diff --git a/main/factor/__pycache__/factor.cpython-311.pyc b/main/factor/__pycache__/factor.cpython-311.pyc
index cdae86a..8c5d745 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 e308a3c..438c078 100644
--- a/main/factor/factor.py
+++ b/main/factor/factor.py
@@ -3150,6 +3150,7 @@ def ts_volatility_slope_20_5(df: pd.DataFrame) -> pd.DataFrame:
     Returns:
         pd.DataFrame: 包含新增 'ts_volatility_slope_20_5' 列的 DataFrame。
     """
+    print(f"计算因子 ts_volatility_slope_20_5")
     df["trade_date"] = pd.to_datetime(df["trade_date"])
     df.sort_values(["ts_code", "trade_date"], inplace=True)
 
@@ -3186,6 +3187,8 @@ def ts_turnover_rate_acceleration_5_20(df: pd.DataFrame) -> pd.DataFrame:
     Returns:
         pd.DataFrame: 包含新增 'ts_turnover_rate_acceleration_5_20' 列的 DataFrame。
     """
+    print(f"计算因子 ts_turnover_rate_acceleration_5_20")
+
     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)
@@ -3204,6 +3207,8 @@ def ts_vol_sustain_10_30(df: pd.DataFrame) -> pd.DataFrame:
     Returns:
         pd.DataFrame: 包含新增 'ts_vol_sustain_10_30' 列的 DataFrame。
     """
+    print(f"计算因子 ts_vol_sustain_10_30")
+
     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)
@@ -3227,6 +3232,8 @@ def cs_turnover_rate_relative_strength_20(df: pd.DataFrame) -> pd.DataFrame:
     Returns:
         pd.DataFrame: 包含新增 'cs_turnover_rate_relative_strength_20' 列的 DataFrame。
     """
+    print(f"计算因子 cs_turnover_rate_relative_strength_20")
+
     df['trade_date'] = pd.to_datetime(df['trade_date'])
     df.sort_values(['ts_code', 'trade_date'], inplace=True)
 
@@ -3254,6 +3261,8 @@ def cs_amount_outlier_10(df: pd.DataFrame) -> pd.DataFrame:
     Returns:
         pd.DataFrame: 包含新增 'cs_amount_outlier_10' 列的 DataFrame。
     """
+    print(f"计算因子 cs_amount_outlier_10")
+
     df['trade_date'] = pd.to_datetime(df['trade_date'])
     df.sort_values(['ts_code', 'trade_date'], inplace=True)
 
@@ -3281,12 +3290,14 @@ 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。
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date', 'turnover_rate' 和 'turnover_rate' 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
+    print(f"计算因子 ts_ff_to_total_turnover_ratio")
+
+    df['ts_ff_to_total_turnover_ratio'] = df['turnover_rate'] / (df['turnover_rate'] + 1e-8) # 避免除零
     return df
 
 
@@ -3300,6 +3311,8 @@ def ts_price_volume_trend_coherence_5_20(df: pd.DataFrame) -> pd.DataFrame:
     Returns:
         pd.DataFrame: 包含新增 'ts_price_volume_trend_coherence_5_20' 列的 DataFrame。
     """
+    print(f"计算因子 ts_price_volume_trend_coherence_5_20")
+
     df['trade_date'] = pd.to_datetime(df['trade_date'])
     df.sort_values(['ts_code', 'trade_date'], inplace=True)
 
@@ -3323,6 +3336,8 @@ def ts_turnover_rate_trend_strength_5(df: pd.DataFrame) -> pd.DataFrame:
     Returns:
         pd.DataFrame: 包含新增 'ts_turnover_rate_trend_strength_5' 列的 DataFrame。
     """
+    print(f"计算因子 ts_turnover_rate_trend_strength_5")
+
     df['trade_date'] = pd.to_datetime(df['trade_date'])
     df.sort_values(['ts_code', 'trade_date'], inplace=True)
 
@@ -3342,14 +3357,219 @@ 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。
+        df (pd.DataFrame): 包含 'ts_code', 'trade_date' 和 'turnover_rate' 列的 DataFrame。
 
     Returns:
         pd.DataFrame: 包含新增 'ts_ff_turnover_rate_surge_10' 列的 DataFrame。
     """
+    print(f"计算因子 ts_ff_turnover_rate_surge_10")
+
     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['avg_ff_turnover_10'] = df.groupby('ts_code')['turnover_rate'].rolling(window=10, min_periods=1).mean().reset_index(level=0, drop=True)
+    df['ts_ff_turnover_rate_surge_10'] = df['turnover_rate'] / (df['avg_ff_turnover_10'] + 1e-8) # 避免除零
     df.drop(columns=['avg_ff_turnover_10'], inplace=True)
     return df
+
+
+# --- Factor 1: 近期积极动量与成交量激增 (简化版催化剂代理) ---
+def cat_senti_mom_vol_spike(df_input: pd.DataFrame,
+                                    return_period: int = 3,
+                                    return_threshold: float = 0.05,
+                                    volume_ratio_threshold: float = 1.5,
+                                    current_pct_chg_min: float = -0.01,
+                                    current_pct_chg_max: float = 0.03,
+                                    factor_name: str = 'cat_senti_mom_vol_spike') -> pd.DataFrame:
+    """
+    计算近期积极动量与成交量激增因子。
+    理念: 近期有显著正收益 + 近期成交量显著放大 + 当日小幅上涨或横盘。
+    """
+    df = df_input
+    print(f"Calculating {factor_name}...")
+    _temp_cols = []
+
+    try:
+        # 1. 计算N日收益率 (如果不存在)
+        return_col = f'_return_{return_period}d'
+        if return_col not in df.columns:
+            df[return_col] = df.groupby('ts_code')['close'].pct_change(periods=return_period)
+            _temp_cols.append(return_col)
+
+        # 2. 检查 volume_ratio 是否存在 (通常由基础数据提供或 factor.txt 计算)
+        # 如果没有，我们可以尝试计算一个简单的 N 日均量比当日量
+        if 'volume_ratio' not in df.columns:
+            print(f"Warning: 'volume_ratio' column not found. Calculating a proxy for {factor_name}.")
+            df['_avg_vol_5d'] = df.groupby('ts_code')['vol'].rolling(window=5, min_periods=1).mean().reset_index(level=0, drop=True)
+            df['_volume_ratio_proxy'] = df['vol'] / (df['_avg_vol_5d'] + epsilon)
+            volume_metric_col = '_volume_ratio_proxy'
+            _temp_cols.extend(['_avg_vol_5d', '_volume_ratio_proxy'])
+        else:
+            volume_metric_col = 'volume_ratio'
+
+        # 条件判断
+        cond_momentum = df[return_col] > return_threshold
+        cond_volume = df[volume_metric_col] > volume_ratio_threshold
+        cond_current_price = (df['pct_chg'] > current_pct_chg_min) & (df['pct_chg'] < current_pct_chg_max)
+
+        df[factor_name] = (cond_momentum.astype(int).astype(str) + cond_volume.astype(int).astype(str) + cond_current_price.astype(int).astype(str))
+
+    except KeyError as e:
+        print(f"Error calculating {factor_name}: Missing column {e}. Factor column will be all zeros or NaN.")
+        df[factor_name] = 0
+    except Exception as e:
+        print(f"An unexpected error occurred in {factor_name}: {e}. Factor column will be all zeros or NaN.")
+        df[factor_name] = 0
+    finally:
+        # 清理中间列
+        df.drop(columns=[col for col in _temp_cols if col in df.columns], inplace=True, errors='ignore')
+        print(f"Finished {factor_name}.")
+    return df
+
+# --- Factor 2: 强主力资金流入信号(未实现) ---
+def calculate_strong_inflow_signal(df_input: pd.DataFrame,
+                                   intensity_avg_N: int = 3,
+                                   intensity_threshold: float = 0.01, # 假设 flow_lg_elg_intensity 的合理阈值
+                                   consecutive_buy_N: int = 2,
+                                   accel_positive_M: int = 1,
+                                   factor_name: str = 'senti_strong_inflow') -> pd.DataFrame:
+    """
+    计算强主力资金流入信号因子。
+    理念: 大单资金持续、显著净流入，且有加速迹象。
+    依赖: df 中已包含 'flow_lg_elg_intensity' 和 'flow_lg_elg_accel' (来自 factor.txt)
+    """
+    df = df_input
+    print(f"Calculating {factor_name}...")
+    _temp_cols = []
+
+    required_flow_cols = ['flow_lg_elg_intensity', 'flow_lg_elg_accel']
+    if not all(col in df.columns for col in required_flow_cols):
+        missing = [col for col in required_flow_cols if col not in df.columns]
+        print(f"Error: DataFrame 缺少必需的资金流因子列: {missing} for {factor_name}. Factor column will be all zeros or NaN.")
+        df[factor_name] = 0
+        return df
+
+    try:
+        # 1. 近N日主力资金强度均值
+        avg_intensity_col = f'_avg_flow_intensity_{intensity_avg_N}d'
+        df[avg_intensity_col] = df.groupby('ts_code')['flow_lg_elg_intensity'].rolling(window=intensity_avg_N, min_periods=1).mean().reset_index(level=0, drop=True)
+        _temp_cols.append(avg_intensity_col)
+        cond_avg_intensity = df[avg_intensity_col] > intensity_threshold
+
+        # 2. 近N日连续主力净买入天数 (近似：flow_lg_elg_intensity > 0)
+        # 或者使用 lg_elg_net_buy_vol > 0 (如果该列存在)
+        df['_lg_elg_is_net_buy'] = (df['flow_lg_elg_intensity'] > 0).astype(int) # 或者用绝对量判断
+        _temp_cols.append('_lg_elg_is_net_buy')
+        # 计算连续天数
+        def count_consecutive_positive(series):
+            return series.rolling(window=consecutive_buy_N, min_periods=consecutive_buy_N).apply(lambda x: x.sum() == consecutive_buy_N, raw=True)
+
+        df['_consecutive_buy_days_flag'] = df.groupby('ts_code')['_lg_elg_is_net_buy'].apply(count_consecutive_positive).reset_index(level=0, drop=True).fillna(0)
+        _temp_cols.append('_consecutive_buy_days_flag')
+        cond_consecutive_buy = df['_consecutive_buy_days_flag'] == 1
+
+
+        # 3. 近M日主力资金流加速度为正
+        df['_accel_is_positive'] = (df['flow_lg_elg_accel'] > 0).astype(int)
+        _temp_cols.append('_accel_is_positive')
+
+        def check_all_positive_recent_M(series):
+            return series.rolling(window=accel_positive_M, min_periods=accel_positive_M).apply(lambda x: x.sum() == accel_positive_M, raw=True)
+
+        df['_accel_positive_M_flag'] = df.groupby('ts_code')['_accel_is_positive'].apply(check_all_positive_recent_M).reset_index(level=0, drop=True).fillna(0)
+        _temp_cols.append('_accel_positive_M_flag')
+        cond_accel_positive = df['_accel_positive_M_flag'] == 1
+
+        df[factor_name] = (cond_avg_intensity & cond_consecutive_buy & cond_accel_positive).astype(int)
+
+    except KeyError as e:
+        print(f"Error calculating {factor_name}: Missing column {e}. Factor column will be all zeros or NaN.")
+        df[factor_name] = 0
+    except Exception as e:
+        print(f"An unexpected error occurred in {factor_name}: {e}. Factor column will be all zeros or NaN.")
+        df[factor_name] = 0
+    finally:
+        df.drop(columns=[col for col in _temp_cols if col in df.columns], inplace=True, errors='ignore')
+        print(f"Finished {factor_name}.")
+    return df
+
+# --- Factor 3: 突破前盘整模式 ---
+def cat_senti_pre_breakout(df_input: pd.DataFrame,
+                                   atr_short_N: int = 10,
+                                   atr_long_M: int = 40,
+                                   vol_atrophy_N: int = 10, # 用于计算短期均量
+                                   vol_atrophy_M: int = 40, # 用于计算长期均量
+                                   price_stab_N: int = 5,
+                                   price_stab_threshold: float = 0.05,
+                                   current_pct_chg_min_signal: float = 0.005, # 当日上涨至少0.5%
+                                   current_pct_chg_max_signal: float = 0.07,  # 当日上涨不超过7% (避免追已大涨的)
+                                   volume_ratio_signal_threshold: float = 1.2,
+                                   factor_name: str = 'cat_senti_pre_breakout') -> pd.DataFrame:
+    """
+    计算突破前盘整模式因子。
+    理念: 波动率收缩、成交量萎缩、近期价格稳定，当日出现温和放量上涨。
+    """
+    df = df_input
+    print(f"Calculating {factor_name}...")
+    _temp_cols = []
+
+    try:
+        # 1. 波动率收缩 (使用 ATR)
+        atr_short_col = f'atr_{atr_short_N}'
+        atr_long_col = f'atr_{atr_long_M}'
+        for N, col_name in [(atr_short_N, atr_short_col), (atr_long_M, atr_long_col)]:
+            if col_name not in df.columns:
+                print(f"Calculating {col_name} as it's missing...")
+                # TA-Lib需要numpy array作为输入，并且不能有NaN在中间 (首行NaN可以)
+                # 分组计算ATR比较麻烦，这里假设如果df不是很大，可以先整列计算，再groupby获取
+                # 一个更稳健的方法是groupby().apply(lambda x: talib.ATR(x['high'], x['low'], x['close'], N))
+                # 但为了避免 apply 的性能问题，这里用一种近似，如果数据量大，最好预计算
+                temp_atr = df.groupby('ts_code', group_keys=False).apply(
+                    lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=N), index=x.index)
+                )
+                df[col_name] = temp_atr
+                _temp_cols.append(col_name)
+
+        cond_vol_contraction = df[atr_short_col] < (0.7 * df[atr_long_col]) # 短期ATR显著小于长期ATR
+
+        # 2. 成交量萎缩
+        avg_vol_short_col = f'_avg_vol_{vol_atrophy_N}'
+        avg_vol_long_col = f'_avg_vol_{vol_atrophy_M}'
+        df[avg_vol_short_col] = df.groupby('ts_code')['vol'].rolling(window=vol_atrophy_N, min_periods=1).mean().reset_index(level=0,drop=True)
+        df[avg_vol_long_col] = df.groupby('ts_code')['vol'].rolling(window=vol_atrophy_M, min_periods=1).mean().reset_index(level=0,drop=True)
+        _temp_cols.extend([avg_vol_short_col, avg_vol_long_col])
+        cond_vol_atrophy = df[avg_vol_short_col] < (0.7 * df[avg_vol_long_col]) # 短期均量显著小于长期均量
+
+        # 3. 近期价格稳定
+        rolling_max_h_col = f'_rolling_max_h_{price_stab_N}'
+        rolling_min_l_col = f'_rolling_min_l_{price_stab_N}'
+        df[rolling_max_h_col] = df.groupby('ts_code')['high'].rolling(window=price_stab_N, min_periods=1).max().reset_index(level=0,drop=True)
+        df[rolling_min_l_col] = df.groupby('ts_code')['low'].rolling(window=price_stab_N, min_periods=1).min().reset_index(level=0,drop=True)
+        _temp_cols.extend([rolling_max_h_col, rolling_min_l_col])
+        cond_price_stability = ( (df[rolling_max_h_col] - df[rolling_min_l_col]) / (df['close'] + epsilon) ) < price_stab_threshold
+
+        # 4. 当日温和放量上涨信号
+        if 'volume_ratio' not in df.columns:
+            print(f"Warning: 'volume_ratio' column not found for {factor_name}. Using a proxy.")
+            # 如果没有量比，就用当日成交量 > 1.2 * 近5日均量作为代理
+            if avg_vol_short_col not in df.columns: # 确保这个短期均量列已计算
+                 df[avg_vol_short_col] = df.groupby('ts_code')['vol'].rolling(window=vol_atrophy_N, min_periods=1).mean().reset_index(level=0,drop=True)
+            cond_vol_signal = df['vol'] > (1.2 * df[avg_vol_short_col])
+        else:
+            cond_vol_signal = df['volume_ratio'] > volume_ratio_signal_threshold
+
+        cond_price_signal = (df['pct_chg'] > current_pct_chg_min_signal) & (df['pct_chg'] < current_pct_chg_max_signal)
+        cond_current_day_signal = cond_price_signal & cond_vol_signal
+
+        df[factor_name] = (cond_vol_contraction.astype(int).astype(str) + cond_vol_atrophy.astype(int).astype(str) + cond_price_stability.astype(int).astype(str) + cond_current_day_signal.astype(int).astype(str))
+
+    except KeyError as e:
+        print(f"Error calculating {factor_name}: Missing column {e}. Factor column will be all zeros or NaN.")
+        df[factor_name] = 0
+    except Exception as e:
+        print(f"An unexpected error occurred in {factor_name}: {e}. Factor column will be all zeros or NaN.")
+        df[factor_name] = 0
+    finally:
+        df.drop(columns=[col for col in _temp_cols if col in df.columns], inplace=True, errors='ignore')
+        print(f"Finished {factor_name}.")
+    return df
+
diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb
index aa6e54b..366afac 100644
--- a/main/train/Classify2.ipynb
+++ b/main/train/Classify2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "id": "79a7758178bafdd3",
    "metadata": {
     "ExecuteTime": {
@@ -18,6 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n",
       "e:\\PyProject\\NewStock\\main\\train\n"
      ]
     }
@@ -44,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "id": "4a481c60",
    "metadata": {},
    "outputs": [],
@@ -56,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -156,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -224,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -324,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -340,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -407,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -435,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -463,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -476,6 +478,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "计算因子 ts_turnover_rate_acceleration_5_20\n",
+      "计算因子 ts_vol_sustain_10_30\n",
+      "计算因子 cs_amount_outlier_10\n",
+      "计算因子 ts_ff_to_total_turnover_ratio\n",
+      "计算因子 ts_price_volume_trend_coherence_5_20\n",
+      "计算因子 ts_ff_turnover_rate_surge_10\n",
       "使用 'ann_date' 作为财务数据生效日期。\n",
       "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
       "使用 'ann_date' 作为财务数据生效日期。\n",
@@ -483,13 +491,7 @@
       "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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
       "开始计算因子: AR, BR (原地修改)...\n",
       "因子 AR, BR 计算成功。\n",
       "因子 AR, BR 计算流程结束。\n",
@@ -513,20 +515,23 @@
       "       'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n",
       "       'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n",
       "       'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n",
-      "       'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe',\n",
-      "       'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n",
-      "       'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20',\n",
-      "       'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol',\n",
-      "       'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n",
-      "       'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n",
-      "       'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n",
-      "       'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n",
-      "       'floating_chip_proxy', 'cost_support_15pct_change',\n",
-      "       'cat_winner_price_zone', 'flow_chip_consistency',\n",
-      "       'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n",
-      "       'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n",
-      "       '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n",
-      "       'return_skew', 'return_kurtosis', 'volume_change_rate',\n",
+      "       'winner_rate', 'l2_code', 'ts_turnover_rate_acceleration_5_20',\n",
+      "       'ts_vol_sustain_10_30', 'cs_turnover_rate_relative_strength_20',\n",
+      "       'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio',\n",
+      "       'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10',\n",
+      "       'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR',\n",
+      "       'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n",
+      "       'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n",
+      "       'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n",
+      "       'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n",
+      "       'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n",
+      "       'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n",
+      "       'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n",
+      "       'cost_support_15pct_change', 'cat_winner_price_zone',\n",
+      "       'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n",
+      "       '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n",
+      "       '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n",
+      "       'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n",
       "       'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n",
       "       'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n",
       "       'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n",
@@ -605,11 +610,11 @@
       "Finished cs_rank_size.\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 4539678 entries, 0 to 4539677\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",
+      "Columns: 188 entries, ts_code to cs_rank_size\n",
+      "dtypes: bool(10), datetime64[ns](1), float64(172), int32(3), object(2)\n",
+      "memory usage: 6.0+ GB\n",
       "None\n",
-      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
+      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_turnover_rate_relative_strength_20', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', '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"
      ]
     }
    ],
@@ -642,10 +647,14 @@
     "# 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 = 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 = 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",
@@ -715,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "id": "b87b938028afa206",
    "metadata": {
     "ExecuteTime": {
@@ -753,7 +762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 23,
    "id": "f4f16d63ad18d1bc",
    "metadata": {
     "ExecuteTime": {
@@ -979,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 24,
    "id": "40e6b68a91b30c79",
    "metadata": {
     "ExecuteTime": {
@@ -1299,7 +1308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 25,
    "id": "47c12bb34062ae7a",
    "metadata": {
     "ExecuteTime": {
@@ -1333,7 +1342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
    "id": "29221dde",
    "metadata": {},
    "outputs": [
@@ -1341,7 +1350,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "191\n"
+      "198\n"
      ]
     }
    ],
@@ -1376,7 +1385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 27,
    "id": "03ee5daf",
    "metadata": {},
    "outputs": [],
@@ -1389,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 28,
    "id": "b76ea08a",
    "metadata": {},
    "outputs": [
@@ -1401,7 +1410,7 @@
       "0  000001.SZ 2019-01-02    16.574219\n",
       "1  000001.SZ 2019-01-03    16.583965\n",
       "2  000001.SZ 2019-01-04    16.633371\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_turnover_rate_relative_strength_20', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "去除极值\n",
       "开始截面 MAD 去极值处理 (k=3.0)...\n"
      ]
@@ -1410,7 +1419,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 131/131 [00:28<00:00,  4.59it/s]\n"
+      "MAD Filtering: 100%|██████████| 138/138 [00:29<00:00,  4.63it/s]\n"
      ]
     },
     {
@@ -1425,7 +1434,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 131/131 [00:23<00:00,  5.52it/s]\n"
+      "MAD Filtering: 100%|██████████| 138/138 [00:24<00:00,  5.56it/s]\n"
      ]
     },
     {
@@ -1463,7 +1472,7 @@
      "output_type": "stream",
      "text": [
       "截面 MAD 去极值处理完成。\n",
-      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_turnover_rate_relative_strength_20', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "df最小日期: 2019-01-02\n",
       "df最大日期: 2025-05-23\n",
       "2057539\n",
@@ -1607,7 +1616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 29,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1748,7 +1757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 30,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1782,7 +1791,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5119f1526efe46b58c2c2c978ebdf1f1",
+       "model_id": "dd188e8edba34874b09e5d22011345b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1797,10 +1806,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "0:\tlearn: 0.6886838\ttest: 0.6900971\tbest: 0.6900971 (0)\ttotal: 305ms\tremaining: 7m 37s\n",
+      "bestTest = 0.5187919143\n",
+      "bestIteration = 718\n",
+      "Shrink model to first 719 iterations.\n"
      ]
     }
    ],
@@ -1822,7 +1831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 31,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -1860,7 +1869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 32,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -1868,8 +1877,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "191\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "198\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_turnover_rate_relative_strength_20', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
       "[]\n"
      ]
     }
@@ -1882,7 +1891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 33,
    "id": "e53b209a",
    "metadata": {},
    "outputs": [
@@ -1915,7 +1924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 34,
    "id": "364e821a",
    "metadata": {},
    "outputs": [],
@@ -1999,7 +2008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 35,
    "id": "1f6e6336",
    "metadata": {},
    "outputs": [
@@ -2018,21 +2027,21 @@
       "2e+05-3e+05\n",
       "3e+05-3e+05\n",
       "二分类评估指标:\n",
-      "accuracy: 0.6585\n",
-      "precision: 0.4597\n",
-      "recall: 0.1487\n",
-      "f1: 0.2247\n",
-      "roc_auc: 0.6122\n",
-      "fpr: (array of length 7368)\n",
-      "tpr: (array of length 7368)\n",
-      "thresholds: (array of length 7368)\n",
-      "score_return_correlation: -0.0355\n",
-      "mv_roc_auc: {'6e+04-9e+04': 0.5986394557823129, '9e+04-1e+05': 0.5806043519120943, '1e+05-1e+05': 0.5902634156155784, '1e+05-2e+05': 0.5821412014040664, '2e+05-2e+05': 0.5919906015824924, '2e+05-3e+05': 0.6058984399066272, '3e+05-3e+05': 0.5884844322344323}\n"
+      "accuracy: 0.6581\n",
+      "precision: 0.4363\n",
+      "recall: 0.1818\n",
+      "f1: 0.2567\n",
+      "roc_auc: 0.6114\n",
+      "fpr: (array of length 7274)\n",
+      "tpr: (array of length 7274)\n",
+      "thresholds: (array of length 7274)\n",
+      "score_return_correlation: -0.0389\n",
+      "mv_roc_auc: {'6e+04-9e+04': 0.5614814814814815, '9e+04-1e+05': 0.536344981967417, '1e+05-1e+05': 0.5884778932556533, '1e+05-2e+05': 0.576412066700775, '2e+05-2e+05': 0.5965007002245437, '2e+05-3e+05': 0.5964582475089183, '3e+05-3e+05': 0.5963487825089773}\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -2068,7 +2077,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 36,
    "id": "7e9023cc",
    "metadata": {},
    "outputs": [],
@@ -2268,7 +2277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 37,
    "id": "a0000d75",
    "metadata": {},
    "outputs": [
@@ -2278,7 +2287,7 @@
      "text": [
       "开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n",
       "准备数据，处理 NaN 值...\n",
-      "原始数据 17280 行，移除 NaN 后剩余 16976 行用于分析。\n",
+      "原始数据 17280 行，移除 NaN 后剩余 16968 行用于分析。\n",
       "对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n",
       "按二维分箱分组计算 Spearman Rank IC...\n",
       "整理结果用于绘图...\n",
@@ -2514,7 +2523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 38,
    "id": "a436dba4",
    "metadata": {},
    "outputs": [
diff --git a/main/train/Rank2.ipynb b/main/train/Rank2.ipynb
index f893ee5..339f582 100644
--- a/main/train/Rank2.ipynb
+++ b/main/train/Rank2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "id": "79a7758178bafdd3",
    "metadata": {
     "ExecuteTime": {
@@ -18,6 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n",
       "e:\\PyProject\\NewStock\\main\\train\n"
      ]
     }
@@ -44,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -69,7 +71,7 @@
       "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",
@@ -80,32 +82,33 @@
       " 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",
+      " 8   amount         float64       \n",
+      " 9   turnover_rate  float64       \n",
+      " 10  pe_ttm         float64       \n",
+      " 11  circ_mv        float64       \n",
+      " 12  total_mv       float64       \n",
+      " 13  volume_ratio   float64       \n",
+      " 14  is_st          bool          \n",
+      " 15  up_limit       float64       \n",
+      " 16  down_limit     float64       \n",
+      " 17  buy_sm_vol     float64       \n",
+      " 18  sell_sm_vol    float64       \n",
+      " 19  buy_lg_vol     float64       \n",
+      " 20  sell_lg_vol    float64       \n",
+      " 21  buy_elg_vol    float64       \n",
+      " 22  sell_elg_vol   float64       \n",
+      " 23  net_mf_vol     float64       \n",
+      " 24  his_low        float64       \n",
+      " 25  his_high       float64       \n",
+      " 26  cost_5pct      float64       \n",
+      " 27  cost_15pct     float64       \n",
+      " 28  cost_50pct     float64       \n",
+      " 29  cost_85pct     float64       \n",
+      " 30  cost_95pct     float64       \n",
+      " 31  weight_avg     float64       \n",
+      " 32  winner_rate    float64       \n",
+      "dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
+      "memory usage: 2.1+ GB\n",
       "None\n"
      ]
     }
@@ -115,7 +118,7 @@
     "\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', 'pct_chg', 'amount'],\n",
     "                            df=None)\n",
     "\n",
     "print('daily basic')\n",
@@ -143,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -211,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -311,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -327,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -394,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -407,7 +410,7 @@
      "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"
      ]
     }
    ],
@@ -422,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -450,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -463,16 +466,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Calculating cat_senti_mom_vol_spike...\n",
+      "Finished cat_senti_mom_vol_spike.\n",
+      "Calculating cat_senti_pre_breakout...\n",
+      "Calculating atr_10 as it's missing...\n",
+      "Calculating atr_40 as it's missing...\n",
+      "Finished cat_senti_pre_breakout.\n",
+      "计算因子 ts_turnover_rate_acceleration_5_20\n",
+      "计算因子 ts_vol_sustain_10_30\n",
+      "计算因子 cs_amount_outlier_10\n",
+      "计算因子 ts_ff_to_total_turnover_ratio\n",
+      "计算因子 ts_price_volume_trend_coherence_5_20\n",
+      "计算因子 ts_ff_turnover_rate_surge_10\n",
       "使用 'ann_date' 作为财务数据生效日期。\n",
       "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
       "使用 'ann_date' 作为财务数据生效日期。\n",
-      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
       "开始计算因子: AR, BR (原地修改)...\n",
       "因子 AR, BR 计算成功。\n",
       "因子 AR, BR 计算流程结束。\n",
@@ -491,12 +500,16 @@
       "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",
+      "       'pct_chg', 'amount', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n",
       "       'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n",
       "       'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n",
       "       'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n",
       "       'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n",
-      "       'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n",
+      "       'winner_rate', 'l2_code', 'cat_senti_mom_vol_spike',\n",
+      "       'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20',\n",
+      "       'ts_vol_sustain_10_30', 'cs_amount_outlier_10',\n",
+      "       'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20',\n",
+      "       'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n",
       "       'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n",
       "       'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n",
       "       'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n",
@@ -587,36 +600,37 @@
       "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",
+      "Columns: 187 entries, ts_code to cs_rank_size\n",
+      "dtypes: bool(10), datetime64[ns](1), float64(169), int32(3), object(4)\n",
+      "memory usage: 6.0+ 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"
+      "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'amount', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
      ]
     }
    ],
    "source": [
-    "\n",
     "import numpy as np\n",
     "from main.factor.factor import *\n",
     "\n",
+    "\n",
     "def filter_data(df):\n",
     "    # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
-    "    df = df[~df['is_st']]\n",
-    "    df = df[~df['ts_code'].str.endswith('BJ')]\n",
-    "    df = df[~df['ts_code'].str.startswith('30')]\n",
-    "    df = df[~df['ts_code'].str.startswith('68')]\n",
-    "    df = df[~df['ts_code'].str.startswith('8')]\n",
-    "    df = df[df['trade_date'] >= '2019-01-01']\n",
-    "    if 'in_date' in df.columns:\n",
-    "        df = df.drop(columns=['in_date'])\n",
+    "    df = df[~df[\"is_st\"]]\n",
+    "    df = df[~df[\"ts_code\"].str.endswith(\"BJ\")]\n",
+    "    df = df[~df[\"ts_code\"].str.startswith(\"30\")]\n",
+    "    df = df[~df[\"ts_code\"].str.startswith(\"68\")]\n",
+    "    df = df[~df[\"ts_code\"].str.startswith(\"8\")]\n",
+    "    df = df[df[\"trade_date\"] >= \"2019-01-01\"]\n",
+    "    if \"in_date\" in df.columns:\n",
+    "        df = df.drop(columns=[\"in_date\"])\n",
     "    df = df.reset_index(drop=True)\n",
     "    return df\n",
     "\n",
+    "\n",
     "gc.collect()\n",
     "\n",
     "df = filter_data(df)\n",
-    "df = df.sort_values(by=['ts_code', 'trade_date'])\n",
+    "df = df.sort_values(by=[\"ts_code\", \"trade_date\"])\n",
     "\n",
     "# df = price_minus_deduction_price(df, n=120)\n",
     "# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n",
@@ -624,19 +638,41 @@
     "# df = cat_reason(df, top_list_df)\n",
     "# df = cat_is_on_top_list(df, top_list_df)\n",
     "\n",
+    "df = cat_senti_mom_vol_spike(\n",
+    "    df,\n",
+    "    return_period=3,\n",
+    "    return_threshold=0.03,  # 近3日涨幅超3%\n",
+    "    volume_ratio_threshold=1.3,\n",
+    "    current_pct_chg_min=0.0,  # 当日必须收红\n",
+    "    current_pct_chg_max=0.04,\n",
+    ")  # 当日涨幅不宜过大\n",
+    "\n",
+    "df = cat_senti_pre_breakout(\n",
+    "    df,\n",
+    "    atr_short_N=10,\n",
+    "    atr_long_M=40,\n",
+    "    vol_atrophy_N=10,\n",
+    "    vol_atrophy_M=40,\n",
+    "    price_stab_N=5,\n",
+    "    price_stab_threshold=0.06,\n",
+    "    current_pct_chg_min_signal=0.002,\n",
+    "    current_pct_chg_max_signal=0.05,\n",
+    "    volume_ratio_signal_threshold=1.1,\n",
+    ")\n",
+    "\n",
     "df = ts_turnover_rate_acceleration_5_20(df)\n",
     "df = ts_vol_sustain_10_30(df)\n",
-    "df = cs_turnover_rate_relative_strength_20(df)\n",
+    "# df = cs_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_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",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col=\"undist_profit_ps\")\n",
+    "df = add_financial_factor(df, fina_indicator_df, factor_value_col=\"ocfps\")\n",
     "calculate_arbr(df, N=26)\n",
-    "df['log_circ_mv'] = np.log(df['circ_mv'])\n",
+    "df[\"log_circ_mv\"] = np.log(df[\"circ_mv\"])\n",
     "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n",
     "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n",
     "df = turnover_rate_n(df, n=5)\n",
@@ -647,8 +683,8 @@
     "df, _ = get_rolling_factor(df)\n",
     "df, _ = get_simple_factor(df)\n",
     "\n",
-    "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n",
-    "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n",
+    "df = df.rename(columns={\"l1_code\": \"cat_l1_code\"})\n",
+    "df = df.rename(columns={\"l2_code\": \"cat_l2_code\"})\n",
     "\n",
     "lg_flow_mom_corr(df, N=20, M=60)\n",
     "lg_flow_accel(df)\n",
@@ -669,7 +705,7 @@
     "\n",
     "cs_rank_net_lg_flow_val(df)\n",
     "cs_rank_flow_divergence(df)\n",
-    "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n",
+    "cs_rank_industry_adj_lg_flow(df)  # Needs cat_l2_code\n",
     "cs_rank_elg_buy_ratio(df)\n",
     "cs_rank_rel_profit_margin(df)\n",
     "cs_rank_cost_breadth(df)\n",
@@ -677,16 +713,16 @@
     "cs_rank_winner_rate(df)\n",
     "cs_rank_intraday_range(df)\n",
     "cs_rank_close_pos_in_range(df)\n",
-    "cs_rank_opening_gap(df) # Needs pre_close\n",
-    "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n",
+    "cs_rank_opening_gap(df)  # Needs pre_close\n",
+    "cs_rank_pos_in_hist_range(df)  # Needs his_low, his_high\n",
     "cs_rank_vol_x_profit_margin(df)\n",
     "cs_rank_lg_flow_price_concordance(df)\n",
     "cs_rank_turnover_per_winner(df)\n",
-    "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n",
-    "cs_rank_volume_ratio(df) # Needs volume_ratio\n",
+    "cs_rank_ind_cap_neutral_pe(df)  # Placeholder - needs external libraries\n",
+    "cs_rank_volume_ratio(df)  # Needs volume_ratio\n",
     "cs_rank_elg_buy_sell_sm_ratio(df)\n",
-    "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n",
-    "cs_rank_size(df) # Needs circ_mv\n",
+    "cs_rank_cost_dist_vol_ratio(df)  # Needs volume_ratio\n",
+    "cs_rank_size(df)  # Needs circ_mv\n",
     "\n",
     "\n",
     "# df = df.merge(index_data, on='trade_date', how='left')\n",
@@ -697,7 +733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "id": "b87b938028afa206",
    "metadata": {
     "ExecuteTime": {
@@ -735,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "id": "f4f16d63ad18d1bc",
    "metadata": {
     "ExecuteTime": {
@@ -981,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "id": "40e6b68a91b30c79",
    "metadata": {
     "ExecuteTime": {
@@ -1308,7 +1344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "id": "47c12bb34062ae7a",
    "metadata": {
     "ExecuteTime": {
@@ -1341,7 +1377,8 @@
     "df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
     "    lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n",
     ")\n",
-    "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n",
+    "# filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n",
+    "filter_index = df['future_return'].between(df['future_return'].quantile(0.001), 0.6)\n",
     "\n",
     "# for col in [col for col in df.columns]:\n",
     "#     train_data[col] = train_data[col].astype('str')\n",
@@ -1350,7 +1387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "id": "29221dde",
    "metadata": {},
    "outputs": [
@@ -1358,7 +1395,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "191\n"
+      "199\n"
      ]
     }
    ],
@@ -1397,7 +1434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 25,
    "id": "03ee5daf",
    "metadata": {},
    "outputs": [],
@@ -1410,7 +1447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 26,
    "id": "b76ea08a",
    "metadata": {},
    "outputs": [
@@ -1421,8 +1458,14 @@
       "     ts_code trade_date  log_circ_mv\n",
       "0  000001.SZ 2019-01-02    16.574219\n",
       "1  000001.SZ 2019-01-03    16.583965\n",
-      "2  000001.SZ 2019-01-04    16.633371\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', '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",
+      "2  000001.SZ 2019-01-04    16.633371\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', '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"
      ]
@@ -1431,7 +1474,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 132/132 [00:28<00:00,  4.64it/s]\n"
+      "MAD Filtering: 100%|██████████| 138/138 [00:30<00:00,  4.50it/s]\n"
      ]
     },
     {
@@ -1446,7 +1489,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 132/132 [00:24<00:00,  5.45it/s]\n"
+      "MAD Filtering: 100%|██████████| 138/138 [00:25<00:00,  5.46it/s]\n"
      ]
     },
     {
@@ -1484,10 +1527,10 @@
      "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', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
+      "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', '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",
+      "2097932\n",
       "train_data最小日期: 2020-01-02\n",
       "train_data最大日期: 2022-12-30\n",
       "1766694\n",
@@ -1628,7 +1671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 37,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1700,7 +1743,7 @@
     "            'loss_function': 'QueryRMSE',  # 适用于二分类\n",
     "            'eval_metric': 'NDCG',  # 评估指标\n",
     "            'iterations': 1500,\n",
-    "            'learning_rate': 0.01,\n",
+    "            'learning_rate': 0.03,\n",
     "            'depth': 10,  # 控制模型复杂度\n",
     "            # 'l2_leaf_reg': 0.1,  # L2 正则化\n",
     "            'verbose': 5000,\n",
@@ -1740,24 +1783,24 @@
     "            'label_gain': [gain * gain for gain in label_gain],\n",
     "            'objective': 'lambdarank',\n",
     "            'metric': 'lambdarank',\n",
-    "            'learning_rate': 0.01,\n",
-    "            'num_leaves': 1024,\n",
-    "            'min_data_in_leaf': 256,\n",
-    "            # 'max_depth': 64,\n",
-    "            'max_bin': 1024,\n",
+    "            'learning_rate': 0.05,\n",
+    "            # 'num_leaves': 1024,\n",
+    "            # 'min_data_in_leaf': 256,\n",
+    "            'max_depth': 10,\n",
+    "            # 'max_bin': 1024,\n",
     "            'feature_fraction': 0.7,\n",
     "            'bagging_fraction': 0.7,\n",
     "            'bagging_freq': 5,\n",
-    "            'lambda_l1': 10,\n",
-    "            # 'lambda_l2': 1,\n",
+    "            'lambda_l1': 5,\n",
+    "            'lambda_l2': 5,\n",
     "            'boosting': 'gbdt',\n",
     "            'verbosity': -1,\n",
     "            'extra_trees': True,\n",
     "            # 'max_position': 5,\n",
-    "            'ndcg_at': 5,\n",
+    "            # 'ndcg_at': 50,\n",
     "            'quant_train_renew_leaf': True,\n",
-    "            'lambdarank_truncation_level': 10,\n",
-    "            # 'lambdarank_position_bias_regularization': 1,\n",
+    "            # 'lambdarank_truncation_level': 100,\n",
+    "            'lambdarank_position_bias_regularization': 1,\n",
     "            'seed': 7\n",
     "        }\n",
     "        train_groups = train_data_split.groupby('trade_date').size().tolist()\n",
@@ -1778,11 +1821,11 @@
     "        evals = {}\n",
     "        callbacks = [lgb.log_evaluation(period=1000),\n",
     "                        lgb.callback.record_evaluation(evals),\n",
-    "                        lgb.early_stopping(500, first_metric_only=False)\n",
+    "                        lgb.early_stopping(300, first_metric_only=False)\n",
     "                    ]\n",
     "        # 训练模型\n",
     "        model = lgb.train(\n",
-    "            params, train_dataset, num_boost_round=1000,\n",
+    "            params, train_dataset, num_boost_round=3000,\n",
     "            valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n",
     "            callbacks=callbacks\n",
     "        )\n",
@@ -1823,7 +1866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 38,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1836,30 +1879,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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",
+      "train data size:  364000\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",
+      "363995  603351.SH 2022-12-30    12.566524\n",
+      "363996  002363.SZ 2022-12-30    12.588302\n",
+      "363997  605218.SH 2022-12-30    11.710093\n",
+      "363998  603519.SH 2022-12-30    12.592329\n",
+      "363999  600293.SH 2022-12-30    12.593635\n",
       "\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",
+      "[364000 rows x 3 columns]\n",
+      "原始样本数: 364000, 去除标签为空后样本数: 364000\n",
+      "Training until validation scores don't improve for 300 rounds\n",
       "Early stopping, best iteration is:\n",
-      "[3]\ttrain's ndcg@5: 0.406681\tvalid's ndcg@5: 0.383736\n"
+      "[368]\ttrain's ndcg@1: 0.694512\ttrain's ndcg@2: 0.65148\ttrain's ndcg@3: 0.62293\ttrain's ndcg@4: 0.606404\ttrain's ndcg@5: 0.592129\tvalid's ndcg@1: 0.456055\tvalid's ndcg@2: 0.458545\tvalid's ndcg@3: 0.457883\tvalid's ndcg@4: 0.445194\tvalid's ndcg@5: 0.439297\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1869,7 +1912,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1889,7 +1932,7 @@
     "# 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(1500, 'total_mv'))\n",
+    "                                 .apply(lambda x: x.nsmallest(500, '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"
@@ -1897,7 +1940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 39,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -1931,7 +1974,7 @@
     ").reset_index(drop=True) # drop=True 避免添加旧索引列\n",
     "# df_to_drop = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n",
     "# score_df = score_df.drop(df_to_drop.index)\n",
-    "save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(2, 'score')).reset_index()\n",
+    "save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1, 'score')).reset_index()\n",
     "# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index()\n",
     "save_df = save_df.sort_values(['trade_date', 'score'])\n",
     "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n"
@@ -1939,7 +1982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 40,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -1947,8 +1990,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "191\n",
-      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', '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"
+      "199\n",
+      "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', '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"
      ]
     }
    ],
@@ -1959,7 +2002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 41,
    "id": "bceabd1f",
    "metadata": {},
    "outputs": [
@@ -1970,7 +2013,7 @@
       "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': 0.9795918367346939, 'ndcg@3': 0.7667326972309916, 'ndcg@5': 0.6789315367339909}\n"
+      "{'ndcg@1': 1.0, 'ndcg@3': 1.0, 'ndcg@5': 1.0}\n"
      ]
     }
    ],
@@ -2030,6 +2073,102 @@
     "print(\"\\nNDCG 结果\")\n",
     "print(ndcg_results_single_group)\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "44f64679",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           ts_code trade_date   open  close   high    low        vol  pct_chg  \\\n",
+      "1626778  002652.SZ 2019-01-02  19.59  19.64  19.89  19.28   20196.79     1.03   \n",
+      "1626779  002652.SZ 2019-01-03  19.74  19.44  19.84  19.33   15731.99    -1.02   \n",
+      "1626780  002652.SZ 2019-01-04  19.33  19.94  19.99  19.08   21099.93     2.57   \n",
+      "1626781  002652.SZ 2019-01-07  20.04  21.95  21.95  20.04   83534.19    10.08   \n",
+      "1626782  002652.SZ 2019-01-08  23.21  21.65  23.87  21.65  149377.97    -1.37   \n",
+      "...            ...        ...    ...    ...    ...    ...        ...      ...   \n",
+      "1628321  002652.SZ 2025-05-19  15.05  15.05  15.21  14.80  142648.00     1.69   \n",
+      "1628322  002652.SZ 2025-05-20  15.11  15.31  15.36  14.85  131075.23     1.73   \n",
+      "1628323  002652.SZ 2025-05-21  15.51  15.26  15.56  15.11  147109.00    -0.33   \n",
+      "1628324  002652.SZ 2025-05-22  15.11  14.95  15.46  14.75  129187.20    -2.03   \n",
+      "1628325  002652.SZ 2025-05-23  14.95  14.70  15.11  14.70  139145.40    -1.67   \n",
+      "\n",
+      "            amount  turnover_rate  ...  cs_rank_vol_x_profit_margin  \\\n",
+      "1626778   7867.047         0.3964  ...                     0.608839   \n",
+      "1626779   6121.460         0.3088  ...                     0.586710   \n",
+      "1626780   8245.083         0.4141  ...                     0.682847   \n",
+      "1626781  35514.117         1.6394  ...                     0.987591   \n",
+      "1626782  67160.354         2.9317  ...                     0.765693   \n",
+      "...            ...            ...  ...                          ...   \n",
+      "1628321  42651.655         2.7857  ...                     0.758644   \n",
+      "1628322  39438.290         2.5597  ...                     0.834661   \n",
+      "1628323  44703.816         2.8729  ...                     0.365327   \n",
+      "1628324  38679.608         2.5229  ...                     0.810362   \n",
+      "1628325  41151.946         2.7173  ...                     0.738293   \n",
+      "\n",
+      "         cs_rank_lg_flow_price_concordance  cs_rank_turnover_per_winner  \\\n",
+      "1626778                           0.203142                     0.864865   \n",
+      "1626779                           0.156684                     0.763417   \n",
+      "1626780                           0.184009                     0.660949   \n",
+      "1626781                           0.734940                     0.700000   \n",
+      "1626782                           0.874042                     0.914234   \n",
+      "...                                    ...                          ...   \n",
+      "1628321                           0.106051                     0.544548   \n",
+      "1628322                           0.202523                     0.478420   \n",
+      "1628323                           0.580870                     0.520757   \n",
+      "1628324                           0.808369                     0.476918   \n",
+      "1628325                           0.617735                     0.404517   \n",
+      "\n",
+      "         cs_rank_ind_cap_neutral_pe  cs_rank_volume_ratio  \\\n",
+      "1626778                         NaN              0.646930   \n",
+      "1626779                         NaN              0.251279   \n",
+      "1626780                         NaN              0.311724   \n",
+      "1626781                         NaN              0.988313   \n",
+      "1626782                         NaN              0.990142   \n",
+      "...                             ...                   ...   \n",
+      "1628321                         NaN              0.695645   \n",
+      "1628322                         NaN              0.542497   \n",
+      "1628323                         NaN              0.678180   \n",
+      "1628324                         NaN              0.524743   \n",
+      "1628325                         NaN              0.585852   \n",
+      "\n",
+      "         cs_rank_elg_buy_sell_sm_ratio  cs_rank_cost_dist_vol_ratio  \\\n",
+      "1626778                       0.341855                     0.678941   \n",
+      "1626779                       0.318912                     0.402916   \n",
+      "1626780                       0.260036                     0.460713   \n",
+      "1626781                       0.796350                     0.988501   \n",
+      "1626782                       0.598905                     0.991571   \n",
+      "...                                ...                          ...   \n",
+      "1628321                       0.287899                     0.788896   \n",
+      "1628322                       0.116534                     0.705843   \n",
+      "1628323                       0.492860                     0.783793   \n",
+      "1628324                       0.130521                     0.696446   \n",
+      "1628325                       0.134175                     0.735636   \n",
+      "\n",
+      "         cs_rank_size  future_return  label  \n",
+      "1626778      0.258948       0.092159   45.0  \n",
+      "1626779      0.258123       0.075103   41.0  \n",
+      "1626780      0.257664       0.058175   41.0  \n",
+      "1626781      0.290146      -0.034169    4.0  \n",
+      "1626782      0.282482      -0.023095    4.0  \n",
+      "...               ...            ...    ...  \n",
+      "1628321      0.032912            NaN    NaN  \n",
+      "1628322      0.034861            NaN    NaN  \n",
+      "1628323      0.035204            NaN    NaN  \n",
+      "1628324      0.034208            NaN    NaN  \n",
+      "1628325      0.032547            NaN    NaN  \n",
+      "\n",
+      "[1548 rows x 189 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[df['ts_code'] == '002652.SZ'])"
+   ]
   }
  ],
  "metadata": {
diff --git a/main/train/catboost_info/catboost_training.json b/main/train/catboost_info/catboost_training.json
index 0df5170..8adefcf 100644
--- a/main/train/catboost_info/catboost_training.json
+++ b/main/train/catboost_info/catboost_training.json
@@ -1,792 +1,1023 @@
 {
 "meta":{"test_sets":["test"],"test_metrics":[{"best_value":"Min","name":"Logloss"}],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":1500,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
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-{"learn":[0.6843872326],"iteration":1,"passed_time":0.4812678685,"remaining_time":360.4696335,"test":[0.6855813377]},
-{"learn":[0.680262838],"iteration":2,"passed_time":0.6902650401,"remaining_time":344.442255,"test":[0.6821273883]},
-{"learn":[0.6762009847],"iteration":3,"passed_time":0.9019649785,"remaining_time":337.334902,"test":[0.6785411841]},
-{"learn":[0.6723192017],"iteration":4,"passed_time":0.9480610433,"remaining_time":283.4702519,"test":[0.6752506177]},
-{"learn":[0.6682080126],"iteration":5,"passed_time":1.191684141,"remaining_time":296.729351,"test":[0.6727377765]},
-{"learn":[0.6642343721],"iteration":6,"passed_time":1.387762589,"remaining_time":295.9899351,"test":[0.6695440266]},
-{"learn":[0.6604090029],"iteration":7,"passed_time":1.586502997,"remaining_time":295.8828089,"test":[0.6666962384]},
-{"learn":[0.6567664506],"iteration":8,"passed_time":1.873528866,"remaining_time":310.3812821,"test":[0.6635424364]},
-{"learn":[0.6530918882],"iteration":9,"passed_time":2.092919986,"remaining_time":311.8450779,"test":[0.660442949]},
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-{"learn":[0.6326337693],"iteration":15,"passed_time":3.389681924,"remaining_time":314.3929985,"test":[0.6462357347]},
-{"learn":[0.6296338495],"iteration":16,"passed_time":3.494064623,"remaining_time":304.8057551,"test":[0.6434692389]},
-{"learn":[0.6265263841],"iteration":17,"passed_time":3.598875359,"remaining_time":296.3074045,"test":[0.6415186602]},
-{"learn":[0.6235141568],"iteration":18,"passed_time":3.815555239,"remaining_time":297.4124899,"test":[0.6387646556]},
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-{"learn":[0.6119898227],"iteration":22,"passed_time":4.721369101,"remaining_time":303.194007,"test":[0.6300870008]},
-{"learn":[0.6092233761],"iteration":23,"passed_time":4.962412586,"remaining_time":305.188374,"test":[0.6275533277]},
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\ No newline at end of file
diff --git a/main/train/catboost_info/learn/events.out.tfevents b/main/train/catboost_info/learn/events.out.tfevents
index 4c7fd6c..0b6d7e3 100644
Binary files a/main/train/catboost_info/learn/events.out.tfevents and b/main/train/catboost_info/learn/events.out.tfevents differ
diff --git a/main/train/catboost_info/learn_error.tsv b/main/train/catboost_info/learn_error.tsv
index c5fbfd7..bc1533f 100644
--- a/main/train/catboost_info/learn_error.tsv
+++ b/main/train/catboost_info/learn_error.tsv
@@ -1,789 +1,1020 @@
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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 128a556..7c13720 100644
Binary files a/main/train/catboost_info/test/events.out.tfevents and b/main/train/catboost_info/test/events.out.tfevents differ
diff --git a/main/train/catboost_info/test_error.tsv b/main/train/catboost_info/test_error.tsv
index 8c0936d..a99e676 100644
--- a/main/train/catboost_info/test_error.tsv
+++ b/main/train/catboost_info/test_error.tsv
@@ -1,789 +1,1020 @@
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diff --git a/main/train/catboost_info/time_left.tsv b/main/train/catboost_info/time_left.tsv
index 1b4a707..3773406 100644
--- a/main/train/catboost_info/time_left.tsv
+++ b/main/train/catboost_info/time_left.tsv
@@ -1,789 +1,1020 @@
 iter	Passed	Remaining
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diff --git a/main/train/predictions_test.tsv b/main/train/predictions_test.tsv
index 7efb568..2bddc26 100644
--- a/main/train/predictions_test.tsv
+++ b/main/train/predictions_test.tsv
@@ -1,1153 +1,577 @@
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