive_benchmark"],
+        df["pct_chg"] - df["daily_negative_benchmark"],
     ]
-    df['daily_deviation'] = np.select(conditions, choices, default=0)
+    df["daily_deviation"] = np.select(conditions, choices, default=0)
 
-    df = df.drop(columns=['daily_positive_benchmark', 'daily_negative_benchmark'])
+    df = df.drop(columns=["daily_positive_benchmark", "daily_negative_benchmark"])
     print("日级别偏离度计算完成 (使用 pct_chg)。")
     return df
 
@@ -2664,36 +2707,41 @@ def daily_industry_momentum_benchmark(df):
     print("--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---")
 
     # 确保必需列存在
-    required_cols = ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']
+    required_cols = ["pct_chg", "cat_l2_code", "trade_date", "ts_code"]
     if not all(col in df.columns for col in required_cols):
         print(f"错误: 计算日级别行业动量基准需要以下列: {required_cols}。")
         return df
 
     # 计算每日每个行业内部的动量基准
     # 使用 trade_date 和 cat_l2_code 进行分组
-    industry_daily_benchmarks = df.groupby(['trade_date', 'cat_l2_code'])['pct_chg'].agg(
-        daily_industry_positive_benchmark = lambda x: x[x > 0].mean(), # 日级别行业内上涨股票的平均涨跌幅
-        daily_industry_negative_benchmark = lambda x: x[x < 0].mean()  # 日级别行业内下跌股票的平均涨跌幅
-    ).reset_index()
+    industry_daily_benchmarks = (
+        df.groupby(["trade_date", "cat_l2_code"])["pct_chg"]
+        .agg(
+            daily_industry_positive_benchmark=lambda x: x[
+                x > 0
+            ].mean(),  # 日级别行业内上涨股票的平均涨跌幅
+            daily_industry_negative_benchmark=lambda x: x[
+                x < 0
+            ].mean(),  # 日级别行业内下跌股票的平均涨跌幅
+        )
+        .reset_index()
+    )
 
     # 将日级别行业动量基准合并回原始日线数据DataFrame
     # 使用 trade_date 和 cat_l2_code 进行 merge
     df = pd.merge(
-        df,
-        industry_daily_benchmarks,
-        on=['trade_date', 'cat_l2_code'],
-        how='left'
+        df, industry_daily_benchmarks, on=["trade_date", "cat_l2_code"], how="left"
     )
 
     # 对可能出现的NaN基准进行填充（例如某个行业某一天没有上涨或下跌的股票）
     # 这里用0填充表示该行业该天没有对应的同向基准
-    df['daily_industry_positive_benchmark'].fillna(0, inplace=True)
-    df['daily_industry_negative_benchmark'].fillna(0, inplace=True)
-
+    df["daily_industry_positive_benchmark"].fillna(0, inplace=True)
+    df["daily_industry_negative_benchmark"].fillna(0, inplace=True)
 
     print("日级别行业动量基准计算完成 (使用 pct_chg 和 cat_l2_code)。")
     return df
 
+
 def daily_industry_deviation(df):
     """
     计算日级别行业偏离度，使用现有的 'pct_chg' 列和计算出的日级别行业动量基准。
@@ -2713,9 +2761,15 @@ def daily_industry_deviation(df):
 
     # 确保所需的列存在
     df = daily_industry_momentum_benchmark(df)
-    required_cols = ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']
+    required_cols = [
+        "pct_chg",
+        "daily_industry_positive_benchmark",
+        "daily_industry_negative_benchmark",
+    ]
     if not all(col in df.columns for col in required_cols):
-        print(f"错误: 计算日级别行业偏离度需要以下列: {required_cols}。请先运行 daily_industry_momentum_benchmark(df)。")
+        print(
+            f"错误: 计算日级别行业偏离度需要以下列: {required_cols}。请先运行 daily_industry_momentum_benchmark(df)。"
+        )
         return df
 
     # 根据规则计算日级别行业偏离度：
@@ -2724,61 +2778,86 @@ def daily_industry_deviation(df):
     # 否则 deviation = 0
 
     conditions = [
-        (df['pct_chg'] > 0) & (df['daily_industry_positive_benchmark'] > 0),
-        (df['pct_chg'] < 0) & (df['daily_industry_negative_benchmark'] < 0)
+        (df["pct_chg"] > 0) & (df["daily_industry_positive_benchmark"] > 0),
+        (df["pct_chg"] < 0) & (df["daily_industry_negative_benchmark"] < 0),
     ]
     choices = [
-        df['pct_chg'] - df['daily_industry_positive_benchmark'],
-        df['pct_chg'] - df['daily_industry_negative_benchmark']
+        df["pct_chg"] - df["daily_industry_positive_benchmark"],
+        df["pct_chg"] - df["daily_industry_negative_benchmark"],
     ]
-    df['daily_industry_deviation'] = np.select(conditions, choices, default=0)
+    df["daily_industry_deviation"] = np.select(conditions, choices, default=0)
 
-    df = df.drop(columns=['daily_industry_positive_benchmark', 'daily_industry_negative_benchmark'])
+    df = df.drop(
+        columns=[
+            "daily_industry_positive_benchmark",
+            "daily_industry_negative_benchmark",
+        ]
+    )
     print("日级别行业偏离度计算完成 (使用 pct_chg 和行业基准)。")
     return df
 
 
-def sentiment_panic_greed_index(df: pd.DataFrame, window_atr: int = 14, window_smooth: int = 5, factor_name: str = 'senti_panic_greed'):
+def sentiment_panic_greed_index(
+    df: pd.DataFrame,
+    window_atr: int = 14,
+    window_smooth: int = 5,
+    factor_name: str = "senti_panic_greed",
+):
     """
     计算市场恐慌/贪婪指数 (原地修改)。
     结合日内振幅、影线、跳空及与近期ATR的比较。
     WARNING: Modifies df in-place.
     """
     print(f"Calculating {factor_name}...")
-    _temp_cols = ['_prev_close', '_atr', '_true_range', '_upper_shadow', '_lower_shadow', '_body', '_gap', '_volatility_surprise']
-    if not all(col in df.columns for col in ['open', 'high', 'low', 'close', 'vol']):
+    _temp_cols = [
+        "_prev_close",
+        "_atr",
+        "_true_range",
+        "_upper_shadow",
+        "_lower_shadow",
+        "_body",
+        "_gap",
+        "_volatility_surprise",
+    ]
+    if not all(col in df.columns for col in ["open", "high", "low", "close", "vol"]):
         print(f"Error: DataFrame 缺少必需的 OHLCV 列。将为 {factor_name} 填充 NaN。")
         df[factor_name] = np.nan
         return
-    
+
     try:
-        df['_prev_close'] = df['close'].shift(1)
+        df["_prev_close"] = df["close"].shift(1)
 
         # 计算真实波幅 (TR) 和 ATR
-        df['_true_range'] = talib.TRANGE(df['high'], df['low'], df['_prev_close'])
-        df['_atr'] = talib.ATR(df['high'], df['low'], df['_prev_close'], timeperiod=window_atr)
+        df["_true_range"] = talib.TRANGE(df["high"], df["low"], df["_prev_close"])
+        df["_atr"] = talib.ATR(
+            df["high"], df["low"], df["_prev_close"], timeperiod=window_atr
+        )
 
         # 计算影线和实体
-        df['_upper_shadow'] = df['high'] - np.maximum(df['open'], df['close'])
-        df['_lower_shadow'] = np.minimum(df['open'], df['close']) - df['low']
-        df['_body'] = np.abs(df['close'] - df['open'])
+        df["_upper_shadow"] = df["high"] - np.maximum(df["open"], df["close"])
+        df["_lower_shadow"] = np.minimum(df["open"], df["close"]) - df["low"]
+        df["_body"] = np.abs(df["close"] - df["open"])
 
         # 计算跳空
-        df['_gap'] = (df['open'] / df['_prev_close'] - 1).fillna(0)
+        df["_gap"] = (df["open"] / df["_prev_close"] - 1).fillna(0)
 
         # 波动性意外: 当日真实波幅相对于近期ATR的倍数，乘以涨跌方向
         # 如果真实波幅显著放大，根据涨跌幅赋予正负号，表明情绪的强度和方向
-        df['_volatility_surprise'] = (df['_true_range'] / (df['_atr'] + epsilon) -1) * np.sign(df['pct_chg'].fillna(0))
-
+        df["_volatility_surprise"] = (
+            df["_true_range"] / (df["_atr"] + epsilon) - 1
+        ) * np.sign(df["pct_chg"].fillna(0))
 
         # 简化版情绪指标：(下影线 - 上影线) / ATR + 跳空幅度 + 当日涨跌幅, 然后平滑
         # 更强的信号：波动性意外，结合跳空
         # 考虑当日振幅相对于ATR的超额部分，并结合实体方向
         # ( (真实波幅/ATR) * 涨跌方向 ) + 跳空幅度
-        raw_senti = (df['_true_range'] / (df['_atr'] + epsilon)) * np.sign(df['pct_chg'].fillna(0)) + df['_gap'] * 2 # 放大跳空影响
+        raw_senti = (df["_true_range"] / (df["_atr"] + epsilon)) * np.sign(
+            df["pct_chg"].fillna(0)
+        ) + df[
+            "_gap"
+        ] * 2  # 放大跳空影响
         df[factor_name] = raw_senti.rolling(window_smooth, min_periods=1).mean()
 
-
     except Exception as e:
         print(f"Error calculating {factor_name}: {e}")
         df[factor_name] = np.nan
@@ -2790,23 +2869,34 @@ def sentiment_panic_greed_index(df: pd.DataFrame, window_atr: int = 14, window_s
         return df
 
 
-def sentiment_market_breadth_proxy(df: pd.DataFrame, window_vol: int = 20, window_smooth: int = 3, factor_name: str = 'senti_breadth_proxy'):
+def sentiment_market_breadth_proxy(
+    df: pd.DataFrame,
+    window_vol: int = 20,
+    window_smooth: int = 3,
+    factor_name: str = "senti_breadth_proxy",
+):
     """
     计算市场宽度情绪代理指标 (基于指数的价量配合度) (原地修改).
     WARNING: Modifies df in-place.
     """
     print(f"Calculating {factor_name}...")
-    _temp_cols = ['_rolling_avg_vol']
-    if not all(col in df.columns for col in ['pct_chg', 'vol']):
-        print(f"Error: DataFrame 缺少 'pct_chg' 或 'vol' 列。将为 {factor_name} 填充 NaN。")
+    _temp_cols = ["_rolling_avg_vol"]
+    if not all(col in df.columns for col in ["pct_chg", "vol"]):
+        print(
+            f"Error: DataFrame 缺少 'pct_chg' 或 'vol' 列。将为 {factor_name} 填充 NaN。"
+        )
         df[factor_name] = np.nan
         return
 
     try:
-        df['_rolling_avg_vol'] = df['vol'].rolling(window_vol, min_periods=max(1, window_vol//2)).mean()
+        df["_rolling_avg_vol"] = (
+            df["vol"].rolling(window_vol, min_periods=max(1, window_vol // 2)).mean()
+        )
         # 价量配合度：涨跌幅乘以相对成交量强度
-        raw_breadth = df['pct_chg'] * (df['vol'] / (df['_rolling_avg_vol'] + epsilon))
-        df[factor_name] = raw_breadth.rolling(window_smooth, min_periods=1).mean() # 平滑处理
+        raw_breadth = df["pct_chg"] * (df["vol"] / (df["_rolling_avg_vol"] + epsilon))
+        df[factor_name] = raw_breadth.rolling(
+            window_smooth, min_periods=1
+        ).mean()  # 平滑处理
 
     except Exception as e:
         print(f"Error calculating {factor_name}: {e}")
@@ -2817,29 +2907,37 @@ def sentiment_market_breadth_proxy(df: pd.DataFrame, window_vol: int = 20, windo
             df.drop(columns=cols_to_drop, inplace=True)
         print(f"Finished {factor_name}.")
         return df
-    
-def sentiment_reversal_indicator(df: pd.DataFrame, window_ret: int = 5, window_vol: int = 5, factor_name: str = 'senti_reversal'):
+
+
+def sentiment_reversal_indicator(
+    df: pd.DataFrame,
+    window_ret: int = 5,
+    window_vol: int = 5,
+    factor_name: str = "senti_reversal",
+):
     """
     计算短期情绪反转因子 (原地修改).
     WARNING: Modifies df in-place.
     """
     print(f"Calculating {factor_name}...")
-    _temp_cols = ['_return_M', '_volatility_M']
-    if 'pct_chg' not in df.columns:
+    _temp_cols = ["_return_M", "_volatility_M"]
+    if "pct_chg" not in df.columns:
         print(f"Error: DataFrame 缺少 'pct_chg' 列。将为 {factor_name} 填充 NaN。")
         df[factor_name] = np.nan
         return
     try:
         # 计算 M 日累计收益率 (这里用连乘近似，或者 sum of log returns)
         # (close / close.shift(M)) -1
-        df['_return_M'] = (df['close'] / df['close'].shift(window_ret)) - 1
+        df["_return_M"] = (df["close"] / df["close"].shift(window_ret)) - 1
         # df['_return_M'] = df['pct_chg'].rolling(window_ret, min_periods=1).sum() # 另一种近似
 
         # 计算 M 日已实现波动率
-        df['_volatility_M'] = df['pct_chg'].rolling(window_vol, min_periods=max(1, window_vol//2)).std()
+        df["_volatility_M"] = (
+            df["pct_chg"].rolling(window_vol, min_periods=max(1, window_vol // 2)).std()
+        )
 
         # 因子计算
-        df[factor_name] = -df['_return_M'] * df['_volatility_M']
+        df[factor_name] = -df["_return_M"] * df["_volatility_M"]
         # 对因子本身可以再做一次平滑
         # df[factor_name] = df[factor_name].rolling(3, min_periods=1).mean()
 
@@ -2868,28 +2966,29 @@ def price_minus_deduction_price(df, n=10):
     返回:
     pd.DataFrame: 增加了 'price_minus_deduction_price_n' 列的DataFrame。
     """
-    if 'close' not in df.columns:
+    if "close" not in df.columns:
         print("错误: DataFrame中没有'close'列，无法计算抵扣价相关因子。")
         return df
 
     if n <= 1:
         print("错误: 均线周期 n 必须大于 1。")
-        df[f'price_minus_deduction_price_{n}'] = np.nan
+        df[f"price_minus_deduction_price_{n}"] = np.nan
         return df
 
-    grouped = df.groupby('ts_code', group_keys=False)
+    grouped = df.groupby("ts_code", group_keys=False)
 
     # 抵扣价是当前窗口移除的最早的价格，即当前价格的 n-1 周期前的价格
     # 例如计算 SMA(10) for P_t, 窗口是 P_{t-9}, ..., P_t. 移除的是 P_{t-9}.
     # P_{t-9} 是 P_t 的 shift(9). So shift(n-1).
-    deduction_price = grouped['close'].shift(n - 1)
+    deduction_price = grouped["close"].shift(n - 1)
 
     # 计算差值
-    df[f'price_minus_deduction_price_{n}'] = df['close'] - deduction_price
+    df[f"price_minus_deduction_price_{n}"] = df["close"] - deduction_price
 
     print(f"因子 price_minus_deduction_price_{n} 计算完成。")
     return df
 
+
 def price_deduction_price_diff_ratio_to_sma(df, n=10):
     """
     因子 2 (定量): 计算当前收盘价与抵扣价的差值占N周期均线的比例。
@@ -2903,32 +3002,32 @@ def price_deduction_price_diff_ratio_to_sma(df, n=10):
     返回:
     pd.DataFrame: 增加了 'price_deduction_price_diff_ratio_to_sma_n' 列的DataFrame。
     """
-    if 'close' not in df.columns:
+    if "close" not in df.columns:
         print("错误: DataFrame中没有'close'列，无法计算抵扣价相关因子。")
         return df
     if n <= 1:
         print("错误: 均线周期 n 必须大于 1。")
-        df[f'price_deduction_price_diff_ratio_to_sma_{n}'] = np.nan
+        df[f"price_deduction_price_diff_ratio_to_sma_{n}"] = np.nan
         return df
 
-    grouped = df.groupby('ts_code', group_keys=False)
+    grouped = df.groupby("ts_code", group_keys=False)
 
     # 计算N周期SMA
     # 使用 transform 可以保持与原始 df 的索引对齐
-    sma = grouped['close'].transform(lambda x: x.rolling(window=n).mean())
+    sma = grouped["close"].transform(lambda x: x.rolling(window=n).mean())
 
     # 抵扣价
-    deduction_price = grouped['close'].shift(n - 1)
+    deduction_price = grouped["close"].shift(n - 1)
 
     # 计算比例，处理均线为零的情况
-    diff = df['close'] - deduction_price
+    diff = df["close"] - deduction_price
     # 使用 np.divide 并指定 where 条件和 fill_value 来避免除以零警告和 NaN 结果
     # 如果 sma 为 0，则结果设为 NaN
-    df[f'price_deduction_price_diff_ratio_to_sma_{n}'] = np.divide(
+    df[f"price_deduction_price_diff_ratio_to_sma_{n}"] = np.divide(
         diff,
         sma,
-        out=np.full_like(diff, np.nan), # 输出数组形状与 diff 相同，NaN 填充
-        where=(sma != 0) # 仅在 sma 不为 0 时执行除法
+        out=np.full_like(diff, np.nan),  # 输出数组形状与 diff 相同，NaN 填充
+        where=(sma != 0),  # 仅在 sma 不为 0 时执行除法
     )
 
     # np.divide with where handles Inf/-Inf and 0/0 (as NaN), but explicitly replace might be slightly safer depending on numpy version
@@ -2958,46 +3057,51 @@ def cat_price_vs_sma_vs_deduction_price(df, n=10):
     返回:
     pd.DataFrame: 增加了 'cat_price_vs_sma_vs_deduction_price_n' 列的DataFrame。
     """
-    if 'close' not in df.columns:
+    if "close" not in df.columns:
         print("错误: DataFrame中没有'close'列，无法计算抵扣价相关因子。")
         return df
     if n <= 1:
         print("错误: 均线周期 n 必须大于 1。")
-        df[f'cat_price_vs_sma_vs_deduction_price_{n}'] = np.nan
+        df[f"cat_price_vs_sma_vs_deduction_price_{n}"] = np.nan
         return df
 
-    grouped = df.groupby('ts_code', group_keys=False)
+    grouped = df.groupby("ts_code", group_keys=False)
 
     # 计算N周期SMA
-    sma = grouped['close'].transform(lambda x: x.rolling(window=n).mean())
+    sma = grouped["close"].transform(lambda x: x.rolling(window=n).mean())
 
     # 抵扣价
-    deduction_price = grouped['close'].shift(n - 1)
+    deduction_price = grouped["close"].shift(n - 1)
 
     # 定义条件和分类值
     conditions = [
-        (df['close'] > sma) & (deduction_price > sma),
-        (df['close'] < sma) & (deduction_price < sma),
-        (df['close'] > sma) & (deduction_price <= sma), # 包含等于的情况
-        (df['close'] <= sma) & (deduction_price > sma)  # 包含等于的情况
+        (df["close"] > sma) & (deduction_price > sma),
+        (df["close"] < sma) & (deduction_price < sma),
+        (df["close"] > sma) & (deduction_price <= sma),  # 包含等于的情况
+        (df["close"] <= sma) & (deduction_price > sma),  # 包含等于的情况
         # 注意：sma 或 deduction_price 为 NaN 的行，其条件结果为 False，会落入 default=0
     ]
     choices = [1, 2, 3, 4]
 
     # 使用 np.select 进行分类
     # 默认值为 0，用于处理条件不满足或输入为 NaN 的情况
-    df[f'cat_price_vs_sma_vs_deduction_price_{n}'] = np.select(conditions, choices, default=0)
+    df[f"cat_price_vs_sma_vs_deduction_price_{n}"] = np.select(
+        conditions, choices, default=0
+    )
 
     print(f"因子 cat_price_vs_sma_vs_deduction_price_{n} 计算完成。")
     return df
 
+
 def cat_is_on_top_list(df: pd.DataFrame, top_list: pd.DataFrame) -> pd.DataFrame:
-    if 'cat_reason' not in df.columns:
-        print('计算因子cat_is_on_top_list失败，缺少cat_reason列')
+    if "cat_reason" not in df.columns:
+        print("计算因子cat_is_on_top_list失败，缺少cat_reason列")
         return df
 
-    df['cat_is_on_top_list'] = df['cat_reason']
-    df['cat_is_on_top_list'] = df['cat_is_on_top_list'] * (df['pct_chg'] > 0).astype(int)
+    df["cat_is_on_top_list"] = df["cat_reason"]
+    df["cat_is_on_top_list"] = df["cat_is_on_top_list"] * (df["pct_chg"] > 0).astype(
+        int
+    )
 
     return df
 
@@ -3014,23 +3118,238 @@ def cat_reason(df: pd.DataFrame, top_list: pd.DataFrame) -> pd.DataFrame:
         pd.DataFrame: 包含 'ts_code', 'trade_date' 和 'cat_reason' 列。
     """
     # 提取所有唯一的 reason 并进行数值编码
-    unique_reasons = top_list['reason'].unique()
+    unique_reasons = top_list["reason"].unique()
     reason_mapping = {reason: i + 1 for i, reason in enumerate(unique_reasons)}
 
     # 在 top_list 中创建数值型的 reason 列
-    top_list['cat_reason'] = top_list['reason'].map(reason_mapping).astype('Int64')
+    top_list["cat_reason"] = top_list["reason"].map(reason_mapping).astype("Int64")
 
     # 转换 trade_date 类型以进行合并
-    df['trade_date'] = pd.to_datetime(df['trade_date'])
-    top_list['trade_date'] = pd.to_datetime(top_list['trade_date'])
+    df["trade_date"] = pd.to_datetime(df["trade_date"])
+    top_list["trade_date"] = pd.to_datetime(top_list["trade_date"])
 
     # 仅保留 top_list 中需要的列进行合并
-    top_list_slim = top_list[['ts_code', 'trade_date', 'cat_reason']]
+    top_list_slim = top_list[["ts_code", "trade_date", "cat_reason"]]
 
     # 合并 DataFrame
-    merged_df = df.merge(top_list_slim, on=['ts_code', 'trade_date'], how='left')
+    merged_df = df.merge(top_list_slim, on=["ts_code", "trade_date"], how="left")
 
     # 填充 NaN 为 0 并转换为 int 类型
-    merged_df['cat_reason'] = merged_df['cat_reason'].fillna(0).astype('int')
+    merged_df["cat_reason"] = merged_df["cat_reason"].fillna(0).astype("int")
 
-    return merged_df
\ No newline at end of file
+    return merged_df
+
+
+def ts_volatility_slope_20_5(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    计算 20 日收益率标准差的 5 日线性回归斜率因子。
+
+    Args:
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date' 和 'pct_chg' 列的 DataFrame。
+
+    Returns:
+        pd.DataFrame: 包含新增 'ts_volatility_slope_20_5' 列的 DataFrame。
+    """
+    df["trade_date"] = pd.to_datetime(df["trade_date"])
+    df.sort_values(["ts_code", "trade_date"], inplace=True)
+
+    def std_slope(series):
+        if len(series) < 2:
+            return 0
+        x = np.arange(len(series))
+        slope, _, _, _, _ = linregress(x, series)
+        return slope
+
+    df["volatility_20"] = (
+        df.groupby("ts_code")["pct_chg"]
+        .rolling(window=20, min_periods=1)
+        .std()
+        .reset_index(level=0, drop=True)
+    )
+    df["ts_volatility_slope_20_5"] = (
+        df.groupby("ts_code")["volatility_20"]
+        .rolling(window=5, min_periods=2)
+        .apply(std_slope)
+        .reset_index(level=0, drop=True)
+    )
+    df.drop(columns=["volatility_20"], inplace=True)
+    return df
+
+
+def ts_turnover_rate_acceleration_5_20(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    计算短期 (5日) 和长期 (20日) 换手率均值的差值因子。
+
+    Args:
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date' 和 'turnover_rate' 列的 DataFrame。
+
+    Returns:
+        pd.DataFrame: 包含新增 'ts_turnover_rate_acceleration_5_20' 列的 DataFrame。
+    """
+    df['trade_date'] = pd.to_datetime(df['trade_date'])
+    df.sort_values(['ts_code', 'trade_date'], inplace=True)
+    df['short_avg_turnover'] = df.groupby('ts_code')['turnover_rate'].rolling(window=5, min_periods=1).mean().reset_index(level=0, drop=True)
+    df['long_avg_turnover'] = df.groupby('ts_code')['turnover_rate'].rolling(window=20, min_periods=1).mean().reset_index(level=0, drop=True)
+    df['ts_turnover_rate_acceleration_5_20'] = df['short_avg_turnover'] - df['long_avg_turnover']
+    df.drop(columns=['short_avg_turnover', 'long_avg_turnover'], inplace=True)
+    return df
+
+def ts_vol_sustain_10_30(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    计算过去 10 日成交量大于 30 日均值的交易天数占比因子。
+
+    Args:
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date' 和 'vol' 列的 DataFrame。
+
+    Returns:
+        pd.DataFrame: 包含新增 'ts_vol_sustain_10_30' 列的 DataFrame。
+    """
+    df['trade_date'] = pd.to_datetime(df['trade_date'])
+    df.sort_values(['ts_code', 'trade_date'], inplace=True)
+    df['long_avg_vol'] = df.groupby('ts_code')['vol'].rolling(window=30, min_periods=1).mean().reset_index(level=0, drop=True)
+
+    def vol_above_avg(group):
+        group['vol_above'] = group['vol'] > group['long_avg_vol']
+        group['ts_vol_sustain_10_30'] = group['vol_above'].rolling(window=10, min_periods=1).mean()
+        return group.drop(columns=['vol_above'])
+
+    df = df.groupby('ts_code', group_keys=False).apply(vol_above_avg)
+    df.drop(columns=['long_avg_vol'], inplace=True)
+    return df
+
+def cs_turnover_rate_relative_strength_20(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    计算当日换手率 vs 20 日均值比值的横截面排名因子。
+
+    Args:
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date' 和 'turnover_rate' 列的 DataFrame。
+
+    Returns:
+        pd.DataFrame: 包含新增 'cs_turnover_rate_relative_strength_20' 列的 DataFrame。
+    """
+    df['trade_date'] = pd.to_datetime(df['trade_date'])
+    df.sort_values(['ts_code', 'trade_date'], inplace=True)
+
+    def calculate_ratio(group):
+        group['avg_turnover_20'] = group['turnover_rate'].rolling(window=20, min_periods=1).mean()
+        group['turnover_ratio'] = group['turnover_rate'] / group['avg_turnover_20']
+        return group.drop(columns=['avg_turnover_20'])
+
+    df = df.groupby('ts_code', group_keys=False).apply(calculate_ratio)
+
+    def rank_ratios(group):
+        group['cs_turnover_rate_relative_strength_20'] = group['turnover_ratio'].rank(method='dense', ascending=False)
+        return group.drop(columns=['turnover_ratio'])
+
+    df = df.groupby('trade_date', group_keys=False).apply(rank_ratios)
+    return df
+
+def cs_amount_outlier_10(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    计算当日成交额 vs 10 日均值差值的横截面 Z-score 因子。
+
+    Args:
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date' 和 'amount' 列的 DataFrame。
+
+    Returns:
+        pd.DataFrame: 包含新增 'cs_amount_outlier_10' 列的 DataFrame。
+    """
+    df['trade_date'] = pd.to_datetime(df['trade_date'])
+    df.sort_values(['ts_code', 'trade_date'], inplace=True)
+
+    def calculate_diff(group):
+        group['avg_amount_10'] = group['amount'].rolling(window=10, min_periods=1).mean()
+        group['amount_diff'] = group['amount'] - group['avg_amount_10']
+        return group.drop(columns=['avg_amount_10'])
+
+    df = df.groupby('ts_code', group_keys=False).apply(calculate_diff)
+
+    def zscore_diff(group):
+        mean_diff = group['amount_diff'].mean()
+        std_diff = group['amount_diff'].std()
+        if std_diff == 0:
+            group['cs_amount_outlier_10'] = 0
+        else:
+            group['cs_amount_outlier_10'] = (group['amount_diff'] - mean_diff) / std_diff
+        return group.drop(columns=['amount_diff'])
+
+    df = df.groupby('trade_date', group_keys=False).apply(zscore_diff)
+    return df
+
+def ts_ff_to_total_turnover_ratio(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    计算自由流通股换手率与总换手率之比。
+
+    Args:
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date', 'turnover_rate' 和 'turnover_rate_ff' 列的 DataFrame。
+
+    Returns:
+        pd.DataFrame: 包含新增 'ts_ff_to_total_turnover_ratio' 列的 DataFrame。
+    """
+    df['ts_ff_to_total_turnover_ratio'] = df['turnover_rate_ff'] / (df['turnover_rate'] + 1e-8) # 避免除零
+    return df
+
+
+def ts_price_volume_trend_coherence_5_20(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    计算过去 5 日价格上涨占比与过去 5 日成交量高于 20 日均量占比的乘积。
+
+    Args:
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date', 'close' 和 'vol' 列的 DataFrame。
+
+    Returns:
+        pd.DataFrame: 包含新增 'ts_price_volume_trend_coherence_5_20' 列的 DataFrame。
+    """
+    df['trade_date'] = pd.to_datetime(df['trade_date'])
+    df.sort_values(['ts_code', 'trade_date'], inplace=True)
+
+    def price_up_days(series):
+        return (series.diff() > 0).rolling(window=5, min_periods=1).mean()
+
+    df['price_up_ratio'] = df.groupby('ts_code')['close'].apply(price_up_days).reset_index(level=0, drop=True)
+    df['vol_avg_20'] = df.groupby('ts_code')['vol'].rolling(window=20, min_periods=1).mean().reset_index(level=0, drop=True)
+    df['vol_above_avg'] = (df['vol'] > df['vol_avg_20']).rolling(window=5, min_periods=1).mean()
+    df['ts_price_volume_trend_coherence_5_20'] = df['price_up_ratio'] * df['vol_above_avg']
+    df.drop(columns=['price_up_ratio', 'vol_avg_20', 'vol_above_avg'], inplace=True)
+    return df
+
+def ts_turnover_rate_trend_strength_5(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    计算过去 5 日换手率的线性回归斜率。
+
+    Args:
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date' 和 'turnover_rate' 列的 DataFrame。
+
+    Returns:
+        pd.DataFrame: 包含新增 'ts_turnover_rate_trend_strength_5' 列的 DataFrame。
+    """
+    df['trade_date'] = pd.to_datetime(df['trade_date'])
+    df.sort_values(['ts_code', 'trade_date'], inplace=True)
+
+    def turnover_slope(series):
+        if len(series) < 2:
+            return 0
+        x = np.arange(len(series))
+        slope, _, _, _, _ = linregress(x, series)
+        return slope
+
+    df['ts_turnover_rate_trend_strength_5'] = df.groupby('ts_code')['turnover_rate'].rolling(window=5, min_periods=2).apply(turnover_slope).reset_index(level=0, drop=True)
+    return df
+
+
+def ts_ff_turnover_rate_surge_10(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    计算当日自由流通股换手率与过去 10 日均值比值。
+
+    Args:
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date' 和 'turnover_rate_ff' 列的 DataFrame。
+
+    Returns:
+        pd.DataFrame: 包含新增 'ts_ff_turnover_rate_surge_10' 列的 DataFrame。
+    """
+    df['trade_date'] = pd.to_datetime(df['trade_date'])
+    df.sort_values(['ts_code', 'trade_date'], inplace=True)
+    df['avg_ff_turnover_10'] = df.groupby('ts_code')['turnover_rate_ff'].rolling(window=10, min_periods=1).mean().reset_index(level=0, drop=True)
+    df['ts_ff_turnover_rate_surge_10'] = df['turnover_rate_ff'] / (df['avg_ff_turnover_10'] + 1e-8) # 避免除零
+    df.drop(columns=['avg_ff_turnover_10'], inplace=True)
+    return df
diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb
index 18c68ad..aa6e54b 100644
--- a/main/train/Classify2.ipynb
+++ b/main/train/Classify2.ipynb
@@ -39,12 +39,24 @@
     "\n",
     "import warnings\n",
     "\n",
-    "warnings.filterwarnings(\"ignore\")"
+    "warnings.filterwarnings(\"ignore\")\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "4a481c60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 设置使用核心\n",
+    "import os\n",
+    "os.environ[\"MODIN_CPUS\"] = \"4\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -58,13 +70,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "daily data\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "daily data\n",
       "daily basic\n",
       "inner merge on ['ts_code', 'trade_date']\n",
       "stk limit\n",
@@ -75,7 +81,7 @@
       "left merge on ['ts_code', 'trade_date']\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 8665405 entries, 0 to 8665404\n",
-      "Data columns (total 32 columns):\n",
+      "Data columns (total 33 columns):\n",
       " #   Column         Dtype         \n",
       "---  ------         -----         \n",
       " 0   ts_code        object        \n",
@@ -85,33 +91,34 @@
       " 4   high           float64       \n",
       " 5   low            float64       \n",
       " 6   vol            float64       \n",
-      " 7   pct_chg        float64       \n",
-      " 8   turnover_rate  float64       \n",
-      " 9   pe_ttm         float64       \n",
-      " 10  circ_mv        float64       \n",
-      " 11  total_mv       float64       \n",
-      " 12  volume_ratio   float64       \n",
-      " 13  is_st          bool          \n",
-      " 14  up_limit       float64       \n",
-      " 15  down_limit     float64       \n",
-      " 16  buy_sm_vol     float64       \n",
-      " 17  sell_sm_vol    float64       \n",
-      " 18  buy_lg_vol     float64       \n",
-      " 19  sell_lg_vol    float64       \n",
-      " 20  buy_elg_vol    float64       \n",
-      " 21  sell_elg_vol   float64       \n",
-      " 22  net_mf_vol     float64       \n",
-      " 23  his_low        float64       \n",
-      " 24  his_high       float64       \n",
-      " 25  cost_5pct      float64       \n",
-      " 26  cost_15pct     float64       \n",
-      " 27  cost_50pct     float64       \n",
-      " 28  cost_85pct     float64       \n",
-      " 29  cost_95pct     float64       \n",
-      " 30  weight_avg     float64       \n",
-      " 31  winner_rate    float64       \n",
-      "dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n",
-      "memory usage: 2.0+ GB\n",
+      " 7   amount         float64       \n",
+      " 8   pct_chg        float64       \n",
+      " 9   turnover_rate  float64       \n",
+      " 10  pe_ttm         float64       \n",
+      " 11  circ_mv        float64       \n",
+      " 12  total_mv       float64       \n",
+      " 13  volume_ratio   float64       \n",
+      " 14  is_st          bool          \n",
+      " 15  up_limit       float64       \n",
+      " 16  down_limit     float64       \n",
+      " 17  buy_sm_vol     float64       \n",
+      " 18  sell_sm_vol    float64       \n",
+      " 19  buy_lg_vol     float64       \n",
+      " 20  sell_lg_vol    float64       \n",
+      " 21  buy_elg_vol    float64       \n",
+      " 22  sell_elg_vol   float64       \n",
+      " 23  net_mf_vol     float64       \n",
+      " 24  his_low        float64       \n",
+      " 25  his_high       float64       \n",
+      " 26  cost_5pct      float64       \n",
+      " 27  cost_15pct     float64       \n",
+      " 28  cost_50pct     float64       \n",
+      " 29  cost_85pct     float64       \n",
+      " 30  cost_95pct     float64       \n",
+      " 31  weight_avg     float64       \n",
+      " 32  winner_rate    float64       \n",
+      "dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
+      "memory usage: 2.1+ GB\n",
       "None\n"
      ]
     }
@@ -121,7 +128,7 @@
     "\n",
     "print('daily data')\n",
     "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n",
-    "                            columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n",
+    "                            columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg'],\n",
     "                            df=None)\n",
     "\n",
     "print('daily basic')\n",
@@ -149,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -217,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -317,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -333,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -400,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -413,7 +420,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n"
+      "['ts_code', 'open', 'close', 'high', 'low', 'amount', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n"
      ]
     }
    ],
@@ -428,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -456,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -476,7 +483,13 @@
       "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",
+      "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "开始计算因子: AR, BR (原地修改)...\n",
       "因子 AR, BR 计算成功。\n",
       "因子 AR, BR 计算流程结束。\n",
@@ -495,7 +508,7 @@
       "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', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\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",
@@ -592,11 +605,11 @@
       "Finished cs_rank_size.\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 4539678 entries, 0 to 4539677\n",
-      "Columns: 180 entries, ts_code to cs_rank_size\n",
-      "dtypes: bool(10), datetime64[ns](1), float64(164), int32(3), object(2)\n",
-      "memory usage: 5.7+ GB\n",
+      "Columns: 181 entries, ts_code to cs_rank_size\n",
+      "dtypes: bool(10), datetime64[ns](1), float64(165), int32(3), object(2)\n",
+      "memory usage: 5.8+ GB\n",
       "None\n",
-      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', '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', '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"
      ]
     }
    ],
@@ -629,6 +642,11 @@
     "# df = cat_reason(df, top_list_df)\n",
     "# df = cat_is_on_top_list(df, top_list_df)\n",
     "\n",
+    "# df = ts_turnover_rate_acceleration_5_20(df)\n",
+    "# df = ts_vol_sustain_10_30(df)\n",
+    "# df = cs_turnover_rate_relative_strength_20(df)\n",
+    "# df = cs_amount_outlier_10(df)\n",
+    "\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",
@@ -697,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "b87b938028afa206",
    "metadata": {
     "ExecuteTime": {
@@ -735,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "f4f16d63ad18d1bc",
    "metadata": {
     "ExecuteTime": {
@@ -745,7 +763,6 @@
    },
    "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",
@@ -962,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "40e6b68a91b30c79",
    "metadata": {
     "ExecuteTime": {
@@ -972,9 +989,6 @@
    },
    "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",
@@ -1071,10 +1085,6 @@
     "    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",
@@ -1289,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "47c12bb34062ae7a",
    "metadata": {
     "ExecuteTime": {
@@ -1323,7 +1333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "29221dde",
    "metadata": {},
    "outputs": [
@@ -1366,7 +1376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "03ee5daf",
    "metadata": {},
    "outputs": [],
@@ -1379,7 +1389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "b76ea08a",
    "metadata": {},
    "outputs": [
@@ -1400,7 +1410,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 131/131 [00:28<00:00,  4.67it/s]\n"
+      "MAD Filtering: 100%|██████████| 131/131 [00:28<00:00,  4.59it/s]\n"
      ]
     },
     {
@@ -1415,7 +1425,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 131/131 [00:24<00:00,  5.43it/s]\n"
+      "MAD Filtering: 100%|██████████| 131/131 [00:23<00:00,  5.52it/s]\n"
      ]
     },
     {
@@ -1597,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1606,8 +1616,6 @@
     "from sklearn.linear_model import LogisticRegression\n",
     "import matplotlib.pyplot as plt # 保持 matplotlib 导入，尽管LightGBM的绘图功能已移除\n",
     "from sklearn.decomposition import PCA\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
     "import datetime # 用于日期计算\n",
     "from catboost import CatBoostClassifier\n",
     "from catboost import Pool\n",
@@ -1740,7 +1748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1774,7 +1782,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b132fdab733b4d4e856d4688f2997dac",
+       "model_id": "5119f1526efe46b58c2c2c978ebdf1f1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1789,8 +1797,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:\tlearn: 0.6887138\ttest: 0.6894516\tbest: 0.6894516 (0)\ttotal: 328ms\tremaining: 8m 12s\n",
-      "bestTest = 0.5217666894\n",
+      "0:\tlearn: 0.6887139\ttest: 0.6894516\tbest: 0.6894516 (0)\ttotal: 271ms\tremaining: 6m 46s\n",
+      "bestTest = 0.5217666495\n",
       "bestIteration = 487\n",
       "Shrink model to first 488 iterations.\n"
      ]
@@ -1814,7 +1822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -1852,7 +1860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -1874,7 +1882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "e53b209a",
    "metadata": {},
    "outputs": [
@@ -1907,12 +1915,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "364e821a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
     "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -1992,7 +1999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "1f6e6336",
    "metadata": {},
    "outputs": [
@@ -2061,7 +2068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "7e9023cc",
    "metadata": {},
    "outputs": [],
@@ -2261,7 +2268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "a0000d75",
    "metadata": {},
    "outputs": [
@@ -2320,8 +2327,6 @@
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "from scipy.stats import spearmanr\n",
@@ -2509,7 +2514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "a436dba4",
    "metadata": {},
    "outputs": [
diff --git a/main/train/Rank2.ipynb b/main/train/Rank2.ipynb
index cd8dbe6..f893ee5 100644
--- a/main/train/Rank2.ipynb
+++ b/main/train/Rank2.ipynb
@@ -450,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -466,7 +466,13 @@
       "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",
+      "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "开始计算因子: AR, BR (原地修改)...\n",
       "因子 AR, BR 计算成功。\n",
       "因子 AR, BR 计算流程结束。\n",
@@ -506,33 +512,86 @@
       "       '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"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[9], line 39\u001b[0m\n\u001b[0;32m     37\u001b[0m df \u001b[38;5;241m=\u001b[39m daily_deviation(df)\n\u001b[0;32m     38\u001b[0m df \u001b[38;5;241m=\u001b[39m daily_industry_deviation(df)\n\u001b[1;32m---> 39\u001b[0m df, _ \u001b[38;5;241m=\u001b[39m get_rolling_factor(df)\n\u001b[0;32m     40\u001b[0m df, _ \u001b[38;5;241m=\u001b[39m get_simple_factor(df)\n\u001b[0;32m     42\u001b[0m df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mrename(columns\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ml1_code\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcat_l1_code\u001b[39m\u001b[38;5;124m'\u001b[39m})\n",
-      "File \u001b[1;32me:\\PyProject\\NewStock\\main\\train\\../..\\main\\factor\\factor.py:340\u001b[0m, in \u001b[0;36mget_rolling_factor\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m    336\u001b[0m \u001b[38;5;66;03m# 计算 act_factor1, act_factor2, act_factor3, act_factor4\u001b[39;00m\n\u001b[0;32m    337\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor1\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_5\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m    338\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m50\u001b[39m\n\u001b[0;32m    339\u001b[0m )\n\u001b[1;32m--> 340\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor2\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_13\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m    341\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m40\u001b[39m\n\u001b[0;32m    342\u001b[0m )\n\u001b[0;32m    343\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor3\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_20\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m    344\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m21\u001b[39m\n\u001b[0;32m    345\u001b[0m )\n\u001b[0;32m    346\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor4\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_60\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m    347\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[0;32m    348\u001b[0m )\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\groupby\\generic.py:230\u001b[0m, in \u001b[0;36mSeriesGroupBy.apply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[38;5;129m@Appender\u001b[39m(\n\u001b[0;32m    225\u001b[0m     _apply_docs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtemplate\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    226\u001b[0m         \u001b[38;5;28minput\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseries\u001b[39m\u001b[38;5;124m\"\u001b[39m, examples\u001b[38;5;241m=\u001b[39m_apply_docs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseries_examples\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    227\u001b[0m     )\n\u001b[0;32m    228\u001b[0m )\n\u001b[0;32m    229\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series:\n\u001b[1;32m--> 230\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mapply(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1824\u001b[0m, in \u001b[0;36mGroupBy.apply\u001b[1;34m(self, func, include_groups, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1822\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m   1823\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1824\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_apply_general(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selected_obj)\n\u001b[0;32m   1825\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   1826\u001b[0m             \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, Series)\n\u001b[0;32m   1827\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1828\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selected_obj\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj_with_exclusions\u001b[38;5;241m.\u001b[39mshape\n\u001b[0;32m   1829\u001b[0m         ):\n\u001b[0;32m   1830\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m   1831\u001b[0m                 message\u001b[38;5;241m=\u001b[39m_apply_groupings_depr\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m   1832\u001b[0m                     \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1835\u001b[0m                 stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m   1836\u001b[0m             )\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1885\u001b[0m, in \u001b[0;36mGroupBy._python_apply_general\u001b[1;34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[0m\n\u001b[0;32m   1850\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m   1851\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_python_apply_general\u001b[39m(\n\u001b[0;32m   1852\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1857\u001b[0m     is_agg: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m   1858\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[0;32m   1859\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1860\u001b[0m \u001b[38;5;124;03m    Apply function f in python space\u001b[39;00m\n\u001b[0;32m   1861\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1883\u001b[0m \u001b[38;5;124;03m        data after applying f\u001b[39;00m\n\u001b[0;32m   1884\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1885\u001b[0m     values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_grouper\u001b[38;5;241m.\u001b[39mapply_groupwise(f, data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis)\n\u001b[0;32m   1886\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1887\u001b[0m         not_indexed_same \u001b[38;5;241m=\u001b[39m mutated\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:919\u001b[0m, in \u001b[0;36mBaseGrouper.apply_groupwise\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m    917\u001b[0m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[0;32m    918\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[1;32m--> 919\u001b[0m res \u001b[38;5;241m=\u001b[39m f(group)\n\u001b[0;32m    920\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[0;32m    921\u001b[0m     mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[1;32me:\\PyProject\\NewStock\\main\\train\\../..\\main\\factor\\factor.py:341\u001b[0m, in \u001b[0;36mget_rolling_factor.<locals>.<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    336\u001b[0m \u001b[38;5;66;03m# 计算 act_factor1, act_factor2, act_factor3, act_factor4\u001b[39;00m\n\u001b[0;32m    337\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor1\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_5\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m    338\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m50\u001b[39m\n\u001b[0;32m    339\u001b[0m )\n\u001b[0;32m    340\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor2\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_13\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m--> 341\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m40\u001b[39m\n\u001b[0;32m    342\u001b[0m )\n\u001b[0;32m    343\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor3\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_20\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m    344\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m21\u001b[39m\n\u001b[0;32m    345\u001b[0m )\n\u001b[0;32m    346\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor4\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_60\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m    347\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[0;32m    348\u001b[0m )\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\ops\\common.py:76\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer.<locals>.new_method\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     72\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[0;32m     74\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[1;32m---> 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m method(\u001b[38;5;28mself\u001b[39m, other)\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\arraylike.py:194\u001b[0m, in \u001b[0;36mOpsMixin.__sub__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    192\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__sub__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    193\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__sub__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[1;32m--> 194\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_arith_method(other, operator\u001b[38;5;241m.\u001b[39msub)\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\series.py:6135\u001b[0m, in \u001b[0;36mSeries._arith_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m   6133\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_arith_method\u001b[39m(\u001b[38;5;28mself\u001b[39m, other, op):\n\u001b[0;32m   6134\u001b[0m     \u001b[38;5;28mself\u001b[39m, other \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_align_for_op(other)\n\u001b[1;32m-> 6135\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m base\u001b[38;5;241m.\u001b[39mIndexOpsMixin\u001b[38;5;241m.\u001b[39m_arith_method(\u001b[38;5;28mself\u001b[39m, other, op)\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\base.py:1382\u001b[0m, in \u001b[0;36mIndexOpsMixin._arith_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m   1379\u001b[0m     rvalues \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(rvalues\u001b[38;5;241m.\u001b[39mstart, rvalues\u001b[38;5;241m.\u001b[39mstop, rvalues\u001b[38;5;241m.\u001b[39mstep)\n\u001b[0;32m   1381\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m-> 1382\u001b[0m     result \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39marithmetic_op(lvalues, rvalues, op)\n\u001b[0;32m   1384\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(result, name\u001b[38;5;241m=\u001b[39mres_name)\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\ops\\array_ops.py:283\u001b[0m, in \u001b[0;36marithmetic_op\u001b[1;34m(left, right, op)\u001b[0m\n\u001b[0;32m    279\u001b[0m     _bool_arith_check(op, left, right)  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[0;32m    281\u001b[0m     \u001b[38;5;66;03m# error: Argument 1 to \"_na_arithmetic_op\" has incompatible type\u001b[39;00m\n\u001b[0;32m    282\u001b[0m     \u001b[38;5;66;03m# \"Union[ExtensionArray, ndarray[Any, Any]]\"; expected \"ndarray[Any, Any]\"\u001b[39;00m\n\u001b[1;32m--> 283\u001b[0m     res_values \u001b[38;5;241m=\u001b[39m _na_arithmetic_op(left, right, op)  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[0;32m    285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res_values\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\ops\\array_ops.py:218\u001b[0m, in \u001b[0;36m_na_arithmetic_op\u001b[1;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[0;32m    215\u001b[0m     func \u001b[38;5;241m=\u001b[39m partial(expressions\u001b[38;5;241m.\u001b[39mevaluate, op)\n\u001b[0;32m    217\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 218\u001b[0m     result \u001b[38;5;241m=\u001b[39m func(left, right)\n\u001b[0;32m    219\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m    220\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_cmp \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[0;32m    221\u001b[0m         left\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(right, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m\n\u001b[0;32m    222\u001b[0m     ):\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    225\u001b[0m         \u001b[38;5;66;03m# Don't do this for comparisons, as that will handle complex numbers\u001b[39;00m\n\u001b[0;32m    226\u001b[0m         \u001b[38;5;66;03m#  incorrectly, see GH#32047\u001b[39;00m\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py:242\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(op, a, b, use_numexpr)\u001b[0m\n\u001b[0;32m    239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m op_str \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    240\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m use_numexpr:\n\u001b[0;32m    241\u001b[0m         \u001b[38;5;66;03m# error: \"None\" not callable\u001b[39;00m\n\u001b[1;32m--> 242\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m _evaluate(op, op_str, a, b)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m    243\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _evaluate_standard(op, op_str, a, b)\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py:131\u001b[0m, in \u001b[0;36m_evaluate_numexpr\u001b[1;34m(op, op_str, a, b)\u001b[0m\n\u001b[0;32m    128\u001b[0m     _store_test_result(result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 131\u001b[0m     result \u001b[38;5;241m=\u001b[39m _evaluate_standard(op, op_str, a, b)\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
-      "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py:73\u001b[0m, in \u001b[0;36m_evaluate_standard\u001b[1;34m(op, op_str, a, b)\u001b[0m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _TEST_MODE:\n\u001b[0;32m     72\u001b[0m     _store_test_result(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op(a, b)\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+      "      dtype='object')\n",
+      "Calculating lg_flow_mom_corr_20_60...\n",
+      "Finished lg_flow_mom_corr_20_60.\n",
+      "Calculating lg_flow_accel...\n",
+      "Finished lg_flow_accel.\n",
+      "Calculating profit_pressure...\n",
+      "Finished profit_pressure.\n",
+      "Calculating underwater_resistance...\n",
+      "Finished underwater_resistance.\n",
+      "Calculating cost_conc_std_20...\n",
+      "Finished cost_conc_std_20.\n",
+      "Calculating profit_decay_20...\n",
+      "Finished profit_decay_20.\n",
+      "Calculating vol_amp_loss_20...\n",
+      "Finished vol_amp_loss_20.\n",
+      "Calculating vol_drop_profit_cnt_5...\n",
+      "Finished vol_drop_profit_cnt_5.\n",
+      "Calculating lg_flow_vol_interact_20...\n",
+      "Finished lg_flow_vol_interact_20.\n",
+      "Calculating cost_break_confirm_cnt_5...\n",
+      "Finished cost_break_confirm_cnt_5.\n",
+      "Calculating atr_norm_channel_pos_14...\n",
+      "Finished atr_norm_channel_pos_14.\n",
+      "Calculating turnover_diff_skew_20...\n",
+      "Finished turnover_diff_skew_20.\n",
+      "Calculating lg_sm_flow_diverge_20...\n",
+      "Finished lg_sm_flow_diverge_20.\n",
+      "Calculating pullback_strong_20_20...\n",
+      "Finished pullback_strong_20_20.\n",
+      "Calculating vol_wgt_hist_pos_20...\n",
+      "Finished vol_wgt_hist_pos_20.\n",
+      "Calculating vol_adj_roc_20...\n",
+      "Finished vol_adj_roc_20.\n",
+      "Calculating cs_rank_net_lg_flow_val...\n",
+      "Finished cs_rank_net_lg_flow_val.\n",
+      "Calculating cs_rank_flow_divergence...\n",
+      "Finished cs_rank_flow_divergence.\n",
+      "Calculating cs_rank_ind_adj_lg_flow...\n",
+      "Finished cs_rank_ind_adj_lg_flow.\n",
+      "Calculating cs_rank_elg_buy_ratio...\n",
+      "Finished cs_rank_elg_buy_ratio.\n",
+      "Calculating cs_rank_rel_profit_margin...\n",
+      "Finished cs_rank_rel_profit_margin.\n",
+      "Calculating cs_rank_cost_breadth...\n",
+      "Finished cs_rank_cost_breadth.\n",
+      "Calculating cs_rank_dist_to_upper_cost...\n",
+      "Finished cs_rank_dist_to_upper_cost.\n",
+      "Calculating cs_rank_winner_rate...\n",
+      "Finished cs_rank_winner_rate.\n",
+      "Calculating cs_rank_intraday_range...\n",
+      "Finished cs_rank_intraday_range.\n",
+      "Calculating cs_rank_close_pos_in_range...\n",
+      "Finished cs_rank_close_pos_in_range.\n",
+      "Calculating cs_rank_opening_gap...\n",
+      "Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n",
+      "Calculating cs_rank_pos_in_hist_range...\n",
+      "Finished cs_rank_pos_in_hist_range.\n",
+      "Calculating cs_rank_vol_x_profit_margin...\n",
+      "Finished cs_rank_vol_x_profit_margin.\n",
+      "Calculating cs_rank_lg_flow_price_concordance...\n",
+      "Finished cs_rank_lg_flow_price_concordance.\n",
+      "Calculating cs_rank_turnover_per_winner...\n",
+      "Finished cs_rank_turnover_per_winner.\n",
+      "Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n",
+      "Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n",
+      "Calculating cs_rank_volume_ratio...\n",
+      "Finished cs_rank_volume_ratio.\n",
+      "Calculating cs_rank_elg_buy_sell_sm_ratio...\n",
+      "Finished cs_rank_elg_buy_sell_sm_ratio.\n",
+      "Calculating cs_rank_cost_dist_vol_ratio...\n",
+      "Finished cs_rank_cost_dist_vol_ratio.\n",
+      "Calculating cs_rank_size...\n",
+      "Finished cs_rank_size.\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4539678 entries, 0 to 4539677\n",
+      "Columns: 178 entries, ts_code to cs_rank_size\n",
+      "dtypes: bool(10), datetime64[ns](1), float64(162), int32(3), object(2)\n",
+      "memory usage: 5.7+ GB\n",
+      "None\n",
+      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
      ]
     }
    ],
@@ -565,6 +624,15 @@
     "# df = cat_reason(df, top_list_df)\n",
     "# df = cat_is_on_top_list(df, top_list_df)\n",
     "\n",
+    "df = ts_turnover_rate_acceleration_5_20(df)\n",
+    "df = ts_vol_sustain_10_30(df)\n",
+    "df = cs_turnover_rate_relative_strength_20(df)\n",
+    "df = cs_amount_outlier_10(df)\n",
+    "df = 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",
@@ -629,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "b87b938028afa206",
    "metadata": {
     "ExecuteTime": {
@@ -667,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "f4f16d63ad18d1bc",
    "metadata": {
     "ExecuteTime": {
@@ -913,7 +981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "40e6b68a91b30c79",
    "metadata": {
     "ExecuteTime": {
@@ -1240,7 +1308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "47c12bb34062ae7a",
    "metadata": {
     "ExecuteTime": {
@@ -1262,7 +1330,7 @@
     "# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n",
     "#                       df.groupby('ts_code')['open'].shift(-1)\n",
     "\n",
-    "df['cat_up_limit'] = df['pct_chg'] > 5\n",
+    "# df['cat_up_limit'] = df['pct_chg'] > 5\n",
     "# df['label'] = (df.groupby('ts_code')['cat_up_limit']\n",
     "#                 .rolling(window=5, min_periods=1).sum()\n",
     "#                 .groupby('ts_code')  # 再次按 ts_code 分组\n",
@@ -1271,7 +1339,7 @@
     "#                 .astype(int)\n",
     "#                 .reset_index(level=0, drop=True))\n",
     "df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
-    "    lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n",
+    "    lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n",
     ")\n",
     "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n",
     "\n",
@@ -1282,103 +1350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "8f4dc587",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          ts_code trade_date   open  close   high    low        vol  pct_chg  \\\n",
-      "976171  002193.SZ 2019-01-02  18.02  17.82  18.21  17.73   22513.62    -1.00   \n",
-      "976172  002193.SZ 2019-01-03  17.82  17.78  17.94  17.65   19523.21    -0.22   \n",
-      "976173  002193.SZ 2019-01-04  17.47  18.04  18.10  17.34   28094.69     1.46   \n",
-      "976174  002193.SZ 2019-01-07  18.17  18.31  18.39  18.00   23866.88     1.50   \n",
-      "976175  002193.SZ 2019-01-08  18.25  18.52  18.66  18.13   22853.46     1.15   \n",
-      "...           ...        ...    ...    ...    ...    ...        ...      ...   \n",
-      "977704  002193.SZ 2025-05-19  11.74  11.95  11.97  11.61   94249.32     2.49   \n",
-      "977705  002193.SZ 2025-05-20  11.99  12.20  12.22  11.84  103040.57     2.09   \n",
-      "977706  002193.SZ 2025-05-21  12.20  11.97  12.28  11.82   83112.00    -1.89   \n",
-      "977707  002193.SZ 2025-05-22  11.95  11.80  12.17  11.72   88811.00    -1.42   \n",
-      "977708  002193.SZ 2025-05-23  11.74  11.51  11.90  11.51   93013.00    -2.46   \n",
-      "\n",
-      "        turnover_rate   pe_ttm  ...  cs_rank_lg_flow_price_concordance  \\\n",
-      "976171         0.9771  23.0129  ...                           0.621118   \n",
-      "976172         0.8473  22.9598  ...                           0.230095   \n",
-      "976173         1.2193  23.3049  ...                           0.507119   \n",
-      "976174         1.0358  23.6499  ...                           0.315078   \n",
-      "976175         0.9918  23.9154  ...                           0.248631   \n",
-      "...               ...      ...  ...                                ...   \n",
-      "977704         3.6012      NaN  ...                           0.224402   \n",
-      "977705         3.9371      NaN  ...                           0.124834   \n",
-      "977706         3.1757      NaN  ...                           0.803720   \n",
-      "977707         3.3934      NaN  ...                           0.667220   \n",
-      "977708         3.5540      NaN  ...                           0.308867   \n",
-      "\n",
-      "        cs_rank_turnover_per_winner  cs_rank_ind_cap_neutral_pe  \\\n",
-      "976171                     0.990869                         NaN   \n",
-      "976172                     0.934283                         NaN   \n",
-      "976173                     0.925182                         NaN   \n",
-      "976174                     0.843796                         NaN   \n",
-      "976175                     0.834672                         NaN   \n",
-      "...                             ...                         ...   \n",
-      "977704                     0.624335                         NaN   \n",
-      "977705                     0.641102                         NaN   \n",
-      "977706                     0.624709                         NaN   \n",
-      "977707                     0.563268                         NaN   \n",
-      "977708                     0.661242                         NaN   \n",
-      "\n",
-      "        cs_rank_volume_ratio  cs_rank_elg_buy_sell_sm_ratio  \\\n",
-      "976171              0.710344                       0.341855   \n",
-      "976172              0.444444                       0.318912   \n",
-      "976173              0.489226                       0.260036   \n",
-      "976174              0.250000                       0.251095   \n",
-      "976175              0.510588                       0.286679   \n",
-      "...                      ...                            ...   \n",
-      "977704              0.234043                       0.397274   \n",
-      "977705              0.344124                       0.116534   \n",
-      "977706              0.221189                       0.126370   \n",
-      "977707              0.412155                       0.130521   \n",
-      "977708              0.534540                       0.134175   \n",
-      "\n",
-      "        cs_rank_cost_dist_vol_ratio  cs_rank_size  future_return  \\\n",
-      "976171                     0.261603      0.262235       0.034231   \n",
-      "976172                     0.185342      0.264695       0.029809   \n",
-      "976173                     0.211959      0.259489       0.025499   \n",
-      "976174                     0.145266      0.255474       0.014746   \n",
-      "976175                     0.202299      0.259854       0.003240   \n",
-      "...                             ...           ...            ...   \n",
-      "977704                     0.299535      0.031250            NaN   \n",
-      "977705                     0.339641      0.032205            NaN   \n",
-      "977706                     0.291597      0.030555            NaN   \n",
-      "977707                     0.372634      0.030555            NaN   \n",
-      "977708                     0.430090      0.027898            NaN   \n",
-      "\n",
-      "        cat_up_limit  label  \n",
-      "976171         False    8.0  \n",
-      "976172         False    7.0  \n",
-      "976173         False   10.0  \n",
-      "976174         False   13.0  \n",
-      "976175         False    6.0  \n",
-      "...              ...    ...  \n",
-      "977704         False    NaN  \n",
-      "977705         False    NaN  \n",
-      "977706         False    NaN  \n",
-      "977707         False    NaN  \n",
-      "977708         False    NaN  \n",
-      "\n",
-      "[1538 rows x 181 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df[df['ts_code'] == '002193.SZ'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "29221dde",
    "metadata": {},
    "outputs": [
@@ -1386,7 +1358,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "192\n"
+      "191\n"
      ]
     }
    ],
@@ -1425,7 +1397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "03ee5daf",
    "metadata": {},
    "outputs": [],
@@ -1438,7 +1410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "b76ea08a",
    "metadata": {},
    "outputs": [
@@ -1450,7 +1422,7 @@
       "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', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', '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"
      ]
@@ -1474,7 +1446,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 132/132 [00:23<00:00,  5.57it/s]\n"
+      "MAD Filtering: 100%|██████████| 132/132 [00:24<00:00,  5.45it/s]\n"
      ]
     },
     {
@@ -1512,7 +1484,7 @@
      "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', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "df最小日期: 2019-01-02\n",
       "df最大日期: 2025-05-23\n",
       "2057539\n",
@@ -1656,57 +1628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "2e4b027e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class RmseObjective(object):\n",
-    "\n",
-    "    def is_max_optimal(self):\n",
-    "        return False\n",
-    "\n",
-    "    def get_final_error(self, error, weight):\n",
-    "        return np.sqrt(error / (weight + 1e-38))\n",
-    "\n",
-    "\n",
-    "    def evaluate(self, approxes, target, weight):\n",
-    "        assert len(approxes) == 1\n",
-    "        assert len(target) == len(approxes[0])\n",
-    "\n",
-    "        approx = approxes[0]\n",
-    "\n",
-    "        error_sum = 0.0\n",
-    "        weight_sum = 0.0\n",
-    "\n",
-    "        for i in range(len(approx)):\n",
-    "            w = 1.0 if weight is None else weight[i]\n",
-    "            weight_sum += w\n",
-    "            error_sum += w * ((approx[i] - target[i])**2)\n",
-    "\n",
-    "        return error_sum, weight_sum\n",
-    "\n",
-    "    def calc_ders_range(self, approxes, targets, weights):\n",
-    "        assert len(approxes) == len(targets)\n",
-    "        if weights is not None:\n",
-    "            assert len(weights) == len(approxes)\n",
-    "\n",
-    "        result = []\n",
-    "        for index in range(len(targets)):\n",
-    "            der1 = targets[index] - approxes[index]\n",
-    "            der2 = -1\n",
-    "\n",
-    "            if weights is not None:\n",
-    "                der1 *= weights[index]\n",
-    "                der2 *= weights[index]\n",
-    "\n",
-    "            result.append((der1, der2))\n",
-    "        return result\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 100,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1817,24 +1739,24 @@
     "        params = {\n",
     "            'label_gain': [gain * gain for gain in label_gain],\n",
     "            'objective': 'lambdarank',\n",
-    "            'metric': 'rank_xendcg',\n",
+    "            'metric': 'lambdarank',\n",
     "            'learning_rate': 0.01,\n",
     "            'num_leaves': 1024,\n",
-    "            # 'min_data_in_leaf': 128,\n",
-    "            'max_depth': 10,\n",
+    "            'min_data_in_leaf': 256,\n",
+    "            # 'max_depth': 64,\n",
     "            'max_bin': 1024,\n",
     "            'feature_fraction': 0.7,\n",
     "            'bagging_fraction': 0.7,\n",
     "            'bagging_freq': 5,\n",
-    "            'lambda_l1': 1,\n",
-    "            'lambda_l2': 1,\n",
+    "            'lambda_l1': 10,\n",
+    "            # 'lambda_l2': 1,\n",
     "            'boosting': 'gbdt',\n",
     "            'verbosity': -1,\n",
     "            'extra_trees': True,\n",
     "            # 'max_position': 5,\n",
-    "            # 'ndcg_at': 1,\n",
+    "            'ndcg_at': 5,\n",
     "            'quant_train_renew_leaf': True,\n",
-    "            # 'lambdarank_truncation_level': 1,\n",
+    "            'lambdarank_truncation_level': 10,\n",
     "            # 'lambdarank_position_bias_regularization': 1,\n",
     "            'seed': 7\n",
     "        }\n",
@@ -1856,44 +1778,44 @@
     "        evals = {}\n",
     "        callbacks = [lgb.log_evaluation(period=1000),\n",
     "                        lgb.callback.record_evaluation(evals),\n",
-    "                        lgb.early_stopping(100, first_metric_only=True)\n",
+    "                        lgb.early_stopping(500, first_metric_only=False)\n",
     "                    ]\n",
-    "        # # 训练模型\n",
-    "        # model = lgb.train(\n",
-    "        #     params, train_dataset, num_boost_round=1000,\n",
-    "        #     valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n",
-    "        #     callbacks=callbacks\n",
-    "        # )\n",
+    "        # 训练模型\n",
+    "        model = lgb.train(\n",
+    "            params, train_dataset, num_boost_round=1000,\n",
+    "            valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n",
+    "            callbacks=callbacks\n",
+    "        )\n",
     "\n",
-    "        # # 打印特征重要性（如果需要）\n",
-    "        # if True:\n",
-    "        #     lgb.plot_metric(evals)\n",
-    "        #     lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
-    "        #     plt.show()\n",
+    "        # 打印特征重要性（如果需要）\n",
+    "        if True:\n",
+    "            lgb.plot_metric(evals)\n",
+    "            lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
+    "            plt.show()\n",
     "\n",
-    "        from flaml import AutoML\n",
-    "        from sklearn.datasets import fetch_california_housing\n",
+    "        # from flaml import AutoML\n",
+    "        # from sklearn.datasets import fetch_california_housing\n",
     "\n",
-    "        # Initialize an AutoML instance\n",
-    "        model = AutoML()\n",
-    "        # Specify automl goal and constraint\n",
-    "        automl_settings = {\n",
-    "            \"time_budget\": 600,  # in seconds\n",
-    "            \"metric\": \"ndcg@1\",\n",
-    "            \"task\": \"rank\",\n",
-    "            \"estimator_list\": [\n",
-    "                \"catboost\",\n",
-    "                \"lgbm\",\n",
-    "                \"xgboost\"\n",
-    "            ], \n",
-    "            \"ensemble\": {\n",
-    "                \"final_estimator\": LGBMRanker(),\n",
-    "                \"passthrough\": False,\n",
-    "            },\n",
-    "        }\n",
-    "        model.fit(X_train=X_train, y_train=y_train, groups=train_groups,\n",
-    "                   X_val=X_val, y_val=y_val,groups_val=val_groups,\n",
-    "                     mlflow_logging=False, **automl_settings)\n",
+    "        # # Initialize an AutoML instance\n",
+    "        # model = AutoML()\n",
+    "        # # Specify automl goal and constraint\n",
+    "        # automl_settings = {\n",
+    "        #     \"time_budget\": 600,  # in seconds\n",
+    "        #     \"metric\": \"ndcg@1\",\n",
+    "        #     \"task\": \"rank\",\n",
+    "        #     \"estimator_list\": [\n",
+    "        #         \"catboost\",\n",
+    "        #         \"lgbm\",\n",
+    "        #         \"xgboost\"\n",
+    "        #     ], \n",
+    "        #     \"ensemble\": {\n",
+    "        #         \"final_estimator\": LGBMRanker(),\n",
+    "        #         \"passthrough\": False,\n",
+    "        #     },\n",
+    "        # }\n",
+    "        # model.fit(X_train=X_train, y_train=y_train, groups=train_groups,\n",
+    "        #            X_val=X_val, y_val=y_val,groups_val=val_groups,\n",
+    "        #              mlflow_logging=False, **automl_settings)\n",
     "\n",
     "\n",
     "    return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象"
@@ -1901,7 +1823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 101,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1914,116 +1836,46 @@
      "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    11.552040\n",
-      "1       603269.SH 2020-01-02    11.324801\n",
-      "2       002633.SZ 2020-01-02    11.759023\n",
-      "3       603991.SH 2020-01-02    11.181150\n",
-      "4       000691.SZ 2020-01-02    11.677910\n",
-      "...           ...        ...          ...\n",
-      "727995  002235.SZ 2022-12-30    12.914708\n",
-      "727996  605598.SH 2022-12-30    11.783580\n",
-      "727997  002613.SZ 2022-12-30    12.489464\n",
-      "727998  600800.SH 2022-12-30    12.571911\n",
-      "727999  603068.SH 2022-12-30    12.967134\n",
+      "train data size:  1091832\n",
+      "           ts_code trade_date  log_circ_mv\n",
+      "0        600306.SH 2020-01-02    11.552040\n",
+      "1        603269.SH 2020-01-02    11.324801\n",
+      "2        002633.SZ 2020-01-02    11.759023\n",
+      "3        603991.SH 2020-01-02    11.181150\n",
+      "4        000691.SZ 2020-01-02    11.677910\n",
+      "...            ...        ...          ...\n",
+      "1091827  603698.SH 2022-12-30    13.370853\n",
+      "1091828  600789.SH 2022-12-30    13.372063\n",
+      "1091829  605366.SH 2022-12-30    12.642936\n",
+      "1091830  603219.SH 2022-12-30    12.089671\n",
+      "1091831  000615.SZ 2022-12-30    13.375555\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",
-      "[flaml.automl.logger: 05-26 20:16:56] {1728} INFO - task = rank\n",
-      "[flaml.automl.logger: 05-26 20:16:56] {1736} INFO - Data split method: group\n",
-      "[flaml.automl.logger: 05-26 20:16:56] {1739} INFO - Evaluation method: holdout\n",
-      "[flaml.automl.logger: 05-26 20:16:56] {1838} INFO - Minimizing error metric: 1-ndcg@1\n",
-      "[flaml.automl.logger: 05-26 20:16:56] {1955} INFO - List of ML learners in AutoML Run: ['catboost', 'lgbm', 'xgboost']\n",
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-      "[flaml.automl.logger: 05-26 20:17:16] {2393} INFO - Estimated sufficient time budget=203147s. Estimated necessary time budget=203s.\n",
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-      "[flaml.automl.logger: 05-26 20:25:09] {2258} INFO - iteration 41, current learner catboost\n",
-      "[flaml.automl.logger: 05-26 20:26:37] {2442} INFO -  at 605.2s,\testimator catboost's best error=0.3789,\tbest estimator catboost's best error=0.3789\n",
-      "[flaml.automl.logger: 05-26 20:26:37] {2550} INFO - selected model: <catboost.core.CatBoostRegressor object at 0x000002736009E2D0>\n",
-      "[flaml.automl.logger: 05-26 20:26:37] {1985} INFO - fit succeeded\n",
-      "[flaml.automl.logger: 05-26 20:26:37] {1986} INFO - Time taken to find the best model: 44.485583543777466\n"
+      "[1091832 rows x 3 columns]\n",
+      "原始样本数: 1091832, 去除标签为空后样本数: 1091832\n",
+      "Training until validation scores don't improve for 500 rounds\n",
+      "Early stopping, best iteration is:\n",
+      "[3]\ttrain's ndcg@5: 0.406681\tvalid's ndcg@5: 0.383736\n"
      ]
+    },
+    {
+     "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": [
@@ -2037,7 +1889,7 @@
     "# print(f'feature_contri: {feature_contri}')\n",
     "model, scaler, pca = train_model(train_data\n",
     "                                 .dropna(subset=['label']).groupby('trade_date', group_keys=False)\n",
-    "                                 .apply(lambda x: x.nsmallest(1000, 'total_mv'))\n",
+    "                                 .apply(lambda x: x.nsmallest(1500, 'total_mv'))\n",
     "                                 .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
     "                                 .merge(index_data, on='trade_date', how='left'), \n",
     "                                 feature_columns, type=type, target_column='label')\n"
@@ -2045,7 +1897,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -2087,7 +1939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 103,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -2095,8 +1947,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "192\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n"
+      "191\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', '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"
      ]
     }
    ],
@@ -2107,7 +1959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 104,
    "id": "bceabd1f",
    "metadata": {},
    "outputs": [
@@ -2118,7 +1970,7 @@
       "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': 0.16666666666666666, 'ndcg@3': 0.12073315639204953, 'ndcg@5': 0.16262598210797247}\n"
+      "{'ndcg@1': 0.9795918367346939, 'ndcg@3': 0.7667326972309916, 'ndcg@5': 0.6789315367339909}\n"
      ]
     }
    ],
diff --git a/main/train/catboost_info/catboost_training.json b/main/train/catboost_info/catboost_training.json
index 6c45bd2..0df5170 100644
--- a/main/train/catboost_info/catboost_training.json
+++ b/main/train/catboost_info/catboost_training.json
@@ -1,792 +1,792 @@
 {
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diff --git a/main/train/catboost_info/learn/events.out.tfevents b/main/train/catboost_info/learn/events.out.tfevents
index ff1c270..4c7fd6c 100644
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diff --git a/main/train/catboost_info/learn_error.tsv b/main/train/catboost_info/learn_error.tsv
index d094c51..c5fbfd7 100644
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diff --git a/main/train/catboost_info/test/events.out.tfevents b/main/train/catboost_info/test/events.out.tfevents
index c28526d..128a556 100644
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diff --git a/main/train/catboost_info/test1/events.out.tfevents b/main/train/catboost_info/test1/events.out.tfevents
deleted file mode 100644
index 2f443b1..0000000
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diff --git a/main/train/catboost_info/test_error.tsv b/main/train/catboost_info/test_error.tsv
index 9055f37..8c0936d 100644
--- a/main/train/catboost_info/test_error.tsv
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diff --git a/main/train/catboost_info/time_left.tsv b/main/train/catboost_info/time_left.tsv
index 6d5801b..1b4a707 100644
--- a/main/train/catboost_info/time_left.tsv
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diff --git a/main/train/catboost_info/tmp/cat_feature_index.6e271dc5-5022da26-a9883518-dbceac3f.tmp b/main/train/catboost_info/tmp/cat_feature_index.6e271dc5-5022da26-a9883518-dbceac3f.tmp
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diff --git a/main/train/catboost_info/tmp/cat_feature_index.7083db21-2a602535-fc74a793-f24f9ca1.tmp b/main/train/catboost_info/tmp/cat_feature_index.7083db21-2a602535-fc74a793-f24f9ca1.tmp
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diff --git a/main/train/catboost_info/tmp/cat_feature_index.c8182d04-ba2f7d40-17be2993-7ebf9fac.tmp b/main/train/catboost_info/tmp/cat_feature_index.c8182d04-ba2f7d40-17be2993-7ebf9fac.tmp
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diff --git a/main/train/catboost_info/tmp/cat_feature_index.d44701fd-1e864ae1-30a8fb3f-c689cc7f.tmp b/main/train/catboost_info/tmp/cat_feature_index.d44701fd-1e864ae1-30a8fb3f-c689cc7f.tmp
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diff --git a/main/train/catboost_info/tmp/cat_feature_index.ea11afac-3656bfa6-7640c1ea-9df76981.tmp b/main/train/catboost_info/tmp/cat_feature_index.ea11afac-3656bfa6-7640c1ea-9df76981.tmp
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diff --git a/main/train/predictions_test.tsv b/main/train/predictions_test.tsv
index f6569f4..7efb568 100644
--- a/main/train/predictions_test.tsv
+++ b/main/train/predictions_test.tsv
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diff --git a/main/utils/__pycache__/utils.cpython-311.pyc b/main/utils/__pycache__/utils.cpython-311.pyc
index 836bdb6..e1b8d19 100644
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