设置原油配置，判断周一的时候发送预警信息

Untitled.ipynb

@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9048d286-415f-4a33-be3a-9d7099aa85de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from config_jingbo import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "07e338e7-1dd1-417f-b4e2-65d0efc983d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(os.path.join(dataset,'last_update_times.csv'))\n",
+    "df = df.sort_values(by=['warning_date'], ascending=[False])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "659eec10-f0b8-4d65-b70e-6f07f45291bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_last_update_time = df[df['feature']=='y']['warning_date'].values[0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "31035e6f-36e8-4b17-a14b-f7dd79ae085a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>last_update_time</th>\n",
+       "      <th>feature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>2024-11-14</td>\n",
+       "      <td>lmcads03 lme comdty</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>2024-11-14</td>\n",
+       "      <td>美国MEH轻原油现货价格（美湾）</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>2024-11-14</td>\n",
+       "      <td>WTI原油Cushing-C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>2024-11-14</td>\n",
+       "      <td>LLS原油现货价格</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>2024-11-14</td>\n",
+       "      <td>OMAN原油：中质：远期现货DES价：山东（日）</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>2023-01-03</td>\n",
+       "      <td>PVM Dubai EFS/Swap vs ICE First Month Brent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>2022-11-16</td>\n",
+       "      <td>Brent/迪拜原油升贴水</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>2022-01-31</td>\n",
+       "      <td>W-B活跃合约价差</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>2022-01-31</td>\n",
+       "      <td>B-W活跃合约价差</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>2018-10-24</td>\n",
+       "      <td>西北欧即期Brent 211利润</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>73 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    last_update_time                                      feature\n",
+       "500       2024-11-14                          lmcads03 lme comdty\n",
+       "251       2024-11-14                             美国MEH轻原油现货价格（美湾）\n",
+       "231       2024-11-14                              WTI原油Cushing-C1\n",
+       "264       2024-11-14                                    LLS原油现货价格\n",
+       "82        2024-11-14                     OMAN原油：中质：远期现货DES价：山东（日）\n",
+       "..               ...                                          ...\n",
+       "83        2023-01-03  PVM Dubai EFS/Swap vs ICE First Month Brent\n",
+       "91        2022-11-16                                Brent/迪拜原油升贴水\n",
+       "94        2022-01-31                                    W-B活跃合约价差\n",
+       "95        2022-01-31                                    B-W活跃合约价差\n",
+       "31        2018-10-24                             西北欧即期Brent 211利润\n",
+       "\n",
+       "[73 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['warning_date']<y_last_update_time][['last_update_time','feature']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "937a9adb-1652-4d6b-a5c7-828d7c17769d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.read_csv(os.path.join(dataset,''))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

config_jingbo.py

@@ -175,7 +175,7 @@ warning_data = {
 # is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
 # is_update_eta  = True  # 预测结果上传到eta
 # is_update_report = True # 是否上传报告
-# is_update_warning_data = False  # 是否上传预警数据
+# is_update_warning_data = True  if datetime.datetime.now().weekday() == 1 else False   # 是否上传预警数据
 
 
 ### 开关
@@ -188,7 +188,7 @@ is_edbcode = False # 特征使用edbcoding列表中的
 is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
 is_update_eta  = False  # 预测结果上传到eta
 is_update_report = False # 是否上传报告
-is_update_warning_data = True  # 是否上传预警数据
+is_update_warning_data =  False # 是否上传预警数据
 
 # 数据截取日期
 end_time = '' # 数据截取日期

lib/dataread.py

@@ -504,21 +504,27 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
     logger.info('特征停更信息保存到文件：last_update_times.csv')
 
     logger.info(f'删除预警的特征前数据量：{df.shape}')
-    y_last_update_time = last_update_times['y']
+    y_last_update_time = last_update_times_df[last_update_times_df['feature']=='y']['warning_date'].values[0]
     columns_to_drop = last_update_times_df[last_update_times_df['warning_date'] < y_last_update_time ]['feature'].values.tolist()
     df = df.drop(columns = columns_to_drop)
     
     logger.info(f'删除预警的特征后数据量：{df.shape}')
 
 
-    
     if is_update_warning_data:
         logger.info(f'上传预警信息')
         try:
-            warning_data_df = last_update_times_df[last_update_times_df['feature'].isin(columns_to_drop)][['last_update_time','feature']]
+            warning_data_df = last_update_times_df[last_update_times_df['warning_date']<y_last_update_time][['last_update_time','feature']]
-            warning_data_df.sort_values(by='last_update_time',ascending=False,inplace=True)
+            if len(warning_data_df) > 0:
+                content = '原油特征指标预警信息：\n\n'
+                warning_data_df = warning_data_df.sort_values(by='last_update_time',ascending=False)
+                for ds, df in warning_data_df.groupby('last_update_time'):
+                    content += f'{ds} \n {df["feature"].to_string(index=False).replace(" ", "")}\n\n'
+            else:
+                logger.info(f'没有需要上传的预警信息')
+                content = '没有需要维护的特征指标'
             warning_data['data']['WARNING_DATE'] = y_last_update_time
-            warning_data['data']['WARNING_CONTENT'] =  warning_data_df.to_string(index=False)
+            warning_data['data']['WARNING_CONTENT'] =  content
             token = get_head_auth_report()
             upload_warning_data(token, warning_data)
             logger.info(f'上传预警信息成功')

main_yuanyou.py

@@ -45,7 +45,7 @@ def predict_main():
         # 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
-
+ 
     # 更改预测列名称
     df.rename(columns={y:'y'},inplace=True)