diff --git a/lib/dataread.py b/lib/dataread.py
index 7d180b5..3c7678b 100644
--- a/lib/dataread.py
+++ b/lib/dataread.py
@@ -436,6 +436,11 @@ def calculate_kdj(data, n=9):
 
 
 def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y',dataset='dataset',delweekenday=False,add_kdj=False,is_timefurture=False):
+    '''
+    原油特征数据处理函数，
+    接收的是两个df，一个是指标数据，一个是指标列表
+    输出的是一个df，包含ds，y，指标列
+    '''
     df = df_zhibiaoshuju.copy()
     if end_time == '':
         end_time = datetime.datetime.now().strftime('%Y-%m-%d')
@@ -457,6 +462,11 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
     two_months_ago = current_date - timedelta(days=40)
 
     def check_column(col_name):
+        '''
+        判断两月不更新指标
+        输入：列名
+        输出：True or False
+        '''
         if 'ds' in col_name or 'y' in col_name:
             return False
         df_check_column = df[['ds',col_name]]
@@ -469,7 +479,6 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
         return corresponding_date < two_months_ago
     columns_to_drop = df.columns[df.columns.map(check_column)].tolist()
     df = df.drop(columns = columns_to_drop)
-    
     logger.info(f'删除两月不更新特征后数据量：{df.shape}')
     
     # 删除预测列空值的行
@@ -481,7 +490,7 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
     # 去掉指标列表中的columns_to_drop的行
     df_zhibiaoliebiao = df_zhibiaoliebiao[df_zhibiaoliebiao['指标名称'].isin(df.columns.tolist())]
     df_zhibiaoliebiao.to_csv(os.path.join(dataset,'特征处理后的指标名称及分类.csv'),index=False)
-    # 频度分析
+    # 数据频度分析
     featurePindu(dataset=dataset)
     # 向上填充
     df = df.ffill()
@@ -491,26 +500,35 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
     # 删除周六日的数据
     if delweekenday:
         df = df[df['ds'].dt.weekday < 5]
-        
+    # kdj指标
     if add_kdj:
         df = calculate_kdj(df)
-        
+    # 衍生时间特征
     if is_timefurture:
         df = addtimecharacteristics(df=df,dataset=dataset)
-
+    # 特征分析
     featureAnalysis(df,dataset=dataset,y=y)
     return df
 
 def datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y',dataset='dataset',delweekenday=False,add_kdj=False,is_timefurture=False):
+    '''
+    聚烯烃特征数据处理函数，
+    接收的是两个df，一个是指标数据，一个是指标列表
+    输出的是一个df，包含ds，y，指标列
+    '''
     df = df_zhibiaoshuju.copy()
     if end_time == '':
         end_time = datetime.datetime.now().strftime('%Y-%m-%d')
     # date转为pddate
     df.rename(columns={datecol:'ds'},inplace=True)
     
+    # 指定列统一减少数值
     df[offsite_col] = df[offsite_col]-offsite
+    # 预测列为avg_cols的均值
     df[y] = df[avg_cols].mean(axis=1)
-    print(df[['ds',y]+avg_cols].head(20))
+    # 去掉多余的列avg_cols
+    df = df.drop(columns=avg_cols)
+
     # 重命名预测列
     df.rename(columns={y:'y'},inplace=True)
     # 按时间顺序排列
@@ -521,10 +539,10 @@ def datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time
     # 获取小于等于当前日期的数据
     df = df[df['ds'] <= end_time]
     logger.info(f'删除两月不更新特征前数据量：{df.shape}')
-    # 去掉近最后数据对应的日期在两月以前的列，删除近2月的数据是常熟的列
+    # 去掉近最后数据对应的日期在两月以前的列，删除近2月的数据是常数的列
     current_date = datetime.datetime.now()
     two_months_ago = current_date - timedelta(days=40)
-
+    # 检查两月不更新的特征 
     def check_column(col_name):
         if 'ds' in col_name or 'y' in col_name:
             return False
diff --git a/lib/tools.py b/lib/tools.py
index 61024d2..4f34b20 100644
--- a/lib/tools.py
+++ b/lib/tools.py
@@ -441,8 +441,5 @@ class SQLiteHandler:
         else:
             print(f"Column '{column_name}' already exists in table '{table_name}'.")
 
-    
-
-
 if __name__ == '__main__':
     print('This is a tool, not a script.')
\ No newline at end of file
diff --git a/main.py b/main_yuanyou.py
similarity index 99%
rename from main.py
rename to main_yuanyou.py
index 802697d..a51635d 100644
--- a/main.py
+++ b/main_yuanyou.py
@@ -26,7 +26,6 @@ def predict_main():
                             )
     # 获取数据
     if is_eta:
-        # eta数据
         logger.info('从eta获取数据...')
         signature = BinanceAPI(APPID, SECRET)
         etadata = EtaReader(signature=signature,
@@ -48,6 +47,7 @@ def predict_main():
         df = datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time) 
 
     else:
+        # 读取数据
         logger.info('读取本地数据：'+os.path.join(dataset,data_set))
         df = getdata(filename=os.path.join(dataset,data_set),y=y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time)  # 原始数据，未处理