diff --git a/Untitled.ipynb b/Untitled.ipynb
index 02b39dd..b5dc8b9 100644
--- a/Untitled.ipynb
+++ b/Untitled.ipynb
@@ -2,165 +2,48 @@
  "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": []
-  },
-  {
-   "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",
+   "execution_count": 6,
+   "id": "b3cde8ea",
    "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"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'ovx index': '原油波动率', 'dxy curncy': '美元指数', 'C2403128043': 'Brent连1合约价格拟合残差/美元指数', 'C2403150124': 'Brent连1合约价格拟合残差/Brent 连2-连3', 'DOESCRUD Index': '美国商业原油库存', 'FVHCM1 INDEX': '美国取暖油裂解C1', 'doedtprd index': '美国成品油表需', 'CFFDQMMN INDEX': 'WTI管理资金净多持仓', 'C2403083739': 'WTI基金多空持仓比', 'C2404167878': 'WTI基金净持仓COT指标（代码运算）', 'lmcads03 lme comdty': 'LME铜价', 'GC1 COMB Comdty': '黄金连1合约', 'C2404167855': '金油比'}\n"
+     ]
     }
    ],
    "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,''))"
+    "data = \"\"\"\n",
+    "ovx index   原油波动率\n",
+    "dxy curncy   美元指数\n",
+    "C2403128043   Brent连1合约价格拟合残差/美元指数\n",
+    "C2403150124   Brent连1合约价格拟合残差/Brent 连2-连3\n",
+    "DOESCRUD Index   美国商业原油库存\n",
+    "FVHCM1 INDEX   美国取暖油裂解C1\n",
+    "doedtprd index   美国成品油表需\n",
+    "CFFDQMMN INDEX   WTI管理资金净多持仓\n",
+    "C2403083739   WTI基金多空持仓比\n",
+    "C2404167878   WTI基金净持仓COT指标（代码运算）\n",
+    "lmcads03 lme comdty   LME铜价\n",
+    "GC1 COMB Comdty   黄金连1合约\n",
+    "C2404167855   金油比\n",
+    "\"\"\"\n",
+    "\n",
+    "result_dict = {}\n",
+    "lines = data.strip().split('\\n')\n",
+    "for line in lines:\n",
+    "    key, value = line.strip().split('   ')\n",
+    "    result_dict[key] = value\n",
+    "\n",
+    "print(result_dict)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
diff --git a/aisenzhecode/沥青/定性模型数据项12-11.xls b/aisenzhecode/沥青/定性模型数据项12-11.xls
index 8a5346d..9d76fd8 100644
Binary files a/aisenzhecode/沥青/定性模型数据项12-11.xls and b/aisenzhecode/沥青/定性模型数据项12-11.xls differ
diff --git a/aisenzhecode/沥青/日度价格预测_最佳模型.pkl b/aisenzhecode/沥青/日度价格预测_最佳模型.pkl
index 987c812..fbdd663 100644
Binary files a/aisenzhecode/沥青/日度价格预测_最佳模型.pkl and b/aisenzhecode/沥青/日度价格预测_最佳模型.pkl differ
diff --git a/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb b/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb
index 331f99e..030d2c1 100644
--- a/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb
+++ b/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -471,22 +471,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20241217\n"
+      "20241224\n",
+      "20241225\n"
      ]
     }
    ],
    "source": [
     "from datetime import datetime, timedelta\n",
     "\n",
-    "start_date = datetime(2024, 12, 17)\n",
-    "end_date = datetime(2024, 12, 18)\n",
+    "start_date = datetime(2024, 12, 24)\n",
+    "end_date = datetime(2024, 12, 26)\n",
     "\n",
     "while start_date < end_date:\n",
     "    print(start_date.strftime('%Y%m%d'))\n",
diff --git a/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb b/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb
index 1bc0950..0b1d622 100644
--- a/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb
+++ b/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb
@@ -795,14 +795,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20241217\n"
+      "20241223\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_4500\\2239815117.py:299: UserWarning:\n",
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2496\\2239815117.py:299: UserWarning:\n",
       "\n",
       "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
       "\n"
@@ -812,7 +812,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Using matplotlib backend: <object object at 0x0000017F4168F090>\n",
+      "Using matplotlib backend: <object object at 0x0000027F8B613090>\n",
       "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
       "Populating the interactive namespace from numpy and matplotlib\n",
       "Fitting 3 folds for each of 180 candidates, totalling 540 fits\n"
@@ -824,7 +824,7 @@
      "text": [
       "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:162: UserWarning:\n",
       "\n",
-      "pylab import has clobbered these variables: ['__version__', 'plot', 'random', 'datetime']\n",
+      "pylab import has clobbered these variables: ['__version__', 'random', 'datetime', 'plot']\n",
       "`%matplotlib` prevents importing * from pylab and numpy\n",
       "\n"
      ]
@@ -837,18 +837,18 @@
       "Best parameters set:\n",
       "\tlearning_rate: 0.1\n",
       "\tmax_depth: 8\n",
-      "\tn_estimators: 100\n"
+      "\tn_estimators: 90\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_4500\\2239815117.py:239: UserWarning:\n",
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2496\\2239815117.py:239: UserWarning:\n",
       "\n",
       "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
       "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_4500\\2239815117.py:273: FutureWarning:\n",
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2496\\2239815117.py:273: FutureWarning:\n",
       "\n",
       "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
       "\n"
@@ -859,18 +859,69 @@
      "output_type": "stream",
      "text": [
       "日期\n",
-      "2024-12-17    3501.835693\n",
+      "2024-12-23    3503.160645\n",
       "Name: 日度预测价格, dtype: float32\n",
       "{\"confirmFlg\":false,\"status\":true}\n",
-      "新增数据： ['2024-12-17', 7957.0, 6984.0, 0.0, 0.25, 3650.0, 0.55, 0.0, 0.0, 3520.0, 7.9, 0.2, 0.2, 3500.0, 0.75, '', 3500.0, 74.35, 72.69, '', 3522.0, 28.8998, '', '', '', 13.58990112, 229522.1, 7722.02, 3511.1707, '', '', 40514.8218813, 7088.86, '']\n"
+      "新增数据： ['2024-12-23', 7957.0, 6904.0, 0.08, 0.25, 3650.0, 1.54, 0.0, 0.0, 3500.0, 7.9, 0.1, 0.2, 3500.0, 1.05, '', 3500.0, 72.6, '', '', 3538.0, 27.0525, '', '', '', '', 229522.1, 8639.74, 3463.8854, '', '', 40121.2216621, 7423.12, '']\n",
+      "20241224\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2496\\2239815117.py:299: UserWarning:\n",
+      "\n",
+      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using matplotlib backend: QtAgg\n",
+      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
+      "Populating the interactive namespace from numpy and matplotlib\n",
+      "Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
+      "Best score: 0.997\n",
+      "Best parameters set:\n",
+      "\tlearning_rate: 0.1\n",
+      "\tmax_depth: 10\n",
+      "\tn_estimators: 100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2496\\2239815117.py:239: UserWarning:\n",
+      "\n",
+      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
+      "\n",
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2496\\2239815117.py:273: FutureWarning:\n",
+      "\n",
+      "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "日期\n",
+      "2024-12-24    3499.874512\n",
+      "Name: 日度预测价格, dtype: float32\n",
+      "{\"confirmFlg\":false,\"status\":true}\n",
+      "新增数据： ['2024-12-24', 7984.0, 6904.0, 0.08, 0.25, 3650.0, 1.71, 0.0, 0.0, 3500.0, 7.9, 0.1, 0.2, 3500.0, 1.15, '', 3500.0, 72.6, 72.67, '', 3521.0, 25.6158, '', '', '', 13.33799789, 229522.1, 5417.02, 3427.8064, '', 1000.0, 44319.2299367, '', 3650.0]\n"
      ]
     }
    ],
    "source": [
     "from datetime import datetime, timedelta\n",
     "\n",
-    "start_date = datetime(2024, 12, 17)\n",
-    "end_date = datetime(2024, 12, 18)\n",
+    "start_date = datetime(2024, 12, 23)\n",
+    "end_date = datetime(2024, 12, 25)\n",
     "\n",
     "while start_date < end_date:\n",
     "    print(start_date.strftime('%Y%m%d'))\n",
diff --git a/aisenzhecode/沥青/沥青数据项.xls b/aisenzhecode/沥青/沥青数据项.xls
index c008979..4599b2a 100644
Binary files a/aisenzhecode/沥青/沥青数据项.xls and b/aisenzhecode/沥青/沥青数据项.xls differ
diff --git a/config_jingbo.py b/config_jingbo.py
index ca3a5ac..6af9264 100644
--- a/config_jingbo.py
+++ b/config_jingbo.py
@@ -38,6 +38,21 @@ edbnamelist = [
     # '新加坡(含硫0.05%) 柴油现货价','柴油：10ppm：国际市场：FOB中间价：新加坡（日）','Bloomberg Commodity Fair Value Singapore Mogas 92 Swap Month 1','97#汽油FOB新加坡现货价','无铅汽油：97#：国际市场：FOB中间价：新加坡（日）'
     ]
 
+edbcodenamedict = {'ovx index': '原油波动率',
+             'dxy curncy': '美元指数',
+             'C2403128043': 'Brent连1合约价格拟合残差/美元指数',
+             'C2403150124': 'Brent连1合约价格拟合残差/Brent 连2-连3',
+             'DOESCRUD Index': '美国商业原油库存',
+             'FVHCM1 INDEX': '美国取暖油裂解C1',
+             'doedtprd index': '美国成品油表需',
+             'CFFDQMMN INDEX': 'WTI管理资金净多持仓',
+             'C2403083739': 'WTI基金多空持仓比',
+             'C2404167878': 'WTI基金净持仓COT指标（代码运算）',
+             'lmcads03 lme comdty': 'LME铜价',
+             'GC1 COMB Comdty': '黄金连1合约',
+             'C2404167855': '金油比'}
+
+
 
 
 # eta自有数据指标编码
@@ -94,6 +109,7 @@ ClassifyId = 1214
 # login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
 # upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
 # upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
+# query_data_list_item_nos_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
 
 # login_data = {
 #     "data": {
@@ -134,6 +150,18 @@ ClassifyId = 1214
 #   }
 # }
 
+# query_data_list_item_nos_data = {
+#    "funcModule": "数据项",
+#    "funcOperation": "查询",
+#     "data": {
+#         "dateStart":"20200101",
+#         "dateEnd":"20241231",
+#         "dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
+#     }
+# }
+
+
+
 # # 生产环境数据库
 # host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
 # port = 3306
@@ -236,7 +264,7 @@ table_name = 'v_tbl_crude_oil_warning'
 ### 开关
 is_train = False # 是否训练
 is_debug = False # 是否调试
-is_eta = True # 是否使用eta接口
+is_eta = False # 是否使用eta接口
 is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
 is_timefurture = True # 是否使用时间特征
 is_fivemodels = False # 是否使用之前保存的最佳的5个模型
diff --git a/data.csv b/data.csv
index 90073b0..7b16d7f 100644
--- a/data.csv
+++ b/data.csv
@@ -327,2241 +327,2243 @@
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-925,20211116,81.52,
-926,20211117,79.78,
-927,20211118,79.28,
-928,20211119,78.05,
-929,20211122,77.58,
-930,20211123,78.55,
-931,20211124,80.64,
-932,20211125,80.52,
-933,20211126,70.99,
-934,20211129,72.15,
-935,20211130,67.45,
-936,20211201,68.14,
-937,20211202,65.72,
-938,20211203,69.24,
-939,20211206,70.24,
-940,20211207,73.2,
-941,20211208,74.38,
-942,20211209,73.84,
-943,20211210,73.8,
-944,20211213,74.2,
-945,20211214,72.57,
-946,20211215,72.5,
-947,20211216,74.0,
-948,20211217,72.65,
-949,20211220,69.28,
-950,20211221,71.24,
-951,20211222,73.62,
-952,20211223,74.75,
-953,20211224,75.39,
-954,20211227,75.38,
-955,20211228,78.06,
-956,20211229,78.1,
-957,20211230,78.41,
-958,20211231,77.55,
-959,20220103,77.04,
-960,20220104,78.62,
-961,20220105,79.51,
-962,20220106,79.61,
-963,20220107,81.42,
-964,20220110,80.5,
-965,20220111,80.89,
-966,20220112,83.52,
-967,20220113,83.79,
-968,20220114,83.99,
-969,20220117,85.54,
-970,20220118,86.44,
-971,20220119,87.61,
-972,20220120,87.05,
-973,20220121,85.71,
-974,20220124,85.04,
-975,20220125,86.2,
-976,20220126,86.71,
-977,20220127,87.79,
-978,20220128,88.15,
-979,20220131,88.09,
-980,20220201,87.72,
-981,20220202,88.28,
-982,20220203,88.02,
-983,20220204,91.2,
-984,20220207,92.13,
-985,20220208,89.93,
-986,20220209,90.05,
-987,20220210,90.88,
-988,20220211,90.51,
-989,20220214,93.44,
-990,20220215,92.06,
-991,20220216,91.11,
-992,20220217,92.14,
-993,20220218,90.12,
-994,20220221,92.57,
-995,20220222,95.8,
-996,20220223,92.81,
-997,20220224,94.12,
-998,20220225,92.75,
-999,20220228,96.34,
-1000,20220301,98.3,
-1001,20220302,106.83,
-1002,20220303,109.4,
-1003,20220304,109.62,
-1004,20220307,119.04,
-1005,20220308,121.31,
-1006,20220309,105.6,
-1007,20220310,109.0,
-1008,20220311,107.13,
-1009,20220314,103.49,
-1010,20220315,97.44,
-1011,20220316,96.93,
-1012,20220317,97.75,
-1013,20220318,102.94,
-1014,20220321,104.29,
-1015,20220322,109.31,
-1016,20220323,110.78,
-1017,20220324,113.41,
-1018,20220325,111.88,
-1019,20220328,106.06,
-1020,20220329,102.19,
-1021,20220330,107.93,
-1022,20220331,104.06,
-1023,20220401,102.35,
-1024,20220404,102.89,
-1025,20220405,104.53,
-1026,20220406,100.54,
-1027,20220407,98.41,
-1028,20220408,99.65,
-1029,20220411,97.57,
-1030,20220412,98.9,
-1031,20220413,104.06,
-1032,20220414,106.63,
-1033,20220418,110.71,
-1034,20220419,106.74,
-1035,20220420,104.65,
-1036,20220421,106.76,
-1037,20220422,105.04,
-1038,20220425,99.25,
-1039,20220426,100.91,
-1040,20220427,103.0,
-1041,20220428,102.9,
-1042,20220429,106.08,
-1043,20220502,103.1,
-1044,20220503,104.62,
-1045,20220504,105.46,
-1046,20220505,109.2,
-1047,20220506,109.87,
-1048,20220509,105.0,
-1049,20220510,101.54,
-1050,20220511,101.3,
-1051,20220512,104.69,
-1052,20220513,107.79,
-1053,20220516,108.84,
-1054,20220517,111.09,
-1055,20220518,108.35,
-1056,20220519,105.7,
-1057,20220520,110.52,
-1058,20220523,111.86,
-1059,20220524,111.7,
-1060,20220525,110.18,
-1061,20220526,110.88,
-1062,20220527,113.12,
-1063,20220530,115.32,
-1064,20220531,115.15,
-1065,20220601,115.4,
-1066,20220602,112.45,
-1067,20220603,116.02,
-1068,20220606,118.77,
-1069,20220607,118.55,
-1070,20220608,120.45,
-1071,20220609,122.5,
-1072,20220610,119.81,
-1073,20220613,118.93,
-1074,20220614,118.91,
-1075,20220615,117.75,
-1076,20220616,115.56,
-1077,20220617,111.72,
-1078,20220620,111.52,
-1079,20220621,114.02,
-1080,20220622,107.03,
-1081,20220623,105.03,
-1082,20220624,105.67,
-1083,20220627,107.15,
-1084,20220628,111.48,
-1085,20220629,111.74,
-1086,20220630,108.19,
-1087,20220701,108.03,
-1088,20220704,110.4,
-1089,20220705,101.1,
-1090,20220706,98.5,
-1091,20220707,98.5,
-1092,20220708,103.67,
-1093,20220711,103.64,
-1094,20220712,98.9,
-1095,20220713,98.28,
-1096,20220714,94.5,
-1097,20220715,98.17,
-1098,20220718,99.46,
-1099,20220719,103.6,
-1100,20220720,105.08,
-1101,20220721,101.5,
-1102,20220722,97.5,
-1103,20220725,96.7,
-1104,20220726,99.05,
-1105,20220727,98.85,
-1106,20220728,100.97,
-1107,20220729,101.64,
-1108,20220801,99.09,
-1109,20220802,98.49,
-1110,20220803,96.5,
-1111,20220804,93.2,
-1112,20220805,92.78,
-1113,20220808,93.01,
-1114,20220809,94.9,
-1115,20220810,93.63,
-1116,20220811,96.63,
-1117,20220812,96.94,
-1118,20220815,92.78,
-1119,20220816,91.71,
-1120,20220817,91.51,
-1121,20220818,93.05,
-1122,20220819,94.25,
-1123,20220822,92.36,
-1124,20220823,96.53,
-1125,20220824,99.1,
-1126,20220825,98.23,
-1127,20220826,96.96,
-1128,20220829,98.25,
-1129,20220830,96.63,
-1130,20220831,94.4,
-1131,20220901,91.81,
-1132,20220902,92.65,
-1133,20220905,93.02,
-1134,20220906,92.3,
-1135,20220907,87.4,
-1136,20220908,87.24,
-1137,20220909,88.6,
-1138,20220912,91.21,
-1139,20220913,91.05,
-1140,20220914,91.93,
-1141,20220915,90.03,
-1142,20220916,90.25,
-1143,20220919,88.5,
-1144,20220920,89.8,
-1145,20220921,88.31,
-1146,20220922,88.28,
-1147,20220923,84.55,
-1148,20220926,82.44,
-1149,20220927,82.81,
-1150,20220928,82.94,
-1151,20220929,86.15,
-1152,20220930,85.0,
-1153,20221003,86.35,
-1154,20221004,88.78,
-1155,20221005,90.76,
-1156,20221006,92.72,
-1157,20221007,93.94,
-1158,20221010,95.6,
-1159,20221011,93.11,
-1160,20221012,91.56,
-1161,20221013,91.08,
-1162,20221014,91.28,
-1163,20221017,90.83,
-1164,20221018,88.77,
-1165,20221019,89.32,
-1166,20221020,91.91,
-1167,20221021,91.0,
-1168,20221024,91.22,
-1169,20221025,91.62,
-1170,20221026,90.39,
-1171,20221027,93.18,
-1172,20221028,93.02,
-1173,20221031,91.46,
-1174,20221101,92.33,
-1175,20221102,94.01,
-1176,20221103,94.19,
-1177,20221104,94.33,
-1178,20221107,96.5,
-1179,20221108,95.0,
-1180,20221109,92.33,
-1181,20221110,91.73,
-1182,20221111,93.69,
-1183,20221114,92.5,
-1184,20221115,91.53,
-1185,20221116,91.67,
-1186,20221117,89.53,
-1187,20221118,85.8,
-1188,20221121,82.31,
-1189,20221122,86.97,
-1190,20221123,83.91,
-1191,20221124,84.0,
-1192,20221125,83.66,
-1193,20221128,80.81,
-1194,20221129,83.19,
-1195,20221130,84.45,
-1196,20221201,86.26,
-1197,20221202,85.16,
-1198,20221205,82.52,
-1199,20221206,78.7,
-1200,20221207,76.91,
-1201,20221208,75.74,
-1202,20221209,75.11,
-1203,20221212,75.26,
-1204,20221213,78.1,
-1205,20221214,80.11,
-1206,20221215,80.83,
-1207,20221216,78.29,
-1208,20221219,78.6,
-1209,20221220,78.28,
-1210,20221221,80.06,
-1211,20221222,81.35,
-1212,20221223,81.83,
-1213,20221227,84.19,
-1214,20221228,82.51,
-1215,20221229,81.85,
-1216,20221230,82.88,
-1217,20240502,83.05,
-1218,20240503,82.78,
-1219,20240506,82.77,
-1220,20240507,82.41,
-1221,20240718,84.22,
-1222,20240624,83.89,
-1223,20240619,84.9,
-1224,20240613,81.8,
-1225,20240410,88.83,
-1226,20240404,88.72,
-1227,20241125,72.3,
-1228,20241119,72.61,
-1229,20240930,71.03,
-1230,20241001,69.91,
-1231,20241002,73.59,
-1232,20241003,74.31,
-1233,20241004,77.39,
-1234,20241007,77.23,
-1235,20241008,76.36,
-1236,20241009,75.15,
-1237,20241010,76.72,
-1238,20240325,84.79,
-1239,20241113,70.72,
-1240,20241101,72.89,
-1241,20240710,84.0,
-1242,20240711,84.64,
-1243,20240712,84.95,
-1244,20240715,84.42,
-1245,20240501,83.29,
-1246,20240521,82.04,
-1247,20240605,77.18,
-1248,20240606,78.38,
-1249,20240607,79.32,
-1250,20240610,79.34,
-1251,20240611,81.19,
-1252,20240612,81.96,
-1253,20240320,85.59,
-1254,20240530,81.83,
-1255,20240906,70.61,
-1256,20240603,78.09,
-1257,20240719,82.56,
-1258,20240625,83.94,
-1259,20240701,84.85,
-1260,20241210,70.73,
-1261,20240708,85.56,
-1262,20241217,72.48,
-1263,20241223,71.69,
-1264,20241211,72.15,
-1265,20240614,81.92,
-1266,20240620,84.19,
-1267,20241220,71.59,
-1268,20240523,80.93,
-1269,20241202,71.52,
-1270,20240405,90.57,
-1271,20241205,71.8,
-1272,20241126,71.63,
-1273,20241106,73.34,
-1274,20241114,71.79,
-1275,20240326,85.19,
-1276,20240604,76.76,
-1277,20240820,76.55,
-1278,20240814,79.6,
-1279,20240806,75.58,
-1280,20240807,75.95,
-1281,20240808,77.63,
-1282,20240426,87.62,
-1283,20240709,,85.85
-1284,20241224,,73.41
-1285,20240626,,85.03
-1286,20241212,,74.0
-1287,20240702,,87.46
-1288,20240627,,85.58
-1289,20240621,,85.3
-1290,20241218,,74.2
-1291,20241206,,72.19
-1292,20240425,,88.06
-1293,20240412,,92.18
-1294,20241203,,73.93
-1295,20241107,,76.08
-1296,20241127,,72.85
-1297,20240321,,85.99
-1298,20240812,,82.4
-1299,20240813,,82.3
-1300,20240327,,85.7
-1301,20241028,,72.92
-1302,20240524,,82.16
-1303,20240926,,73.28
-1304,20240416,,90.84
-1305,20240417,,90.17
-1306,20240418,,87.8
-1307,20240419,,90.75
-1308,20240422,,87.26
-1309,20240423,,88.51
-1310,20240424,,87.8
-1311,20240522,,82.63
-1312,20240902,,77.63
-1313,20240815,,81.43
-1314,20240809,,79.83
-1315,20240703,,87.39
-1316,20240816,,81.02
-1317,20240819,,79.81
-1318,20240628,,86.18
-1319,20240909,,72.21
-1320,20240910,,72.28
-1321,20240911,,71.09
-1322,20240912,,72.87
-1323,20240913,,72.57
-1324,20240916,,72.72
-1325,20240917,,73.55
-1326,20240918,,73.33
-1327,20240919,,74.35
-1328,20240920,,74.16
-1329,20240923,,74.44
-1330,20240924,,75.12
-1331,20240925,,74.67
-1332,20241219,,73.45
-1333,20241213,,74.59
-1334,20240508,,83.82
-1335,20240509,,84.33
-1336,20240510,,84.53
-1337,20241216,,73.28
-1338,20240515,,83.07
-1339,20241204,,74.28
-1340,20241120,,73.94
-1341,20240531,,82.69
-1342,20241108,,75.25
-1343,20241128,,72.96
-1344,20241122,,75.41
-1345,20241104,,75.4
-1346,20240328,,87.07
-1347,20241029,,72.11
-1348,20240411,,90.92
-1349,20240322,,85.58
-1350,20240927,,72.0
-1351,20240821,,78.21
-1352,20240822,,77.7
-1353,20240903,,77.55
-1354,20240716,,84.86
-1355,20240704,,87.59
-1356,20200102,,66.58
-1357,20200103,,69.5
-1358,20200106,,70.74
-1359,20200107,,68.75
-1360,20200108,,71.75
-1361,20200109,,66.1
-1362,20200110,,65.66
-1363,20200113,,65.27
-1364,20200114,,65.05
-1365,20200115,,64.6
-1366,20200116,,64.97
-1367,20200117,,65.6
-1368,20200120,,66.0
-1369,20200121,,64.48
-1370,20200122,,63.81
-1371,20200123,,61.92
-1372,20200124,,61.68
-1373,20200127,,60.16
-1374,20200128,,59.39
-1375,20200129,,59.84
-1376,20200130,,58.75
-1377,20200131,,58.58
-1378,20200203,,56.77
-1379,20200204,,55.65
-1380,20200205,,56.46
-1381,20200206,,56.58
-1382,20200207,,55.4
-1383,20200210,,54.71
-1384,20200211,,54.69
-1385,20200212,,56.36
-1386,20200213,,56.83
-1387,20200214,,57.42
-1388,20200217,,57.52
-1389,20200218,,57.58
-1390,20200219,,59.03
-1391,20200220,,59.56
-1392,20200221,,58.72
-1393,20200224,,57.26
-1394,20200225,,56.38
-1395,20200226,,54.78
-1396,20200227,,52.65
-1397,20200228,,51.14
-1398,20200302,,53.2
-1399,20200303,,53.9
-1400,20200304,,53.03
-1401,20200305,,52.04
-1402,20200306,,50.45
-1403,20200309,,38.34
-1404,20200310,,38.22
-1405,20200311,,39.7
-1406,20200312,,37.8
-1407,20200313,,37.41
-1408,20200316,,36.97
-1409,20200317,,32.91
-1410,20200318,,30.9
-1411,20200319,,31.23
-1412,20200320,,32.87
-1413,20200323,,29.81
-1414,20200324,,30.95
-1415,20200325,,30.87
-1416,20200326,,30.19
-1417,20200327,,29.33
-1418,20200330,,27.53
-1419,20200331,,27.91
-1420,20200401,,26.28
-1421,20200402,,36.29
-1422,20200403,,35.0
-1423,20200406,,34.24
-1424,20200407,,34.18
-1425,20200408,,33.87
-1426,20200409,,36.4
-1427,20200414,,32.32
-1428,20200415,,30.33
-1429,20200416,,29.0
-1430,20200417,,32.48
-1431,20200420,,31.86
-1432,20200421,,30.18
-1433,20200422,,26.0
-1434,20200423,,26.98
-1435,20200424,,26.19
-1436,20200427,,25.12
-1437,20200428,,23.79
-1438,20200429,,25.23
-1439,20200430,,27.0
-1440,20200501,,27.88
-1441,20200504,,28.08
-1442,20200505,,32.06
-1443,20200506,,32.27
-1444,20200507,,31.84
-1445,20200511,,31.47
-1446,20200512,,30.7
-1447,20200513,,30.55
-1448,20200514,,31.55
-1449,20200515,,32.91
-1450,20200518,,35.72
-1451,20200519,,35.76
-1452,20200520,,36.46
-1453,20200521,,36.98
-1454,20200522,,36.23
-1455,20200526,,37.24
-1456,20200527,,36.71
-1457,20200528,,36.63
-1458,20200529,,38.17
-1459,20200601,,38.68
-1460,20200602,,39.75
-1461,20200603,,40.53
-1462,20200604,,40.1
-1463,20200605,,42.48
-1464,20200608,,43.41
-1465,20200609,,41.45
-1466,20200610,,41.99
-1467,20200611,,41.01
-1468,20200612,,39.45
-1469,20200615,,39.9
-1470,20200616,,41.65
-1471,20200617,,41.46
-1472,20200618,,41.64
-1473,20200619,,42.92
-1474,20200622,,43.18
-1475,20200623,,43.97
-1476,20200624,,42.95
-1477,20200625,,41.55
-1478,20200626,,41.81
-1479,20200629,,42.0
-1480,20200630,,41.98
-1481,20200701,,42.62
-1482,20200702,,43.23
-1483,20200703,,42.99
-1484,20200706,,43.71
-1485,20200707,,43.45
-1486,20200708,,43.47
-1487,20200709,,43.5
-1488,20200710,,43.42
-1489,20200713,,43.34
-1490,20200714,,43.22
-1491,20200715,,43.85
-1492,20200716,,43.8
-1493,20200717,,43.45
-1494,20200720,,43.45
-1495,20200721,,44.89
-1496,20200722,,44.4
-1497,20200723,,44.77
-1498,20200724,,43.9
-1499,20200727,,44.2
-1500,20200728,,44.34
-1501,20200729,,44.29
-1502,20200730,,44.22
-1503,20200731,,43.79
-1504,20200803,,44.46
-1505,20200804,,44.82
-1506,20200805,,46.23
-1507,20200806,,45.71
-1508,20200807,,45.3
-1509,20200810,,45.26
-1510,20200811,,45.79
-1511,20200812,,45.63
-1512,20200813,,45.58
-1513,20200814,,45.29
-1514,20200817,,45.41
-1515,20200818,,45.55
-1516,20200819,,45.48
-1517,20200820,,45.18
-1518,20200821,,45.08
-1519,20200824,,45.18
-1520,20200825,,46.52
-1521,20200826,,46.54
-1522,20200827,,46.33
-1523,20200828,,45.94
-1524,20200901,,46.22
-1525,20200902,,46.05
-1526,20200903,,44.65
-1527,20200904,,44.54
-1528,20200907,,42.43
-1529,20200908,,42.23
-1530,20200909,,41.2
-1531,20200910,,40.96
-1532,20200911,,40.34
-1533,20200914,,40.09
-1534,20200915,,40.82
-1535,20200916,,42.44
-1536,20200917,,43.5
-1537,20200918,,43.8
-1538,20200921,,43.84
-1539,20200922,,42.72
-1540,20200923,,43.07
-1541,20200924,,42.54
-1542,20200925,,42.8
-1543,20200928,,43.03
-1544,20200929,,42.97
-1545,20200930,,42.49
-1546,20201001,,42.56
-1547,20201002,,40.77
-1548,20201005,,41.78
-1549,20201006,,42.84
-1550,20201007,,42.42
-1551,20201008,,43.47
-1552,20201009,,43.57
-1553,20201012,,42.71
-1554,20201013,,42.68
-1555,20201014,,43.45
-1556,20201015,,43.48
-1557,20201016,,43.21
-1558,20201019,,43.23
-1559,20201020,,43.3
-1560,20201021,,43.04
-1561,20201022,,42.79
-1562,20201023,,42.8
-1563,20201026,,41.92
-1564,20201027,,41.88
-1565,20201028,,41.11
-1566,20201029,,39.97
-1567,20201030,,38.71
-1568,20201102,,39.36
-1569,20201103,,40.45
-1570,20201104,,41.33
-1571,20201105,,41.53
-1572,20201106,,40.69
-1573,20201109,,43.48
-1574,20201110,,44.11
-1575,20201111,,45.3
-1576,20201112,,44.5
-1577,20201113,,43.34
-1578,20201116,,44.67
-1579,20201117,,44.25
-1580,20201118,,44.89
-1581,20201119,,44.57
-1582,20201120,,45.24
-1583,20201123,,46.12
-1584,20201124,,48.0
-1585,20201125,,48.96
-1586,20201126,,49.0
-1587,20201127,,48.46
-1588,20201130,,48.29
-1589,20201201,,48.3
-1590,20201202,,48.76
-1591,20201203,,48.94
-1592,20201204,,49.92
-1593,20201207,,49.43
-1594,20201208,,49.0
-1595,20201209,,49.55
-1596,20201210,,51.06
-1597,20201211,,50.74
-1598,20201214,,50.8
-1599,20201215,,50.89
-1600,20201216,,51.2
-1601,20201217,,51.9
-1602,20201218,,52.48
-1603,20201221,,52.32
-1604,20201222,,50.86
-1605,20201223,,51.62
-1606,20201224,,51.78
-1607,20201229,,51.69
-1608,20201230,,51.92
-1609,20201231,,51.84
-1610,20240826,,80.53
-1611,20240827,,80.46
-1612,20240828,,79.11
-1613,20240829,,79.57
-1614,20240830,,79.52
-1615,20240527,,82.96
-1616,20240528,,84.35
-1617,20241115,,72.49
-1618,20240408,,91.1
-1619,20241121,,73.0
-1620,20240401,,87.98
-1621,20240402,,89.32
-1622,20241129,,73.34
-1623,20241111,,74.13
-1624,20240513,,83.84
-1625,20240514,,83.62
-1626,20241105,,74.71
-1627,20241011,,79.5
-1628,20241014,,78.55
-1629,20241030,,72.74
-1630,20241015,,75.57
-1631,20241016,,74.93
-1632,20241017,,74.92
-1633,20241018,,74.95
-1634,20241021,,74.57
-1635,20241022,,76.32
-1636,20241023,,76.05
-1637,20241024,,76.54
-1638,20240516,,83.78
-1639,20240823,,78.37
-1640,20240904,,74.8
-1641,20240604,,78.22
-1642,20240717,,85.2
-1643,20230103,,87.0
-1644,20230104,,82.72
-1645,20230105,,79.97
-1646,20230106,,80.59
-1647,20230109,,81.37
-1648,20230110,,80.89
-1649,20230111,,83.0
-1650,20230112,,84.62
-1651,20230113,,85.5
-1652,20230116,,85.59
-1653,20230117,,86.82
-1654,20230118,,87.85
-1655,20230119,,86.85
-1656,20230120,,87.79
-1657,20230123,,89.09
-1658,20230124,,88.61
-1659,20230125,,87.07
-1660,20230126,,87.99
-1661,20230127,,88.63
-1662,20230130,,87.28
-1663,20230131,,85.79
-1664,20230201,,86.21
-1665,20230202,,83.61
-1666,20230203,,84.2
-1667,20230206,,81.43
-1668,20230207,,84.13
-1669,20230208,,85.15
-1670,20230209,,85.5
-1671,20230210,,86.9
-1672,20230213,,86.95
-1673,20230214,,86.42
-1674,20230215,,85.85
-1675,20230216,,86.21
-1676,20230217,,84.87
-1677,20230220,,84.3
-1678,20230221,,84.2
-1679,20230222,,83.25
-1680,20230223,,82.46
-1681,20230224,,83.09
-1682,20230227,,83.25
-1683,20230228,,83.99
-1684,20230301,,84.49
-1685,20230302,,85.1
-1686,20230303,,86.0
-1687,20230306,,86.35
-1688,20230307,,86.75
-1689,20230308,,83.59
-1690,20230309,,84.0
-1691,20230310,,83.11
-1692,20230313,,83.48
-1693,20230314,,80.92
-1694,20230315,,78.73
-1695,20230316,,75.12
-1696,20230317,,75.59
-1697,20230320,,73.65
-1698,20230321,,75.12
-1699,20230322,,76.79
-1700,20230323,,77.06
-1701,20230324,,75.89
-1702,20230327,,78.0
-1703,20230328,,78.7
-1704,20230329,,79.0
-1705,20230330,,78.85
-1706,20230331,,79.95
-1707,20230403,,86.44
-1708,20230404,,86.04
-1709,20230405,,85.54
-1710,20230406,,85.45
-1711,20230410,,85.66
-1712,20230411,,85.67
-1713,20230412,,87.49
-1714,20230413,,87.47
-1715,20230414,,86.87
-1716,20230417,,86.52
-1717,20230418,,85.41
-1718,20230419,,85.15
-1719,20230420,,82.92
-1720,20230421,,82.05
-1721,20230424,,82.8
-1722,20230425,,82.88
-1723,20230426,,81.36
-1724,20230427,,78.55
-1725,20230428,,80.53
-1726,20230501,,80.24
-1727,20230502,,79.78
-1728,20230503,,75.58
-1729,20230504,,73.72
-1730,20230505,,75.75
-1731,20230508,,77.43
-1732,20230509,,77.5
-1733,20230510,,77.6
-1734,20230511,,77.41
-1735,20230512,,75.85
-1736,20230515,,75.75
-1737,20230516,,75.95
-1738,20230517,,77.31
-1739,20230518,,76.99
-1740,20230519,,77.5
-1741,20230522,,76.46
-1742,20230523,,77.74
-1743,20230524,,78.66
-1744,20230525,,78.39
-1745,20230526,,77.32
-1746,20230529,,77.83
-1747,20230530,,77.59
-1748,20230531,,73.95
-1749,20230601,,75.25
-1750,20230602,,76.5
-1751,20230605,,78.73
-1752,20230606,,76.75
-1753,20230607,,77.64
-1754,20230608,,77.68
-1755,20230609,,76.57
-1756,20230612,,74.87
-1757,20230613,,74.66
-1758,20230614,,75.49
-1759,20230615,,75.97
-1760,20230616,,76.74
-1761,20230619,,76.93
-1762,20230620,,77.15
-1763,20230621,,77.24
-1764,20230622,,77.2
-1765,20230623,,74.55
-1766,20230626,,75.0
-1767,20230627,,75.08
-1768,20230628,,74.42
-1769,20230629,,75.15
-1770,20230630,,75.84
-1771,20230703,,76.6
-1772,20230704,,76.29
-1773,20230705,,76.95
-1774,20230706,,77.09
-1775,20230707,,78.54
-1776,20230710,,78.77
-1777,20230711,,79.5
-1778,20230712,,80.55
-1779,20230713,,81.75
-1780,20230714,,81.7
-1781,20230717,,80.64
-1782,20230718,,79.99
-1783,20230719,,80.93
-1784,20230720,,80.27
-1785,20230721,,81.24
-1786,20230724,,82.9
-1787,20230725,,83.47
-1788,20230726,,83.43
-1789,20230727,,84.09
-1790,20230728,,84.5
-1791,20230731,,85.58
-1792,20230801,,85.91
-1793,20230802,,85.99
-1794,20230803,,85.41
-1795,20230804,,86.65
-1796,20230807,,86.73
-1797,20230808,,86.34
-1798,20230809,,87.65
-1799,20230810,,88.1
-1800,20230811,,87.35
-1801,20230814,,86.71
-1802,20230815,,86.68
-1803,20230816,,85.36
-1804,20230817,,84.88
-1805,20230818,,85.08
-1806,20230821,,85.86
-1807,20230822,,84.66
-1808,20230823,,84.26
-1809,20230824,,83.23
-1810,20230825,,84.41
-1811,20230828,,84.69
-1812,20230829,,85.07
-1813,20230830,,85.68
-1814,20230831,,86.88
-1815,20230901,,88.99
-1816,20230904,,89.22
-1817,20230905,,91.15
-1818,20230906,,91.1
-1819,20230907,,90.89
-1820,20230908,,91.02
-1821,20230911,,91.45
-1822,20230912,,92.4
-1823,20230913,,92.84
-1824,20230914,,94.21
-1825,20230915,,94.63
-1826,20230918,,93.91
-1827,20230919,,94.57
-1828,20230920,,93.51
-1829,20230921,,93.44
-1830,20230922,,93.37
-1831,20230925,,92.73
-1832,20230926,,92.7
-1833,20230927,,94.8
-1834,20230928,,95.35
-1835,20230929,,94.13
-1836,20231002,,93.33
-1837,20231003,,91.56
-1838,20231004,,91.21
-1839,20231005,,86.52
-1840,20231006,,84.95
-1841,20231009,,89.0
-1842,20231010,,88.49
-1843,20231011,,88.26
-1844,20231012,,87.64
-1845,20231013,,91.0
-1846,20231016,,91.39
-1847,20231017,,91.0
-1848,20231018,,93.0
-1849,20231019,,93.48
-1850,20231020,,93.79
-1851,20231023,,92.45
-1852,20231024,,90.68
-1853,20231025,,89.31
-1854,20231026,,89.27
-1855,20231027,,89.43
-1856,20231030,,88.94
-1857,20231031,,87.39
-1858,20231101,,87.24
-1859,20231102,,87.05
-1860,20231103,,87.8
-1861,20231106,,86.46
-1862,20231107,,85.19
-1863,20231108,,81.96
-1864,20231109,,81.48
-1865,20231110,,82.06
-1866,20231113,,82.84
-1867,20231114,,83.97
-1868,20231115,,83.04
-1869,20231116,,81.17
-1870,20231117,,80.81
-1871,20231120,,82.94
-1872,20231121,,82.54
-1873,20231122,,82.65
-1874,20231123,,81.48
-1875,20231124,,82.03
-1876,20231127,,81.14
-1877,20231128,,81.97
-1878,20231129,,82.99
-1879,20231130,,84.61
-1880,20231201,,81.54
-1881,20231204,,79.72
-1882,20231205,,79.09
-1883,20231206,,77.65
-1884,20231207,,75.48
-1885,20231208,,76.36
-1886,20231211,,76.5
-1887,20231212,,76.66
-1888,20231213,,74.7
-1889,20231214,,77.35
-1890,20231215,,77.23
-1891,20231218,,79.51
-1892,20231219,,79.77
-1893,20231220,,80.6
-1894,20231221,,79.99
-1895,20231222,,80.1
-1896,20231226,,81.45
-1897,20231227,,81.02
-1898,20231228,,79.87
-1899,20231229,,77.98
-1900,20240102,,79.06
-1901,20240103,,78.67
-1902,20240104,,79.41
-1903,20240105,,79.26
-1904,20240108,,78.95
-1905,20240109,,78.19
-1906,20240110,,78.73
-1907,20240111,,79.1
-1908,20240112,,80.75
-1909,20240115,,78.9
-1910,20240116,,79.4
-1911,20240117,,78.19
-1912,20240118,,79.3
-1913,20240119,,79.73
-1914,20240122,,80.55
-1915,20240123,,80.43
-1916,20240124,,80.73
-1917,20240125,,82.07
-1918,20240126,,83.19
-1919,20240129,,84.17
-1920,20240130,,82.86
-1921,20240131,,82.65
-1922,20240201,,81.55
-1923,20240202,,79.44
-1924,20240205,,78.33
-1925,20240206,,79.08
-1926,20240207,,79.5
-1927,20240208,,81.9
-1928,20240209,,82.45
-1929,20240212,,82.19
-1930,20240213,,83.24
-1931,20240214,,83.6
-1932,20240215,,83.25
-1933,20240216,,83.66
-1934,20240219,,83.6
-1935,20240220,,83.63
-1936,20240221,,83.17
-1937,20240222,,82.9
-1938,20240223,,82.54
-1939,20240226,,82.14
-1940,20240227,,82.75
-1941,20240228,,83.13
-1942,20240229,,82.84
-1943,20240301,,84.34
-1944,20240304,,84.08
-1945,20240305,,83.14
-1946,20240306,,84.05
-1947,20240307,,83.53
-1948,20240308,,83.84
-1949,20240311,,82.75
-1950,20240312,,83.01
-1951,20240313,,84.24
-1952,20240314,,85.69
-1953,20240315,,85.55
-1954,20240318,,87.18
-1955,20240705,,87.95
-1956,20240617,,84.55
-1957,20241209,,72.65
-1958,20240520,,84.49
-1959,20240618,,85.49
-1960,20240517,,84.04
-1961,20240409,,90.94
-1962,20240403,,89.99
-1963,20241118,,73.47
-1964,20241112,,72.82
-1965,20241031,,74.28
-1966,20241025,,76.14
-1967,20240722,,83.22
-1968,20240723,,82.79
-1969,20240724,,81.29
-1970,20240725,,81.56
-1971,20240726,,81.72
-1972,20240729,,80.86
-1973,20240730,,79.21
-1974,20240731,,81.59
-1975,20240801,,81.8
-1976,20240802,,80.46
-1977,20240805,,77.74
-1978,20240415,,91.05
-1979,20240529,,84.72
-1980,20240429,,88.22
-1981,20240430,,87.8
-1982,20240905,,74.2
-1983,20240718,,85.81
-1984,20240319,,87.7
-1985,20240624,,85.28
-1986,20240619,,85.84
-1987,20210104,,53.33
-1988,20210105,,53.9
-1989,20210106,,54.73
-1990,20210107,,54.9
-1991,20210108,,56.36
-1992,20210111,,56.39
-1993,20210112,,56.79
-1994,20210113,,57.42
-1995,20210114,,56.57
-1996,20210115,,56.5
-1997,20210118,,55.21
-1998,20210119,,56.08
-1999,20210120,,56.64
-2000,20210121,,56.24
-2001,20210122,,56.03
-2002,20210125,,56.02
-2003,20210126,,56.09
-2004,20210127,,56.19
-2005,20210128,,56.22
-2006,20210129,,56.02
-2007,20210201,,56.53
-2008,20210202,,58.05
-2009,20210203,,58.94
-2010,20210204,,59.12
-2011,20210205,,59.79
-2012,20210208,,60.72
-2013,20210209,,61.34
-2014,20210210,,61.69
-2015,20210211,,61.5
-2016,20210212,,62.83
-2017,20210215,,63.76
-2018,20210216,,63.7
-2019,20210217,,64.96
-2020,20210218,,65.52
-2021,20210219,,63.94
-2022,20210222,,64.97
-2023,20210223,,65.83
-2024,20210224,,66.47
-2025,20210225,,66.82
-2026,20210226,,66.05
-2027,20210301,,65.93
-2028,20210302,,64.13
-2029,20210303,,64.7
-2030,20210304,,67.75
-2031,20210305,,69.69
-2032,20210308,,71.38
-2033,20210309,,69.33
-2034,20210310,,68.42
-2035,20210311,,69.84
-2036,20210312,,69.9
-2037,20210315,,70.03
-2038,20210316,,68.94
-2039,20210317,,68.89
-2040,20210318,,67.76
-2041,20210319,,64.75
-2042,20210322,,64.97
-2043,20210323,,64.29
-2044,20210324,,64.39
-2045,20210325,,64.03
-2046,20210326,,64.75
-2047,20210329,,65.34
-2048,20210330,,65.39
-2049,20210331,,64.83
-2050,20210401,,65.17
-2051,20210405,,64.81
-2052,20210406,,64.28
-2053,20210407,,63.55
-2054,20210408,,63.43
-2055,20210409,,63.49
-2056,20210412,,64.31
-2057,20210413,,64.19
-2058,20210414,,66.9
-2059,20210415,,67.03
-2060,20210416,,67.38
-2061,20210419,,67.23
-2062,20210420,,68.08
-2063,20210421,,66.52
-2064,20210422,,65.83
-2065,20210423,,66.3
-2066,20210426,,66.26
-2067,20210427,,66.22
-2068,20210428,,67.37
-2069,20210429,,68.44
-2070,20210430,,67.97
-2071,20210503,,67.82
-2072,20210504,,69.5
-2073,20210505,,69.95
-2074,20210506,,69.37
-2075,20210507,,68.65
-2076,20210510,,69.2
-2077,20210511,,68.75
-2078,20210512,,69.9
-2079,20210513,,69.04
-2080,20210514,,68.89
-2081,20210517,,69.62
-2082,20210518,,70.24
-2083,20210519,,68.46
-2084,20210520,,67.17
-2085,20210521,,66.94
-2086,20210524,,68.55
-2087,20210525,,68.78
-2088,20210526,,68.95
-2089,20210527,,69.26
-2090,20210528,,69.74
-2091,20210531,,69.82
-2092,20210601,,71.34
-2093,20210602,,71.48
-2094,20210603,,71.99
-2095,20210604,,72.17
-2096,20210607,,72.27
-2097,20210608,,72.42
-2098,20210609,,72.87
-2099,20210610,,72.93
-2100,20210611,,73.09
-2101,20210614,,73.64
-2102,20210615,,74.33
-2103,20210616,,74.96
-2104,20210617,,74.5
-2105,20210618,,73.76
-2106,20210621,,74.95
-2107,20210622,,75.3
-2108,20210623,,75.26
-2109,20210624,,75.04
-2110,20210625,,75.41
-2111,20210628,,75.77
-2112,20210629,,74.99
-2113,20210630,,75.35
-2114,20210701,,76.74
-2115,20210702,,76.43
-2116,20210705,,77.26
-2117,20210706,,77.84
-2118,20210707,,75.99
-2119,20210708,,74.4
-2120,20210709,,75.8
-2121,20210712,,75.84
-2122,20210713,,76.6
-2123,20210714,,76.72
-2124,20210715,,74.59
-2125,20210716,,74.09
-2126,20210719,,73.34
-2127,20210720,,69.73
-2128,20210721,,72.38
-2129,20210722,,73.9
-2130,20210723,,74.22
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+2355,20220602,,118.46
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+2399,20220803,,102.41
+2400,20220804,,97.66
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+2405,20220811,,100.17
+2406,20220812,,100.38
+2407,20220815,,98.17
+2408,20220816,,95.95
+2409,20220817,,94.48
+2410,20220818,,97.42
+2411,20220819,,97.88
+2412,20220822,,97.28
+2413,20220823,,100.45
+2414,20220824,,101.9
+2415,20220825,,101.53
+2416,20220826,,100.1
+2417,20220829,,103.22
+2418,20220830,,103.48
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+2424,20220907,,93.8
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+2427,20220912,,95.17
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+2433,20220920,,93.05
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+2437,20220926,,86.45
+2438,20220927,,85.84
+2439,20220928,,88.22
+2440,20220929,,88.74
+2441,20220930,,88.64
+2442,20221003,,89.82
+2443,20221004,,92.4
+2444,20221005,,93.96
+2445,20221006,,94.98
+2446,20221007,,98.58
+2447,20221010,,98.75
+2448,20221011,,96.46
+2449,20221012,,95.17
+2450,20221013,,94.92
+2451,20221014,,95.11
+2452,20221017,,93.21
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+2456,20221021,,93.8
+2457,20221024,,94.4
+2458,20221025,,94.38
+2459,20221026,,94.3
+2460,20221027,,95.3
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+2462,20221031,,94.48
+2463,20221101,,95.55
+2464,20221102,,96.5
+2465,20221103,,95.98
+2466,20221104,,98.81
+2467,20221107,,99.56
+2468,20221108,,98.3
+2469,20221109,,95.65
+2470,20221110,,94.32
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+2473,20221115,,95.77
+2474,20221116,,94.79
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+2476,20221118,,90.63
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+2489,20221207,,80.49
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+2493,20221213,,81.28
+2494,20221214,,83.15
+2495,20221215,,83.18
+2496,20221216,,81.78
+2497,20221219,,80.87
+2498,20221220,,80.89
+2499,20221221,,82.92
+2500,20221222,,84.21
+2501,20221223,,84.7
+2502,20221227,,86.1
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+2514,20241004,,79.3
+2515,20241007,,81.16
+2516,20241008,,81.14
+2517,20241009,,78.02
+2518,20241010,,79.72
+2519,20240613,,83.05
+2520,20240410,,90.71
+2521,20240404,,91.3
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+2524,20240325,,86.51
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+2526,20241101,,74.94
+2527,20240710,,85.66
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+2545,20240701,,86.88
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+2551,20240614,,83.39
+2552,20240620,,85.14
+2553,20241220,,72.89
+2554,20240523,,82.97
+2555,20241202,,72.89
+2556,20240405,,91.91
+2557,20241205,,72.92
+2558,20241126,,73.8
+2559,20241106,,75.98
+2560,20241114,,73.22
+2561,20240326,,86.42
+2562,20240820,,78.35
+2563,20240814,,81.44
+2564,20240806,,77.85
+2565,20240807,,78.87
+2566,20240808,,79.37
+2567,20240426,,88.64
diff --git a/lib/dataread.py b/lib/dataread.py
index 21d9a96..4b5dbce 100644
--- a/lib/dataread.py
+++ b/lib/dataread.py
@@ -1341,6 +1341,7 @@ class EtaReader():
                         Frequency = i.get('Frequency')  # 频度，要保存到df的频度列
                         SourceName = i.get('SourceName')  # 来源名称，要保存到df的频度列
                         Source = i.get('Source')  # 来源ID，要保存到df的频度列
+                        Unit = i.get('Unit')  # 单位，要保存到df的单位列
                         # 频度不是 日 或者 周的 跳过
                         if Frequency not in ['日度','周度','日','周']:
                             continue
@@ -1390,7 +1391,18 @@ class EtaReader():
                                 stop_update_period = (datetime.datetime.strptime(today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days
 
                             # 保存频度  指标名称  分类  指标id 到 df
-                            df2 = pd.DataFrame({'指标分类': ClassifyName, '指标名称': EdbName, '指标id': EdbCode, '频度': Frequency,'指标来源':SourceName,'来源id':Source,'最后更新时间':edbname_df['DataTime'].values[-1],'更新周期':most_common_weekday,'预警日期':warning_date,'停更周期':stop_update_period},index=[0])
+                            df2 = pd.DataFrame({'指标分类': ClassifyName, 
+                                                '指标名称': EdbName, 
+                                                '指标id': EdbCode, 
+                                                '单位': Unit,
+                                                '频度': Frequency,
+                                                '指标来源':SourceName,
+                                                '来源id':Source,
+                                                '最后更新时间':edbname_df['DataTime'].values[-1],
+                                                '更新周期':most_common_weekday,
+                                                '预警日期':warning_date,
+                                                '停更周期':stop_update_period},index=[0],
+                                               )
                             
                             # df = pd.merge(df, df2, how='outer') 
                             df = pd.concat([df, df2])
diff --git a/lib/tools.py b/lib/tools.py
index 2fb3a32..fa1e2c8 100644
--- a/lib/tools.py
+++ b/lib/tools.py
@@ -549,7 +549,18 @@ def exception_logger(func):
             raise e  # 重新抛出异常
     return wrapper
 
-
+def get_week_date(end_time):
+    '''
+    获取上上周五，上周周一周二周三周四周五的日期
+    '''
+    import datetime
+    endtime = end_time
+    endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')
+    up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)
+    up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)]
+    create_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates[4:-3]]
+    ds_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates[-7:-2]]
+    return create_dates,ds_dates
 
 if __name__ == '__main__':
     print('This is a tool, not a script.')
\ No newline at end of file
diff --git a/main_yuanyou.py b/main_yuanyou.py
index 5a4f63b..eb9d602 100644
--- a/main_yuanyou.py
+++ b/main_yuanyou.py
@@ -106,12 +106,13 @@ def predict_main():
     # 保存最新日期的y值到数据库
     # 取第一行数据存储到数据库中
     first_row = df[['ds', 'y']].tail(1)
-    # 判断ds是否与ent_time 一致且 y 不为空
-    if first_row['ds'].values[0] == end_time and not np.isnan(first_row['y'].values[0]):
-        pass
-    else:
+    print(first_row['ds'].values[0])
+    print(first_row['y'].values[0])
+    # 判断y的类型是否为float
+    if not isinstance(first_row['y'].values[0], float):
         logger.info(f'{end_time}预测目标数据为空，跳过')
         return None
+
     # 将最新真实值保存到数据库
     if not sqlitedb.check_table_exists('trueandpredict'):
         first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
@@ -135,10 +136,16 @@ def predict_main():
             logger.info('更新accuracy表的y值')
             # 找到update_y 中ds且df中的y的行
             update_y = update_y[update_y['ds']<=end_time]
-            for row in update_y.itertuples(index=False):
-                row_dict = row._asdict()
-                yy = df[df['ds']==row_dict['ds']]['y'].values[0]
-                sqlitedb.update_data('accuracy', f"y = {yy}", where_condition=f"ds = '{row_dict['ds']}'")
+            logger.info(f'要更新y的信息：{update_y}')
+            try:
+                for row in update_y.itertuples(index=False):
+                    row_dict = row._asdict()  	
+                    yy = df[df['ds']==row_dict['ds']]['y'].values[0]
+                    LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
+                    HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
+                    sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
+            except Exception as e:
+                logger.info(f'更新accuracy表的y值失败：{e}')
 
     import datetime
     # 判断当前日期是不是周一
@@ -240,14 +247,14 @@ def predict_main():
     logger.info('训练数据绘图end')
     
     # 模型报告
-    # logger.info('制作报告ing')
-    # title = f'{settings}--{now}-预测报告' # 报告标题
+    logger.info('制作报告ing')
+    title = f'{settings}--{end_time}-预测报告' # 报告标题
 
-    # brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
-    #             reportname=reportname,sqlitedb=sqlitedb),
+    brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
+                reportname=reportname,sqlitedb=sqlitedb),
 
-    # logger.info('制作报告end')
-    # logger.info('模型训练完成')
+    logger.info('制作报告end')
+    logger.info('模型训练完成')
 
     # # LSTM 单变量模型
     # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
@@ -275,11 +282,6 @@ if __name__ == '__main__':
     global end_time
     is_on = True
     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    for i_time in pd.date_range('2024-12-24', '2024-12-26', freq='B'):
+    for i_time in pd.date_range('2024-12-24', '2024-12-25', freq='B'):
         end_time = i_time.strftime('%Y-%m-%d')
         predict_main()
-        if is_on:
-            is_train = False
-            is_on = False
-            is_fivemodels = True
-            is_eta = False
\ No newline at end of file
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 8608490..82f66f2 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -7,7 +7,7 @@ import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 import datetime
 from lib.tools import Graphs,mse,rmse,mae,exception_logger
-from lib.tools import save_to_database
+from lib.tools import save_to_database,get_week_date
 from lib.dataread import *
 from neuralforecast import NeuralForecast
 from neuralforecast.models import  NHITS,Informer, NBEATSx,LSTM,PatchTST, iTransformer, TSMixer
@@ -251,7 +251,7 @@ def model_losss(sqlitedb,end_time):
     df_combined = df_combined[df_combined['CREAT_DATE'] == df_combined['max_cutoff']]
     df_combined4 = df_combined.copy()  # 备份df_combined,后面画图需要
     # 删除模型生成的cutoff列
-    df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price'], inplace=True) 
+    df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE'], inplace=True) 
     # 获取模型名称
     modelnames  = df_combined.columns.to_list()[1:] 
     if 'y' in modelnames:
@@ -313,30 +313,6 @@ def model_losss(sqlitedb,end_time):
         df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')
     except ValueError :
         df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
-
-    # def first_row_to_database(df):
-    #     # # 取第一行数据存储到数据库中
-    #     first_row = df.head(1)
-    #     first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
-    #     # 将预测结果保存到数据库
-    #     if not sqlitedb.check_table_exists('trueandpredict'):
-    #         first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
-    #     else:
-    #         for col in first_row.columns:
-    #                 sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
-    #         for row in first_row.itertuples(index=False):
-    #             row_dict = row._asdict()
-    #             columns=row_dict.keys()
-    #             check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
-    #             if len(check_query) > 0:
-    #                 set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
-    #                 sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
-    #                 continue
-    #             sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
-
-    # first_row_to_database(df_predict)
-    
-    
     
     df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
 
@@ -408,7 +384,7 @@ def model_losss(sqlitedb,end_time):
 
     # 保存到数据库
     if not sqlitedb.check_table_exists('accuracy'):
-        columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE','min_price','max_price'])
+        columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean'])
         sqlitedb.create_table('accuracy',columns=columns)
     existing_data = sqlitedb.select_data(table_name = "accuracy")
 
@@ -422,7 +398,8 @@ def model_losss(sqlitedb,end_time):
     save_to_database(sqlitedb,df_predict2,"accuracy",end_time)
 
     # 上周准确率计算
-    predict_y = sqlitedb.select_data(table_name = "accuracy") 
+    accuracy_df = sqlitedb.select_data(table_name = "accuracy") 
+    predict_y = accuracy_df.copy()
     # ids = predict_y[predict_y['min_price'].isnull()]['id'].tolist()
     ids = predict_y['id'].tolist()
     # 准确率基准与绘图上下界逻辑一致
@@ -432,6 +409,7 @@ def model_losss(sqlitedb,end_time):
     # predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1
     # 模型评估前十均值      
     predict_y['min_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) -1.5
+    predict_y['mean'] = predict_y[allmodelnames[0:10]].mean(axis=1)
     predict_y['max_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) +1.5
     # 模型评估前十最大最小
     # allmodelnames 和 predict_y 列 重复的
@@ -441,17 +419,11 @@ def model_losss(sqlitedb,end_time):
     for id in ids:
         row = predict_y[predict_y['id'] == id]
         try:
-            sqlitedb.update_data('accuracy',f"min_price = {row['min_price'].values[0]},max_price = {row['max_price'].values[0]}",f"id = {id}")
+            sqlitedb.update_data('accuracy',f"min_price = {row['min_price'].values[0]},max_price = {row['max_price'].values[0]},mean={row['mean']}",f"id = {id}")
         except:
-            logger.error(f'更新accuracy表中的min_price,max_price值失败，row={row}')
+            logger.error(f'更新accuracy表中的min_price,max_price,mean值失败，row={row}')
     
-    
-    # 拼接市场最高最低价
-    xlsfilename = os.path.join(dataset,'数据项下载.xls')
-    df2 = pd.read_excel(xlsfilename)[5:]
-    df2 = df2.rename(columns = {'数据项名称':'ds','布伦特最低价':'LOW_PRICE','布伦特最高价':'HIGH_PRICE'})
-    print(df2.shape)
-    df = pd.merge(predict_y,df2,on=['ds'],how='left')
+    df = accuracy_df.copy()
     df['ds'] =  pd.to_datetime(df['ds'])
     df = df.reindex()
 
@@ -486,15 +458,6 @@ def model_losss(sqlitedb,end_time):
     df[columns] = df[columns].astype(float)
     df['ACCURACY'] = df.apply(calculate_accuracy, axis=1)
     # df['ACCURACY'] = df.apply(is_within_range, axis=1)
-    # 取结束日期上一周的日期
-    def get_week_date(end_time):
-        endtime = end_time
-        endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')
-        up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)
-        up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)]
-        create_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates[4:-3]]
-        ds_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates[-7:-2]]
-        return create_dates,ds_dates
 
     # 计算准确率并保存结果
     def _get_accuracy_rate(df,create_dates,ds_dates,endtime):
@@ -503,10 +466,6 @@ def model_losss(sqlitedb,end_time):
         df3 = df3[df3['ds'].isin(ds_dates)]
         accuracy_rote = 0
         for i,group in df3.groupby('CREAT_DATE'):
-            # print('日期：',i)
-            # print(group)
-            # print('权重：',weight_dict[len(group)-1])
-            # print('准确率：',(group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1])
             accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1]
         df3.to_csv(os.path.join(dataset,f'accuracy_{endtime}.csv'),index=False)
         df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率'])
@@ -548,8 +507,6 @@ def model_losss(sqlitedb,end_time):
      # 历史价格+预测价格
     sqlitedb.drop_table('testandpredict_groupby')
     df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
-    # 新增均值列
-    df_combined3['mean'] = df_combined3[modelnames].mean(axis=1)
 
     def _plt_predict_ture(df):
         lens = df.shape[0] if df.shape[0] < 180 else 90
@@ -557,15 +514,13 @@ def model_losss(sqlitedb,end_time):
         # 历史价格
         plt.figure(figsize=(20, 10))
         plt.plot(df['ds'], df['y'], label='真实值')
-        # 均值线
-        plt.plot(df['ds'], df['mean'], color='r', linestyle='--', label='前五模型预测均值')
         # 颜色填充
         plt.fill_between(df['ds'], df['max_within_quantile'], df['min_within_quantile'], alpha=0.2)
-        markers = ['o', 's', '^', 'D', 'v', '*', 'p', 'h', 'H', '+', 'x', 'd']
-        random_marker = random.choice(markers)
-        for model in modelnames:
+        # markers = ['o', 's', '^', 'D', 'v', '*', 'p', 'h', 'H', '+', 'x', 'd']
+        # random_marker = random.choice(markers)
+        # for model in allmodelnames:
         # for model in ['BiTCN','RNN']:
-            plt.plot(df['ds'][-horizon:], df[model][-horizon:], label=model,marker=random_marker)
+        #     plt.plot(df['ds'], df[model], label=model,marker=random_marker)
         # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')
         # 网格
         plt.grid(True)
@@ -573,15 +528,15 @@ def model_losss(sqlitedb,end_time):
         for i, j in zip(df['ds'], df['y']):
             plt.text(i, j, str(j), ha='center', va='bottom')
 
-        # for model in most_model:
-        #     plt.plot(df['ds'], df[model], label=model,marker='o')
+        for model in most_model:
+            plt.plot(df['ds'], df[model], label=model,marker='o')
         # 当前日期画竖虚线
         plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')
         plt.legend()
         plt.xlabel('日期')
         plt.ylabel('价格')
         
-        plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(dataset,f'{end_time}历史价格-预测值.png'), bbox_inches='tight')
         plt.close()
 
     
@@ -613,7 +568,7 @@ def model_losss(sqlitedb,end_time):
         plt.xlabel('日期')
         plt.ylabel('价格')
         
-        plt.savefig(os.path.join(dataset,'历史价格-预测值1.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(dataset,f'{end_time}历史价格-预测值1.png'), bbox_inches='tight')
         plt.close()
 
 
@@ -997,6 +952,9 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
     content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据，通道上界取近60个交易日内预测在上界值的模型对应的预测值，通道下界同理；'))
     content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内，最多接近真实值的模型的预测值对应的预测结果；'))
     content.append(Graphs.draw_text('6. 预测结果在通道外的，代表最接近真实值的预测结果不在置信波动范围内。'))
+    
+    # 添加历史走势及预测价格的走势图片
+    content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值1.png')))
 
 
     # 取df中y列为空的行
diff --git a/测试环境获取市场信息平台数据项.ipynb b/测试环境获取市场信息平台数据项.ipynb
index 248b05f..9d097d1 100644
--- a/测试环境获取市场信息平台数据项.ipynb
+++ b/测试环境获取市场信息平台数据项.ipynb
@@ -2,10 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 1,
    "id": "31c0e11d-c87a-4e95-92a0-d1d09625e255",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "数据库连接成功 192.168.101.27 jingbo_test root\n"
+     ]
+    }
+   ],
    "source": [
     "from config_jingbo import *\n",
     "import requests\n",
@@ -15,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 2,
    "id": "83c81b9e",
    "metadata": {},
    "outputs": [
@@ -25,7 +33,7 @@
        "'http://192.168.100.53:8080/jingbo-dev/api/server/login'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -36,22 +44,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a058f507",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "login_pushreport_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
+    "upload_url = \"http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave\"\n",
+    "upload_warning_url = \"http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save\"\n",
+    "query_data_list_item_nos_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos\"\n",
+    "\n",
+    "login_data = {\n",
+    "    \"data\": {\n",
+    "        \"account\": \"api_dev\",\n",
+    "        \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
+    "        \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
+    "        \"terminal\": \"API\"\n",
+    "    },\n",
+    "    \"funcModule\": \"API\",\n",
+    "    \"funcOperation\": \"获取token\"\n",
+    "}"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 4,
+   "id": "8af0311d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'http://10.200.32.39/jingbo-api/api/server/login'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "login_pushreport_url"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "2b330ee3-c006-4ab1-8558-59c51ac8d86f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'data': {'account': 'api_test',\n",
+       "{'data': {'account': 'api_dev',\n",
        "  'password': 'ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=',\n",
        "  'tenantHashCode': '8a4577dbd919675758d57999a1e891fe',\n",
        "  'terminal': 'API'},\n",
@@ -59,7 +104,7 @@
        " 'funcOperation': '获取token'}"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -70,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 6,
    "id": "dcb6100a-ed2b-4077-a1a9-361c6cb565f9",
    "metadata": {},
    "outputs": [],
@@ -87,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 7,
    "id": "22c0c7c4",
    "metadata": {},
    "outputs": [
@@ -95,7 +140,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzUyMzQ4MjAsImp0aSI6IjU5MWE4YWUzZGM1MjQxMWQ5Nzg5NGM5NjE1Mjc0MDZiIn0.qD81hhr7L4It2hM4K6uZlUs3iDRwebRG-FYSFWwYtkE', 'md5Token': '3b2ddd87d60b651a666ced63260655c3'}, 'status': True}\n"
+      "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTczNTMxNTg5NywianRpIjoiNDk1NGEyNDYyYThkNDc1YjkwMTMwMDRjNGNlZWY4NTcifQ.ABXdVR-X4ZOtzPJN2slCa1HwqQgmwGjDxOX4aKqaBWU', 'md5Token': 'd870019072ff52333add0545d26398a8'}, 'status': True}\n"
      ]
     }
    ],
@@ -105,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 8,
    "id": "12077ead",
    "metadata": {},
    "outputs": [],
@@ -115,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 9,
    "id": "a7ae21d1",
    "metadata": {},
    "outputs": [],
@@ -149,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 10,
    "id": "54942e1a",
    "metadata": {},
    "outputs": [
@@ -157,7 +202,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      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+      "<!DOCTYPE HTML PUBLIC \"-//IETF//DTD HTML 2.0//EN\">\n",
+      "<html>\n",
+      "<head><title>404 Not Found</title></head>\n",
+      "<body bgcolor=\"white\">\n",
+      "<h1>404 Not Found</h1>\n",
+      "<p>The requested URL was not found on this server. Sorry for the inconvenience.<br/>\n",
+      "Please report this message and include the following information to us.<br/>\n",
+      "Thank you very much!</p>\n",
+      "<table>\n",
+      "<tr>\n",
+      "<td>URL:</td>\n",
+      "<td>http://10.200.32.39/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos</td>\n",
+      "</tr>\n",
+      "<tr>\n",
+      "<td>Server:</td>\n",
+      "<td>iz2zeb0rw4fi7a0b3dh8jfz</td>\n",
+      "</tr>\n",
+      "<tr>\n",
+      "<td>Date:</td>\n",
+      "<td>2024/12/27 14:11:26</td>\n",
+      "</tr>\n",
+      "</table>\n",
+      "<hr/>Powered by Tengine</body>\n",
+      "</html>\n",
+      "\n"
      ]
     }
    ],
@@ -179,28 +248,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 11,
    "id": "5272c84b",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      dataDate  Brentzdj  Brentzgj\n",
-      "0     20240709     84.53       NaN\n",
-      "1     20241224     72.46       NaN\n",
-      "2     20200102     65.72       NaN\n",
-      "3     20200103     66.22       NaN\n",
-      "4     20200106     68.21       NaN\n",
-      "...        ...       ...       ...\n",
-      "2561  20240814       NaN     81.44\n",
-      "2562  20240806       NaN     77.85\n",
-      "2563  20240807       NaN     78.87\n",
-      "2564  20240808       NaN     79.37\n",
-      "2565  20240426       NaN     88.64\n",
-      "\n",
-      "[2566 rows x 3 columns]\n"
+     "ename": "JSONDecodeError",
+     "evalue": "Expecting value: line 1 column 1 (char 0)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[11], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjson\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m json_data \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(items_res\u001b[38;5;241m.\u001b[39mtext)\n\u001b[0;32m      5\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(json_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe\u001b[39;00m\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\json\\__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[1;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[0;32m    341\u001b[0m     s \u001b[38;5;241m=\u001b[39m s\u001b[38;5;241m.\u001b[39mdecode(detect_encoding(s), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msurrogatepass\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    344\u001b[0m         parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    345\u001b[0m         parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[1;32m--> 346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _default_decoder\u001b[38;5;241m.\u001b[39mdecode(s)\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONDecoder\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\json\\decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[1;34m(self, s, _w)\u001b[0m\n\u001b[0;32m    332\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m, s, _w\u001b[38;5;241m=\u001b[39mWHITESPACE\u001b[38;5;241m.\u001b[39mmatch):\n\u001b[0;32m    333\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[0;32m    334\u001b[0m \u001b[38;5;124;03m    containing a JSON document).\u001b[39;00m\n\u001b[0;32m    335\u001b[0m \n\u001b[0;32m    336\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 337\u001b[0m     obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw_decode(s, idx\u001b[38;5;241m=\u001b[39m_w(s, \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mend())\n\u001b[0;32m    338\u001b[0m     end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n\u001b[0;32m    339\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(s):\n",
+      "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\json\\decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[1;34m(self, s, idx)\u001b[0m\n\u001b[0;32m    353\u001b[0m     obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscan_once(s, idx)\n\u001b[0;32m    354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 355\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpecting value\u001b[39m\u001b[38;5;124m\"\u001b[39m, s, err\u001b[38;5;241m.\u001b[39mvalue) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj, end\n",
+      "\u001b[1;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
      ]
     }
    ],