From d4187e8c1ee4f26559c0dfbc3c5c1d66bcefac74 Mon Sep 17 00:00:00 2001
From: workpc <liurui_test@163.com>
Date: Mon, 10 Mar 2025 17:00:07 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B6=B2=E5=8C=96=E7=9F=B3=E6=B2=B9=E6=B0=94?=
 =?UTF-8?q?=E6=95=B0=E6=8D=AE=E6=9B=B4=E6=96=B0=E5=88=B02.28=EF=BC=9B?=
 =?UTF-8?q?=E8=81=9A=E7=83=AF=E7=83=83=E6=95=B0=E6=8D=AE=E8=8E=B7=E5=8F=96?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../液化石油气/液化气价格预测ytj.ipynb        | 1237 +----------------
 aisenzhecode/液化石油气/液化气数据.xls        |  Bin 186880 -> 186880 bytes
 config_jingbo_yuedu.py                        |    2 +-
 config_juxiting.py                            |  329 +++--
 lib/dataread.py                               |   14 +-
 main_juxiting.py                              |  350 +++--
 main_yuanyou_yuedu.py                         |   17 +-
 7 files changed, 428 insertions(+), 1521 deletions(-)

diff --git a/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb b/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb
index 02a48b9..c18db50 100644
--- a/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb
+++ b/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb
@@ -2,17 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From C:\\Users\\EDY\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -555,8 +547,8 @@
     "        else:\n",
     "            append_rows.append(\"\")\n",
     "    save_xls(append_rows)\n",
-    "    optimize_Model()\n",
-    "    upload_data_to_system(token_push,cur_time)\n",
+    "    # optimize_Model()\n",
+    "    # upload_data_to_system(token_push,cur_time)\n",
     "    \n",
     "    \n",
     "    \n",
@@ -724,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
@@ -770,1213 +762,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20250204 <class 'str'>\n",
-      "2025-02-04 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250204', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250204', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8905.0}, {'dataDate': '20250204', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250204', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250204', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250204', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5000.0}, {'dataDate': '20250204', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 4900.0}, {'dataDate': '20250204', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4690.0}, {'dataDate': '20250204', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5050.0}, {'dataDate': '20250204', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250204', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250204', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250204', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5608.0}, {'dataDate': '20250204', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250204', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5605.0}, {'dataDate': '20250204', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250204', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5400.0}, {'dataDate': '20250204', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 105.9453}, {'dataDate': '20250204', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250204', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250204', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -150.0}, {'dataDate': '20250204', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250204', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250204', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 6900.0}]\n",
-      "{'dataDate': '20250204', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250204', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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: <object object at 0x000001C1300CF310>\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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:162: UserWarning:\n",
-      "\n",
-      "pylab import has clobbered these variables: ['__version__', 'random', 'plot', 'datetime']\n",
-      "`%matplotlib` prevents importing * from pylab and numpy\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-04    5410.651855\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250205 <class 'str'>\n",
-      "2025-02-05 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250205', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250205', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8956.0}, {'dataDate': '20250205', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250205', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5140.0}, {'dataDate': '20250205', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250205', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250205', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5150.0}, {'dataDate': '20250205', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5050.0}, {'dataDate': '20250205', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4790.0}, {'dataDate': '20250205', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250205', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5680.0}, {'dataDate': '20250205', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5530.0}, {'dataDate': '20250205', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250205', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5638.0}, {'dataDate': '20250205', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250205', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5635.0}, {'dataDate': '20250205', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250205', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250205', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 81.2796}, {'dataDate': '20250205', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250205', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250205', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -150.0}, {'dataDate': '20250205', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250205', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5050.0}, {'dataDate': '20250205', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250205', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8800.0}, {'dataDate': '20250205', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6200.0}]\n",
-      "{'dataDate': '20250205', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250205', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-05    5494.333496\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250206 <class 'str'>\n",
-      "2025-02-06 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250206', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250206', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8956.0}, {'dataDate': '20250206', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5550.0}, {'dataDate': '20250206', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5160.0}, {'dataDate': '20250206', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250206', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250206', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250206', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5200.0}, {'dataDate': '20250206', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4870.0}, {'dataDate': '20250206', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250206', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5700.0}, {'dataDate': '20250206', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5630.0}, {'dataDate': '20250206', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250206', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5678.0}, {'dataDate': '20250206', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250206', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5655.0}, {'dataDate': '20250206', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250206', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250206', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 152.4238}, {'dataDate': '20250206', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250206', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 0.0}, {'dataDate': '20250206', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -150.0}, {'dataDate': '20250206', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250206', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5070.0}, {'dataDate': '20250206', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250206', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8800.0}, {'dataDate': '20250206', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6200.0}]\n",
-      "{'dataDate': '20250206', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-06    5552.835938\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250207 <class 'str'>\n",
-      "2025-02-07 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250207', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250207', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8921.0}, {'dataDate': '20250207', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5550.0}, {'dataDate': '20250207', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5160.0}, {'dataDate': '20250207', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250207', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250207', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250207', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250207', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4870.0}, {'dataDate': '20250207', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250207', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5700.0}, {'dataDate': '20250207', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5630.0}, {'dataDate': '20250207', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250207', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5678.0}, {'dataDate': '20250207', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250207', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5655.0}, {'dataDate': '20250207', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250207', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5550.0}, {'dataDate': '20250207', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': -7.6913}, {'dataDate': '20250207', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250207', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250207', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -150.0}, {'dataDate': '20250207', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250207', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5110.0}, {'dataDate': '20250207', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250207', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8700.0}, {'dataDate': '20250207', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6150.0}]\n",
-      "{'dataDate': '20250207', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250207', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-07    5574.327637\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250208 <class 'str'>\n",
-      "2025-02-08 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250208', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250208', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8866.0}, {'dataDate': '20250208', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250208', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5220.0}, {'dataDate': '20250208', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250208', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE', 'dataValue': 0.0}, {'dataDate': '20250208', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250208', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250208', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4870.0}, {'dataDate': '20250208', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250208', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250208', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250208', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250208', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5578.0}, {'dataDate': '20250208', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250208', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5555.0}, {'dataDate': '20250208', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250208', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5450.0}, {'dataDate': '20250208', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 153.4345}, {'dataDate': '20250208', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250208', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 0.0}, {'dataDate': '20250208', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250208', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250208', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5130.0}, {'dataDate': '20250208', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250208', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8700.0}, {'dataDate': '20250208', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6100.0}]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-08    5400.607422\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250209 <class 'str'>\n",
-      "2025-02-09 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250209', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250209', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8875.0}, {'dataDate': '20250209', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250209', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250209', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250209', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250209', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250209', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4870.0}, {'dataDate': '20250209', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250209', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5550.0}, {'dataDate': '20250209', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5470.0}, {'dataDate': '20250209', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250209', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5558.0}, {'dataDate': '20250209', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250209', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5555.0}, {'dataDate': '20250209', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250209', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250209', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 231.5606}, {'dataDate': '20250209', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250209', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250209', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250209', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250209', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250209', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 7000.0}]\n",
-      "{'dataDate': '20250209', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250209', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-09    5392.519043\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250210 <class 'str'>\n",
-      "2025-02-10 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250210', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250210', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8875.0}, {'dataDate': '20250210', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250210', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250210', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250210', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250210', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250210', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250210', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4820.0}, {'dataDate': '20250210', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250210', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5550.0}, {'dataDate': '20250210', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5470.0}, {'dataDate': '20250210', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250210', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5558.0}, {'dataDate': '20250210', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250210', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5555.0}, {'dataDate': '20250210', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250210', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250210', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 236.8478}, {'dataDate': '20250210', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250210', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250210', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250210', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250210', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5160.0}, {'dataDate': '20250210', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250210', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8600.0}, {'dataDate': '20250210', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6000.0}]\n",
-      "{'dataDate': '20250210', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250210', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-10    5405.626465\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250211 <class 'str'>\n",
-      "2025-02-11 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250211', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250211', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8875.0}, {'dataDate': '20250211', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5420.0}, {'dataDate': '20250211', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5280.0}, {'dataDate': '20250211', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250211', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250211', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250211', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5200.0}, {'dataDate': '20250211', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4820.0}, {'dataDate': '20250211', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250211', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5650.0}, {'dataDate': '20250211', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5490.0}, {'dataDate': '20250211', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250211', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5608.0}, {'dataDate': '20250211', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250211', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5605.0}, {'dataDate': '20250211', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5420.0}, {'dataDate': '20250211', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5420.0}, {'dataDate': '20250211', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 263.0919}, {'dataDate': '20250211', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250211', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250211', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250211', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250211', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5150.0}, {'dataDate': '20250211', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250211', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8550.0}, {'dataDate': '20250211', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5700.0}]\n",
-      "{'dataDate': '20250211', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250211', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-11    5423.526855\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250212 <class 'str'>\n",
-      "2025-02-12 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250212', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250212', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8875.0}, {'dataDate': '20250212', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5480.0}, {'dataDate': '20250212', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5280.0}, {'dataDate': '20250212', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250212', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250212', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250212', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5200.0}, {'dataDate': '20250212', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4720.0}, {'dataDate': '20250212', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250212', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5750.0}, {'dataDate': '20250212', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5550.0}, {'dataDate': '20250212', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250212', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5708.0}, {'dataDate': '20250212', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250212', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5705.0}, {'dataDate': '20250212', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250212', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250212', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 332.637}, {'dataDate': '20250212', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250212', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250212', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250212', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250212', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5100.0}, {'dataDate': '20250212', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250212', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8600.0}, {'dataDate': '20250212', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5675.0}]\n",
-      "{'dataDate': '20250212', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250212', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-12    5480.074707\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250213 <class 'str'>\n",
-      "2025-02-13 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250213', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250213', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8793.0}, {'dataDate': '20250213', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5420.0}, {'dataDate': '20250213', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5280.0}, {'dataDate': '20250213', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250213', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250213', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250213', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250213', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4720.0}, {'dataDate': '20250213', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250213', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5650.0}, {'dataDate': '20250213', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5470.0}, {'dataDate': '20250213', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250213', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5658.0}, {'dataDate': '20250213', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250213', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5625.0}, {'dataDate': '20250213', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250213', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5420.0}, {'dataDate': '20250213', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 229.5496}, {'dataDate': '20250213', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250213', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250213', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250213', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250213', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5080.0}, {'dataDate': '20250213', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250213', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8400.0}, {'dataDate': '20250213', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5800.0}]\n",
-      "{'dataDate': '20250213', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250213', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-13    5421.194336\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250214 <class 'str'>\n",
-      "2025-02-14 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250214', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250214', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8722.0}, {'dataDate': '20250214', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5370.0}, {'dataDate': '20250214', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5270.0}, {'dataDate': '20250214', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250214', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250214', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250214', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250214', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4720.0}, {'dataDate': '20250214', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250214', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5550.0}, {'dataDate': '20250214', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250214', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250214', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5608.0}, {'dataDate': '20250214', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250214', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5555.0}, {'dataDate': '20250214', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250214', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5370.0}, {'dataDate': '20250214', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 322.7353}, {'dataDate': '20250214', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250214', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250214', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250214', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250214', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5090.0}, {'dataDate': '20250214', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250214', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8300.0}, {'dataDate': '20250214', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5800.0}]\n",
-      "{'dataDate': '20250214', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250214', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-14    5389.237305\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250215 <class 'str'>\n",
-      "2025-02-15 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250215', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250215', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8722.0}, {'dataDate': '20250215', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250215', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250215', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250215', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250215', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250215', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4720.0}, {'dataDate': '20250215', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250215', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250215', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250215', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250215', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5538.0}, {'dataDate': '20250215', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250215', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5505.0}, {'dataDate': '20250215', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250215', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5300.0}, {'dataDate': '20250215', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 461.3501}, {'dataDate': '20250215', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250215', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250215', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250215', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250215', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250215', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 6850.0}]\n",
-      "{'dataDate': '20250215', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250215', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-15    5337.031738\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250216 <class 'str'>\n",
-      "2025-02-16 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250216', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250216', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8674.0}, {'dataDate': '20250216', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250216', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250216', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250216', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4720.0}, {'dataDate': '20250216', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250216', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250216', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5380.0}, {'dataDate': '20250216', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5538.0}, {'dataDate': '20250216', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5505.0}, {'dataDate': '20250216', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5300.0}, {'dataDate': '20250216', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 786.1007}, {'dataDate': '20250216', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-16    5371.679688\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250217 <class 'str'>\n",
-      "2025-02-17 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250217', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250217', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8674.0}, {'dataDate': '20250217', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250217', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5270.0}, {'dataDate': '20250217', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250217', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250217', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250217', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5200.0}, {'dataDate': '20250217', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4720.0}, {'dataDate': '20250217', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250217', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5520.0}, {'dataDate': '20250217', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250217', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250217', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5538.0}, {'dataDate': '20250217', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250217', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5505.0}, {'dataDate': '20250217', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250217', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5300.0}, {'dataDate': '20250217', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1158.2119}, {'dataDate': '20250217', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250217', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250217', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250217', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250217', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5180.0}, {'dataDate': '20250217', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250217', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8300.0}, {'dataDate': '20250217', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5800.0}]\n",
-      "{'dataDate': '20250217', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250217', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-17    5310.317383\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250218 <class 'str'>\n",
-      "2025-02-18 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250218', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250218', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8674.0}, {'dataDate': '20250218', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5480.0}, {'dataDate': '20250218', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5270.0}, {'dataDate': '20250218', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250218', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250218', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250218', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5200.0}, {'dataDate': '20250218', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4690.0}, {'dataDate': '20250218', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250218', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5630.0}, {'dataDate': '20250218', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5550.0}, {'dataDate': '20250218', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250218', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5588.0}, {'dataDate': '20250218', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250218', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5585.0}, {'dataDate': '20250218', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250218', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250218', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1308.9073}, {'dataDate': '20250218', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250218', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250218', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250218', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250218', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5220.0}, {'dataDate': '20250218', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250218', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8400.0}, {'dataDate': '20250218', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5775.0}]\n",
-      "{'dataDate': '20250218', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250218', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-18    5466.825195\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250219 <class 'str'>\n",
-      "2025-02-19 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250219', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250219', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8674.0}, {'dataDate': '20250219', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250219', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250219', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250219', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250219', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250219', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4690.0}, {'dataDate': '20250219', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250219', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5700.0}, {'dataDate': '20250219', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5650.0}, {'dataDate': '20250219', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250219', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5668.0}, {'dataDate': '20250219', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250219', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5665.0}, {'dataDate': '20250219', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250219', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5550.0}, {'dataDate': '20250219', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1600.0196}, {'dataDate': '20250219', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250219', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250219', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250219', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250219', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5220.0}, {'dataDate': '20250219', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250219', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8400.0}, {'dataDate': '20250219', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5750.0}]\n",
-      "{'dataDate': '20250219', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250219', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-19    5479.943848\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250220 <class 'str'>\n",
-      "2025-02-20 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250220', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250220', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8674.0}, {'dataDate': '20250220', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5340.0}, {'dataDate': '20250220', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250220', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250220', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250220', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250220', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4770.0}, {'dataDate': '20250220', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250220', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5700.0}, {'dataDate': '20250220', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5650.0}, {'dataDate': '20250220', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250220', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5668.0}, {'dataDate': '20250220', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250220', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5665.0}, {'dataDate': '20250220', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250220', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5550.0}, {'dataDate': '20250220', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1471.9223}, {'dataDate': '20250220', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250220', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250220', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250220', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250220', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5230.0}, {'dataDate': '20250220', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250220', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8500.0}, {'dataDate': '20250220', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5750.0}]\n",
-      "{'dataDate': '20250220', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250220', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-20    5478.131348\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250221 <class 'str'>\n",
-      "2025-02-21 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250221', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250221', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8674.0}, {'dataDate': '20250221', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5340.0}, {'dataDate': '20250221', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250221', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250221', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250221', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250221', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4770.0}, {'dataDate': '20250221', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250221', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5700.0}, {'dataDate': '20250221', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5650.0}, {'dataDate': '20250221', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250221', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5668.0}, {'dataDate': '20250221', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250221', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5665.0}, {'dataDate': '20250221', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250221', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250221', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1313.0783}, {'dataDate': '20250221', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250221', 'dataItemNo': 'C01100008|PLAN_SALE'}, {'dataDate': '20250221', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250221', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250221', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5240.0}, {'dataDate': '20250221', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250221', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8400.0}, {'dataDate': '20250221', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5875.0}]\n",
-      "{'dataDate': '20250221', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20250221', 'dataItemNo': 'C01100008|PLAN_SALE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-21    5482.609375\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250222 <class 'str'>\n",
-      "2025-02-22 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250222', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250222', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8593.0}, {'dataDate': '20250222', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250222', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE', 'dataValue': 0.0}, {'dataDate': '20250222', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250222', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250222', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4770.0}, {'dataDate': '20250222', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250222', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250222', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5550.0}, {'dataDate': '20250222', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250222', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5618.0}, {'dataDate': '20250222', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250222', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5605.0}, {'dataDate': '20250222', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250222', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250222', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1043.6716}, {'dataDate': '20250222', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250222', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 700.0}, {'dataDate': '20250222', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250222', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250222', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250222', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 6850.0}]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-22    5422.866211\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250223 <class 'str'>\n",
-      "2025-02-23 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250223', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250223', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8513.0}, {'dataDate': '20250223', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250223', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250223', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5450.0}, {'dataDate': '20250223', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250223', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4770.0}, {'dataDate': '20250223', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250223', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250223', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5550.0}, {'dataDate': '20250223', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250223', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5568.0}, {'dataDate': '20250223', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250223', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5555.0}, {'dataDate': '20250223', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250223', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5400.0}, {'dataDate': '20250223', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1127.7162}, {'dataDate': '20250223', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250223', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 400.0}, {'dataDate': '20250223', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250223', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250223', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250223', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 6850.0}]\n",
-      "{'dataDate': '20250223', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-23    5481.371094\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "20250224 <class 'str'>\n",
-      "2025-02-24 00:00:00 <class 'datetime.datetime'>\n",
-      "{'data': {'date': '20250224', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
-      "[{'dataDate': '20250224', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8513.0}, {'dataDate': '20250224', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250224', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250224', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250224', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250224', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250224', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4670.0}, {'dataDate': '20250224', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250224', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250224', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250224', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250224', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5488.0}, {'dataDate': '20250224', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250224', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5455.0}, {'dataDate': '20250224', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250224', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5400.0}, {'dataDate': '20250224', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1218.5774}, {'dataDate': '20250224', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250224', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 400.0}, {'dataDate': '20250224', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250224', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250224', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5200.0}, {'dataDate': '20250224', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250224', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8300.0}, {'dataDate': '20250224', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 5900.0}]\n",
-      "{'dataDate': '20250224', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:293: 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_10368\\3326068263.py:295: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10368\\3326068263.py:241: 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_10368\\3326068263.py:243: 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_10368\\3326068263.py:275: 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": [
-      "Date\n",
-      "2025-02-24    5480.000488\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "[5410.65, 5494.33, 5552.84, 5574.33, 5400.61, 5392.52, 5405.63, 5423.53, 5480.07, 5421.19, 5389.24, 5337.03, 5371.68, 5310.32, 5466.83, 5479.94, 5478.13, 5482.61, 5422.87, 5481.37, 5480.0]\n"
+      "20250225 <class 'str'>\n",
+      "2025-02-25 00:00:00 <class 'datetime.datetime'>\n",
+      "{'data': {'date': '20250225', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
+      "[{'dataDate': '20250225', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8513.0}, {'dataDate': '20250225', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250225', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250225', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250225', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250225', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250225', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4670.0}, {'dataDate': '20250225', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250225', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250225', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250225', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250225', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5488.0}, {'dataDate': '20250225', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250225', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5455.0}, {'dataDate': '20250225', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250225', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5400.0}, {'dataDate': '20250225', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1102.7538}, {'dataDate': '20250225', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250225', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 400.0}, {'dataDate': '20250225', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250225', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250225', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5210.0}, {'dataDate': '20250225', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250225', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8350.0}, {'dataDate': '20250225', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6000.0}]\n",
+      "{'dataDate': '20250225', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
+      "20250226 <class 'str'>\n",
+      "2025-02-26 00:00:00 <class 'datetime.datetime'>\n",
+      "{'data': {'date': '20250226', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
+      "[{'dataDate': '20250226', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8513.0}, {'dataDate': '20250226', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250226', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250226', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250226', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250226', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250226', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4670.0}, {'dataDate': '20250226', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250226', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250226', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250226', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250226', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5488.0}, {'dataDate': '20250226', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250226', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5455.0}, {'dataDate': '20250226', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250226', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5400.0}, {'dataDate': '20250226', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 973.1879}, {'dataDate': '20250226', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250226', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 400.0}, {'dataDate': '20250226', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250226', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250226', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250226', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250226', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8300.0}, {'dataDate': '20250226', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6000.0}]\n",
+      "{'dataDate': '20250226', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
+      "20250227 <class 'str'>\n",
+      "2025-02-27 00:00:00 <class 'datetime.datetime'>\n",
+      "{'data': {'date': '20250227', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
+      "[{'dataDate': '20250227', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8441.0}, {'dataDate': '20250227', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5340.0}, {'dataDate': '20250227', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250227', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250227', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5350.0}, {'dataDate': '20250227', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250227', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4670.0}, {'dataDate': '20250227', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250227', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250227', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250227', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250227', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5488.0}, {'dataDate': '20250227', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250227', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5455.0}, {'dataDate': '20250227', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250227', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5480.0}, {'dataDate': '20250227', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 698.2293}, {'dataDate': '20250227', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250227', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 600.0}, {'dataDate': '20250227', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250227', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250227', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250227', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250227', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8300.0}, {'dataDate': '20250227', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6000.0}]\n",
+      "{'dataDate': '20250227', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
+      "20250228 <class 'str'>\n",
+      "2025-02-28 00:00:00 <class 'datetime.datetime'>\n",
+      "{'data': {'date': '20250228', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
+      "[{'dataDate': '20250228', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8441.0}, {'dataDate': '20250228', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5380.0}, {'dataDate': '20250228', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250228', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250228', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5150.0}, {'dataDate': '20250228', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5150.0}, {'dataDate': '20250228', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4670.0}, {'dataDate': '20250228', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250228', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250228', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250228', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250228', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5488.0}, {'dataDate': '20250228', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250228', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5455.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': -550.2}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 500.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250228', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8400.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6050.0}]\n",
+      "{'dataDate': '20250228', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
+      "[]\n"
      ]
     }
    ],
    "source": [
-    "start_date = datetime(2025, 2, 4)\n",
-    "end_date = datetime(2025, 2, 25)\n",
+    "start_date = datetime(2025, 2, 25)\n",
+    "end_date = datetime(2025, 3, 1)\n",
     "\n",
     "while start_date < end_date:\n",
     "    date = start_date.strftime('%Y%m%d')\n",
diff --git a/aisenzhecode/液化石油气/液化气数据.xls b/aisenzhecode/液化石油气/液化气数据.xls
index cf91e1c86307a804e2189b4002cc67cf17fa889a..9f177cd7be1a096851d5e7f1534a7bfa42f4d39d 100644
GIT binary patch
delta 1032
zcma*lO=uHA6ae72FU{tsVBJ_X7E&!(R7@pVNELrTibC*aqlX?8QHnqGB)#ZmXC?$u
zj1Y2Is5eo>i-;**qeMk8Dqc)L5WGr3j~<H>-^~7G3!%7&$Go?1X7<||wmi(1ho9}m
zL<F>xa02F_k;e;>(Z~CM9vw<o=I0ml71#-vKGh#ZC!#F`c-i<I&Fs>ejh~aZqj@uD
z7N&CMlvzju1j%$bIT%h3*_}3?syFwK#mcl8z_@+%12)Rl?5%26bE;W9u%yRvGgw%k
zc(Nr;JqD~$)KI6IMp+9|VA)^Z<JN+OLsT#zO2st2)@iI)<dhj*#*1eF;@;E*<t><|
zsbg)-?74PYHY}B7e0NU9*-;tYeNr@P!ES1195ePv6O)%MOjC9B>Ew^dk=k+f7I#o(
zt=L~FIV;C<TLW$ifDA|rqwgPwp5zi_1pY!&)eLYv84G00m9cd)wt<YHFJrFE{f5CZ
zhQ5sTWfoJix$RDeUA?DdY(v>|?#uW9nXDP`!jthp#u2Ih;H>|LBJi621IHng%KjO0
zC0#A6ri9<>o*@sUJS@FF@C<n%<sm6o(vPuY_rG>3O3MFJDh5bp&48vS6@gSJ>2D>K
z(UpqzQW27NV{)M`T^?5|0;vc~Q|eybIWL?0w^ex3A_yhjbhy{+8BluohvHh%ezTx$
e!<n+<wDFa`(!~Wm{`SVNT&?z9a~8khUF{!{(7@jS

delta 122
zcmZqJ!ricidxHiOi(h_T>SRNv^vMoPtea;r{bUj;)?@$zHiqDg)YKBgRt9;7t4Ag;
zWmRO|$;800ZS!eXSp~+{%`a6uS*ItRW^$kWR$Gd(zL`U(okNFlJBJR_^I6+HPcv<2
a+rHrpQy<H=OH4Uz(}7Hm?IkyudKdw83@Pyd

diff --git a/config_jingbo_yuedu.py b/config_jingbo_yuedu.py
index 1cdcfbe..65ba324 100644
--- a/config_jingbo_yuedu.py
+++ b/config_jingbo_yuedu.py
@@ -159,7 +159,7 @@ 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 = False  # 是否使用eta接口
+is_eta = True  # 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/config_juxiting.py b/config_juxiting.py
index a08c9f5..6054a38 100644
--- a/config_juxiting.py
+++ b/config_juxiting.py
@@ -2,7 +2,7 @@ import logging
 import os
 import logging.handlers
 import datetime
-from lib.tools import MySQLDB,SQLiteHandler
+from lib.tools import MySQLDB, SQLiteHandler
 
 
 # eta 接口token
@@ -10,35 +10,35 @@ APPID = "XNLDvxZHHugj7wJ7"
 SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
 
 # eta 接口url
-sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'  
+sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
 classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
 uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
 classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
 edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
-edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push' 
-edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'  
-edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'  
-edbcodelist = ['ID01385938','lmcads03 lme comdty',
-'GC1 COMB Comdty',
-'C2404171822',
-'dxy curncy',
-'S5443199 ',
-'S5479800',
-'S5443108',
-'H7358586',
-'LC3FM1 INDEX',
-'CNY REGN Curncy',
-'s0105897',
-'M0067419',
-'M0066351',
-'S0266372',
-'S0266438',
-'S0266506',
-'ID01384463']
+edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
+edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
+edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
+edbcodelist = ['ID01385938', 'lmcads03 lme comdty',
+               'GC1 COMB Comdty',
+               'C2404171822',
+               'dxy curncy',
+               'S5443199 ',
+               'S5479800',
+               'S5443108',
+               'H7358586',
+               'LC3FM1 INDEX',
+               'CNY REGN Curncy',
+               's0105897',
+               'M0067419',
+               'M0066351',
+               'S0266372',
+               'S0266438',
+               'S0266506',
+               'ID01384463']
 
 # 临时写死用指定的列,与上面的edbcode对应,后面更改
 edbnamelist = [
-    'ds','y',
+    'ds', 'y',
     'LME铜价',
     '黄金连1合约',
     'Brent-WTI',
@@ -52,66 +52,65 @@ edbnamelist = [
     '南华工业品指数',
     'PVC期货主力',
     'PE期货收盘价',
-'PP连续-1月',
-'PP连续-5月',
-'PP连续-9月',
-'PP:拉丝:L5E89:出厂价:华北(第二区域):内蒙古久泰新材料(日)'
-    ]
+    'PP连续-1月',
+    'PP连续-5月',
+    'PP连续-9月',
+    'PP:拉丝:L5E89:出厂价:华北(第二区域):内蒙古久泰新材料(日)'
+]
 
 edbcodenamedict = {
-'ID01385938':'PP:拉丝:1102K:市场价:青州:国家能源宁煤(日)',
-'ID01384463':'PP:拉丝:L5E89:出厂价:华北(第二区域):内蒙古久泰新材料(日)',
-'lmcads03 lme comdty':'LME铜价',
-'GC1 COMB Comdty':'黄金连1合约',
-'C2404171822':'Brent-WTI',
-'dxy curncy':'美元指数',
-'S5443199 ':'甲醇鲁南价格',
-'S5479800':'甲醇太仓港口价格',
-'S5443108':'山东丙烯主流价',
-'H7358586':'丙烷(山东)',
-'LC3FM1 INDEX':'FEI丙烷 M1',
-'CNY REGN Curncy':'在岸人民币汇率',
-'s0105897':'南华工业品指数',
-'M0067419':'PVC期货主力',
-'M0066351':'PE期货收盘价',
-'S0266372':'PP连续-1月',
-'S0266438':'PP连续-5月',
-'S0266506':'PP连续-9月',
+    'ID01385938': 'PP:拉丝:1102K:市场价:青州:国家能源宁煤(日)',
+    'ID01384463': 'PP:拉丝:L5E89:出厂价:华北(第二区域):内蒙古久泰新材料(日)',
+    'lmcads03 lme comdty': 'LME铜价',
+    'GC1 COMB Comdty': '黄金连1合约',
+    'C2404171822': 'Brent-WTI',
+    'dxy curncy': '美元指数',
+    'S5443199 ': '甲醇鲁南价格',
+    'S5479800': '甲醇太仓港口价格',
+    'S5443108': '山东丙烯主流价',
+    'H7358586': '丙烷(山东)',
+    'LC3FM1 INDEX': 'FEI丙烷 M1',
+    'CNY REGN Curncy': '在岸人民币汇率',
+    's0105897': '南华工业品指数',
+    'M0067419': 'PVC期货主力',
+    'M0066351': 'PE期货收盘价',
+    'S0266372': 'PP连续-1月',
+    'S0266438': 'PP连续-5月',
+    'S0266506': 'PP连续-9月',
 
 }
 
 # eta自有数据指标编码
 modelsindex = {
-        'NHITS': 'SELF0000077',
-        'Informer':'SELF0000078',
-        'LSTM':'SELF0000079',
-        'iTransformer':'SELF0000080',
-        'TSMixer':'SELF0000081',
-        'TSMixerx':'SELF0000082',
-        'PatchTST':'SELF0000083',
-        'RNN':'SELF0000084',
-        'GRU':'SELF0000085',
-        'TCN':'SELF0000086',
-        'BiTCN':'SELF0000087',
-        'DilatedRNN':'SELF0000088',
-        'MLP':'SELF0000089',
-        'DLinear':'SELF0000090',
-        'NLinear':'SELF0000091',
-        'TFT':'SELF0000092',
-        'FEDformer':'SELF0000093',
-        'StemGNN':'SELF0000094',
-        'MLPMultivariate':'SELF0000095',
-        'TiDE':'SELF0000096',
-        'DeepNPTS':'SELF0000097'
-    }
-
+    'NHITS': 'SELF0000077',
+    'Informer': 'SELF0000078',
+    'LSTM': 'SELF0000079',
+    'iTransformer': 'SELF0000080',
+    'TSMixer': 'SELF0000081',
+    'TSMixerx': 'SELF0000082',
+    'PatchTST': 'SELF0000083',
+    'RNN': 'SELF0000084',
+    'GRU': 'SELF0000085',
+    'TCN': 'SELF0000086',
+    'BiTCN': 'SELF0000087',
+    'DilatedRNN': 'SELF0000088',
+    'MLP': 'SELF0000089',
+    'DLinear': 'SELF0000090',
+    'NLinear': 'SELF0000091',
+    'TFT': 'SELF0000092',
+    'FEDformer': 'SELF0000093',
+    'StemGNN': 'SELF0000094',
+    'MLPMultivariate': 'SELF0000095',
+    'TiDE': 'SELF0000096',
+    'DeepNPTS': 'SELF0000097'
+}
 
 
 # eta 上传预测结果的请求体,后面发起请求的时候更改 model datalist  数据
 data = {
-            "IndexCode": "",
-            "IndexName": "价格预测模型",
-            "Unit": "无",
+    "IndexCode": "",
+    "IndexName": "价格预测模型",
+    "Unit": "无",
             "Frequency": "日度",
             "SourceName": f"价格预测",
             "Remark": 'ddd',
@@ -121,19 +120,18 @@ data = {
                     "Value": 333444
                 }
             ]
-        }
+}
 
 # eta 分类
 # level:3才可以获取到数据,所以需要人工把能源化工下所有的level3级都找到
-        # url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
-        #ParentId ":1160, 能源化工
-        # ClassifyId ":1214,原油
-        #ParentId ":1214,",就是原油下所有的数据。
-ClassifyId = 1161  
+# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
+# ParentId ":1160, 能源化工
+# ClassifyId ":1214,原油
+# ParentId ":1214,",就是原油下所有的数据。
+ClassifyId = 1161
 
 
-
-###############################################################################################################  变量定义--测试环境
+# 变量定义--测试环境
 server_host = '192.168.100.53'
 
 login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
@@ -145,7 +143,7 @@ login_data = {
     "data": {
         "account": "api_test",
         # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
-        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
+        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",  # 123456
         "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
         "terminal": "API"
     },
@@ -154,41 +152,41 @@ login_data = {
 }
 
 upload_data = {
-    "funcModule":'研究报告信息',
-    "funcOperation":'上传聚烯烃PP价格预测报告',
-    "data":{
+    "funcModule": '研究报告信息',
+    "funcOperation": '上传聚烯烃PP价格预测报告',
+    "data": {
         "groupNo": "000127",
-        "ownerAccount":'arui', #报告所属用户账号
-        "reportType":'OIL_PRICE_FORECAST', # 报告类型,固定为OIL_PRICE_FORECAST
-        "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称 
-        "fileBase64": '' ,#文件内容base64
-        "categoryNo":'yyjgycbg', # 研究报告分类编码
-        "smartBusinessClassCode":'JXTJGYCBG', #分析报告分类编码
-        "reportEmployeeCode":"E40116", # 报告人
-        "reportDeptCode" :"D0044" ,# 报告部门
-        "productGroupCode":"RAW_MATERIAL"  # 商品分类
-  }
+        "ownerAccount": 'arui',  # 报告所属用户账号
+        "reportType": 'OIL_PRICE_FORECAST',  # 报告类型,固定为OIL_PRICE_FORECAST
+        "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf',  # 文件名称
+        "fileBase64": '',  # 文件内容base64
+        "categoryNo": 'yyjgycbg',  # 研究报告分类编码
+        "smartBusinessClassCode": 'JXTJGYCBG',  # 分析报告分类编码
+        "reportEmployeeCode": "E40116",  # 报告人
+        "reportDeptCode": "D0044",  # 报告部门
+        "productGroupCode": "RAW_MATERIAL"  # 商品分类
+    }
 }
 
 
 warning_data = {
-    "funcModule":'原油特征停更预警',
-    "funcOperation":'原油特征停更预警',
-    "data":{
-    "groupNo": "000127",
-    'WARNING_TYPE_NAME':'特征数据停更预警',
-    'WARNING_CONTENT':'',
-    'WARNING_DATE':''
-  }
+    "funcModule": '原油特征停更预警',
+    "funcOperation": '原油特征停更预警',
+    "data": {
+        "groupNo": "000127",
+        'WARNING_TYPE_NAME': '特征数据停更预警',
+        'WARNING_CONTENT': '',
+        'WARNING_DATE': ''
+    }
 }
 
 query_data_list_item_nos_data = {
-   "funcModule": "数据项",
-   "funcOperation": "查询",
+    "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
+        "dateStart": "20200101",
+        "dateEnd": "20241231",
+        "dataItemNoList": ["Brentzdj", "Brentzgj"]  # 数据项编码,代表 brent最低价和最高价
     }
 }
 
@@ -196,104 +194,104 @@ query_data_list_item_nos_data = {
 # 北京环境数据库
 host = '192.168.101.27'
 port = 3306
-dbusername ='root'  
+dbusername = 'root'
 password = '123456'
 dbname = 'jingbo_test'
 table_name = 'v_tbl_crude_oil_warning'
 
 
-### 开关
-is_train = False # 是否训练
-is_debug = True # 是否调试
-is_eta = False # 是否使用eta接口
-is_market = False # 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
-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 = True # 是否上传报告
-is_update_warning_data =  True # 是否上传预警数据
-is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ,0 为不删除,0.6 表示删除相关性小于0.6的特征
-is_del_tow_month = True # 是否删除两个月不更新的特征
-
+# 开关
+is_train = False  # 是否训练
+is_debug = True  # 是否调试
+is_eta = True  # 是否使用eta接口
+is_market = False  # 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
+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 = True  # 是否上传报告
+is_update_warning_data = True  # 是否上传预警数据
+is_del_corr = 0.6  # 是否删除相关性高的特征,取值为 0-1 ,0 为不删除,0.6 表示删除相关性小于0.6的特征
+is_del_tow_month = True  # 是否删除两个月不更新的特征
 
 
 # 连接到数据库
-db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
+db_mysql = MySQLDB(host=host, user=dbusername,
+                   password=password, database=dbname)
 db_mysql.connect()
-print("数据库连接成功",host,dbname,dbusername)
+print("数据库连接成功", host, dbname, dbusername)
 
 
 # 数据截取日期
-start_year = 2020 # 数据开始年份
-end_time = '2025-01-27' # 数据截取日期
+start_year = 2020  # 数据开始年份
+end_time = ''  # 数据截取日期
 freq = 'B'  # 时间频率,"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
-delweekenday = True if freq == 'B' else False # 是否删除周末数据
-is_corr = False # 特征是否参与滞后领先提升相关系数
-add_kdj = False # 是否添加kdj指标
+delweekenday = True if freq == 'B' else False  # 是否删除周末数据
+is_corr = False  # 特征是否参与滞后领先提升相关系数
+add_kdj = False  # 是否添加kdj指标
 if add_kdj and is_edbnamelist:
-    edbnamelist = edbnamelist+['K','D','J']
+    edbnamelist = edbnamelist+['K', 'D', 'J']
 
-### 模型参数  
-y = 'AVG-金能大唐久泰青州' 
+# 模型参数
+y = 'AVG-金能大唐久泰青州'
 avg_cols = [
     'PP:拉丝:1102K:出厂价:青州:国家能源宁煤(日)',
-    'PP:拉丝:L5E89:出厂价:华北(第二区域):内蒙古久泰新材料(日)',  
+    'PP:拉丝:L5E89:出厂价:华北(第二区域):内蒙古久泰新材料(日)',
     'PP:拉丝:L5E89:出厂价:河北、鲁北:大唐内蒙多伦(日)',
     'PP:拉丝:HP550J:市场价:青岛:金能化学(日)'
 ]
 offsite = 80
 offsite_col = ['PP:拉丝:HP550J:市场价:青岛:金能化学(日)']
-horizon =5 # 预测的步长
+horizon = 5  # 预测的步长
 input_size = 40  # 输入序列长度
-train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
+train_steps = 50 if is_debug else 1000  # 训练步数,用来限定epoch次数
 val_check_steps = 30  # 评估频率
-early_stop_patience_steps = 5 # 早停的耐心步数   
+early_stop_patience_steps = 5  # 早停的耐心步数
 # --- 交叉验证用的参数
 test_size = 200  # 测试集大小,定义100,后面使用的时候重新赋值
-val_size = test_size # 验证集大小,同测试集大小
+val_size = test_size  # 验证集大小,同测试集大小
 
-### 特征筛选用到的参数
-k = 100 # 特征筛选数量,如果是0或者值比特征数量大,代表全部特征
-corr_threshold = 0.6 # 相关性大于0.6的特征
-rote = 0.06 # 绘图上下界阈值
+# 特征筛选用到的参数
+k = 100  # 特征筛选数量,如果是0或者值比特征数量大,代表全部特征
+corr_threshold = 0.6  # 相关性大于0.6的特征
+rote = 0.06  # 绘图上下界阈值
 
-### 计算准确率
-weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
+# 计算准确率
+weight_dict = [0.4, 0.15, 0.1, 0.1, 0.25]  # 权重
 
 
-### 文件
-data_set = 'PP指标数据.xlsx'  # 数据集文件  
-dataset = 'juxitingdataset' # 数据集文件夹
+# 文件
+data_set = 'PP指标数据.xlsx'  # 数据集文件
+dataset = 'juxitingdataset'  # 数据集文件夹
 
 # 数据库名称
-db_name = os.path.join(dataset,'jbsh_juxiting.db')
-sqlitedb = SQLiteHandler(db_name) 
+db_name = os.path.join(dataset, 'jbsh_juxiting.db')
+sqlitedb = SQLiteHandler(db_name)
 sqlitedb.connect()
 
-settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}' 
+settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
 # 获取日期时间
 # now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
-now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
-reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
-reportname = reportname.replace(':', '-') # 替换冒号
+now = datetime.datetime.now().strftime('%Y-%m-%d')  # 获取当前日期时间
+reportname = f'PP大模型预测报告--{end_time}.pdf'  # 报告文件名
+reportname = reportname.replace(':', '-')  # 替换冒号
 if end_time == '':
     end_time = now
-### 邮件配置
-username='1321340118@qq.com'
-passwd='wgczgyhtyyyyjghi'
+# 邮件配置
+username = '1321340118@qq.com'
+passwd = 'wgczgyhtyyyyjghi'
 # recv=['liurui_test@163.com','52585119@qq.com']
-recv=['liurui_test@163.com']
+recv = ['liurui_test@163.com']
 # recv=['liurui_test@163.com']
-title='reportname'
-content=y+'预测报告请看附件'
-file=os.path.join(dataset,'reportname')
+title = 'reportname'
+content = y+'预测报告请看附件'
+file = os.path.join(dataset, 'reportname')
 # file=os.path.join(dataset,'14-7-50--100-原油指标数据.xlsx-Brent连1合约价格--20240731175936-预测报告.pdf')
-ssl=True
+ssl = True
 
 
-### 日志配置
+# 日志配置
 
 # 创建日志目录(如果不存在)
 log_dir = 'logs'
@@ -305,8 +303,10 @@ logger = logging.getLogger('my_logger')
 logger.setLevel(logging.INFO)
 
 # 配置文件处理器,将日志记录到文件
-file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
-file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
+file_handler = logging.handlers.RotatingFileHandler(os.path.join(
+    log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
+file_handler.setFormatter(logging.Formatter(
+    '%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
 
 # 配置控制台处理器,将日志打印到控制台
 console_handler = logging.StreamHandler()
@@ -317,4 +317,3 @@ logger.addHandler(file_handler)
 logger.addHandler(console_handler)
 
 # logger.info('当前配置:'+settings)
-
diff --git a/lib/dataread.py b/lib/dataread.py
index f3a385d..e30c6a1 100644
--- a/lib/dataread.py
+++ b/lib/dataread.py
@@ -103,6 +103,14 @@ global_config = {
     # ETA配置
     'APPID': None,
     'SECRET': None,
+    'classifylisturl': None,
+    'classifyidlisturl': None,
+    'edbcodedataurl': None,
+    'edbcodelist': None,
+    'edbdatapushurl': None,
+    'edbdeleteurl': None,
+    'edbbusinessurl': None,
+    'ClassifyId': None,
 
     # 数据库配置
     'sqlitedb': None,
@@ -1352,7 +1360,7 @@ def style_row(row):
 
 
 class EtaReader():
-    def __init__(self, signature, classifylisturl, classifyidlisturl, edbcodedataurl, edbcodelist, edbdatapushurl, edbdeleteurl, edbbusinessurl):
+    def __init__(self, signature, classifylisturl, classifyidlisturl, edbcodedataurl, edbcodelist, edbdatapushurl, edbdeleteurl, edbbusinessurl, classifyId):
         '''
         初始化 EtaReader 类的实例。
 
@@ -1377,6 +1385,7 @@ class EtaReader():
         self.edbcodelist = edbcodelist
         self.edbdeleteurl = edbdeleteurl
         self.edbbusinessurl = edbbusinessurl
+        self.classifyId = classifyId
 
     def filter_yuanyou_data(self, ClassifyName, data):
         '''
@@ -1779,7 +1788,6 @@ class EtaReader():
         return df_zhibiaoshuju, df_zhibiaoliebiao
 
     def get_eta_api_pp_data(self, data_set, dataset=''):
-        global ClassifyId
         today = datetime.date.today().strftime("%Y-%m-%d")
 
         # 定义你的headers,这里可以包含多个参数
@@ -1818,7 +1826,7 @@ class EtaReader():
             # 请求成功,处理响应内容
             # config.logger.info(data.get('Data'))
             # 定义你想要保留的固定值
-            fixed_value = ClassifyId
+            fixed_value = self.classifyId
 
             # 遍历列表,只保留那些'category' key的值为固定值的数据项
             filtered_data = [item for item in data.get(
diff --git a/main_juxiting.py b/main_juxiting.py
index f7563d2..fab405c 100644
--- a/main_juxiting.py
+++ b/main_juxiting.py
@@ -1,12 +1,75 @@
 # 读取配置
-from lib.dataread import *
-from lib.tools import SendMail,exception_logger
-from models.nerulforcastmodels import ex_Model_Juxiting,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
 
-import glob
+from lib.dataread import *
+from config_juxiting import *
+from lib.tools import SendMail, exception_logger
+from models.nerulforcastmodels import ex_Model, model_losss, model_losss_juxiting, brent_export_pdf, tansuanli_export_pdf, pp_export_pdf, model_losss_juxiting
+import datetime
 import torch
 torch.set_float32_matmul_precision("high")
 
+global_config.update({
+    # 核心参数
+    'logger': logger,
+    'dataset': dataset,
+    'y': y,
+    'is_debug': is_debug,
+    'is_train': is_train,
+    'is_fivemodels': is_fivemodels,
+    'settings': settings,
+
+
+    # 模型参数
+    'data_set': data_set,
+    'input_size': input_size,
+    'horizon': horizon,
+    'train_steps': train_steps,
+    'val_check_steps': val_check_steps,
+    'val_size': val_size,
+    'test_size': test_size,
+    'modelsindex': modelsindex,
+    'rote': rote,
+
+    # 特征工程开关
+    'is_del_corr': is_del_corr,
+    'is_del_tow_month': is_del_tow_month,
+    'is_eta': is_eta,
+    'is_update_eta': is_update_eta,
+    'early_stop_patience_steps': early_stop_patience_steps,
+
+    # 时间参数
+    'start_year': start_year,
+    'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
+    'freq': freq,  # 保持列表结构
+
+    # 接口配置
+    'login_pushreport_url': login_pushreport_url,
+    'login_data': login_data,
+    'upload_url': upload_url,
+    'upload_warning_url': upload_warning_url,
+    'warning_data': warning_data,
+
+    # 查询接口
+    'query_data_list_item_nos_url': query_data_list_item_nos_url,
+    'query_data_list_item_nos_data': query_data_list_item_nos_data,
+
+    # eta 配置
+    'APPID': APPID,
+    'SECRET': SECRET,
+    'etadata': data,
+    'edbcodelist': edbcodelist,
+    'ClassifyId': ClassifyId,
+    'edbcodedataurl': edbcodedataurl,
+    'classifyidlisturl': classifyidlisturl,
+    'edbdatapushurl': edbdatapushurl,
+    'edbdeleteurl': edbdeleteurl,
+    'edbbusinessurl': edbbusinessurl,
+    'ClassifyId':   ClassifyId,
+    'classifylisturl': classifylisturl,
+
+    # 数据库配置
+    'sqlitedb': sqlitedb,
+})
 
 
 def predict_main():
@@ -48,31 +111,23 @@ def predict_main():
     返回:
         None
     """
-    global end_time
-    signature = BinanceAPI(APPID, SECRET)
-    etadata = EtaReader(signature=signature,
-                        classifylisturl=classifylisturl,
-                        classifyidlisturl=classifyidlisturl,
-                        edbcodedataurl=edbcodedataurl,
-                        edbcodelist=edbcodelist,
-                        edbdatapushurl=edbdatapushurl,
-                        edbdeleteurl=edbdeleteurl,
-                        edbbusinessurl=edbbusinessurl
-                        )
+    end_time = global_config['end_time']
     # 获取数据
     if is_eta:
         logger.info('从eta获取数据...')
         signature = BinanceAPI(APPID, SECRET)
         etadata = EtaReader(signature=signature,
-                            classifylisturl=classifylisturl,
-                            classifyidlisturl=classifyidlisturl,
-                            edbcodedataurl=edbcodedataurl,
-                            edbcodelist=edbcodelist,
-                            edbdatapushurl=edbdatapushurl,
-                            edbdeleteurl=edbdeleteurl,
-                            edbbusinessurl=edbbusinessurl,
+                            classifylisturl=global_config['classifylisturl'],
+                            classifyidlisturl=global_config['classifyidlisturl'],
+                            edbcodedataurl=global_config['edbcodedataurl'],
+                            edbcodelist=global_config['edbcodelist'],
+                            edbdatapushurl=global_config['edbdatapushurl'],
+                            edbdeleteurl=global_config['edbdeleteurl'],
+                            edbbusinessurl=global_config['edbbusinessurl'],
+                            classifyId=global_config['ClassifyId'],
                             )
-        df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(data_set=data_set, dataset=dataset)  # 原始数据,未处理
+        df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
+            data_set=data_set, dataset=dataset)  # 原始数据,未处理
 
         if is_market:
             logger.info('从市场信息平台获取数据...')
@@ -83,26 +138,26 @@ def predict_main():
                     df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
                 else:
                     logger.info('从市场信息平台获取数据')
-                    df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
-                    
-            except :
+                    df_zhibiaoshuju = get_market_data(
+                        end_time, df_zhibiaoshuju)
+
+            except:
                 logger.info('最高最低价拼接失败')
-        
+
         # 保存到xlsx文件的sheet表
-        with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
+        with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
             df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
             df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
-        
-        
+
         # 数据处理
-        df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
-                        end_time=end_time)
+        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,df_zhibiaoliebiao = getdata_juxiting(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
+        df, df_zhibiaoliebiao = 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)
@@ -124,47 +179,65 @@ def predict_main():
     else:
         for row in first_row.itertuples(index=False):
             row_dict = row._asdict()
-            row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
-            check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
+            config.logger.info(f'要保存的真实值:{row_dict}')
+            # 判断ds是否为字符串类型,如果不是则转换为字符串类型
+            if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
+                row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
+            elif not isinstance(row_dict['ds'], str):
+                try:
+                    row_dict['ds'] = pd.to_datetime(
+                        row_dict['ds']).strftime('%Y-%m-%d')
+                except:
+                    logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
+            # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
+            # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
+            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}'")
+                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=row_dict.keys())
+            sqlitedb.insert_data('trueandpredict', tuple(
+                row_dict.values()), columns=row_dict.keys())
 
     # 更新accuracy表的y值
     if not sqlitedb.check_table_exists('accuracy'):
         pass
     else:
-        update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
+        update_y = sqlitedb.select_data(
+            'accuracy', where_condition="y is null")
         if len(update_y) > 0:
             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]
+            update_y = update_y[update_y['ds'] <= end_time]
             logger.info(f'要更新y的信息:{update_y}')
             # try:
             for row in update_y.itertuples(index=False):
                 try:
-                    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']}'")
+                    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:
                     logger.info(f'更新accuracy表的y值失败:{row_dict}')
             # 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
     is_weekday = datetime.datetime.now().weekday() == 0
     if is_weekday:
         logger.info('今天是周一,更新预测模型')
         # 计算最近60天预测残差最低的模型名称
-        model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
+        model_results = sqlitedb.select_data(
+            'trueandpredict', order_by="ds DESC", limit="60")
         # 删除空值率为90%以上的列
         if len(model_results) > 10:
-            model_results = model_results.dropna(thresh=len(model_results)*0.1,axis=1)
+            model_results = model_results.dropna(
+                thresh=len(model_results)*0.1, axis=1)
         # 删除空行
         model_results = model_results.dropna()
         modelnames = model_results.columns.to_list()[2:-1]
@@ -172,51 +245,61 @@ def predict_main():
             model_results[col] = model_results[col].astype(np.float32)
         # 计算每个预测值与真实值之间的偏差率
         for model in modelnames:
-            model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
+            model_results[f'{model}_abs_error_rate'] = abs(
+                model_results['y'] - model_results[model]) / model_results['y']
         # 获取每行对应的最小偏差率值
-        min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
+        min_abs_error_rate_values = model_results.apply(
+            lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
         # 获取每行对应的最小偏差率值对应的列名
-        min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
+        min_abs_error_rate_column_name = model_results.apply(
+            lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
         # 将列名索引转换为列名
-        min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
+        min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
+            lambda x: x.split('_')[0])
         # 取出现次数最多的模型名称
         most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
         logger.info(f"最近60天预测残差最低的模型名称:{most_common_model}")
         # 保存结果到数据库
         if not sqlitedb.check_table_exists('most_model'):
-            sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
-        sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
+            sqlitedb.create_table(
+                'most_model', columns="ds datetime, most_common_model TEXT")
+        sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
+            '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
 
     try:
-        # if is_weekday:
-        if True:
-            # logger.info('今天是周一,发送特征预警')
-            # # 上传预警信息到数据库
-            # warning_data_df = df_zhibiaoliebiao.copy()
-            # warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
-            # # 重命名列名
-            # warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
-            # from sqlalchemy import create_engine
-            # import urllib
-            # global password
-            # if '@' in password:
-            #     password = urllib.parse.quote_plus(password)
+        if is_weekday:
+            # if True:
+            logger.info('今天是周一,发送特征预警')
+            # 上传预警信息到数据库
+            warning_data_df = df_zhibiaoliebiao.copy()
+            warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
+                '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
+            # 重命名列名
+            warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
+                                                     '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
+            from sqlalchemy import create_engine
+            import urllib
+            global password
+            if '@' in password:
+                password = urllib.parse.quote_plus(password)
 
-            # engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
-            # warning_data_df['WARNING_DATE'] =  datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
-            # warning_data_df['TENANT_CODE'] =  'T0004'
-            # # 插入数据之前查询表数据然后新增id列
-            # existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
-            # if not existing_data.empty:
-            #     max_id = existing_data['ID'].astype(int).max()
-            #     warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
-            # else:
-            #     warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
-            # warning_data_df.to_sql(table_name,  con=engine, if_exists='append', index=False)
-            # if is_update_warning_data:
-            #     upload_warning_info(len(warning_data_df))
+            engine = create_engine(
+                f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
+            warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
+                "%Y-%m-%d %H:%M:%S")
+            warning_data_df['TENANT_CODE'] = 'T0004'
+            # 插入数据之前查询表数据然后新增id列
+            existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
+            if not existing_data.empty:
+                max_id = existing_data['ID'].astype(int).max()
+                warning_data_df['ID'] = range(
+                    max_id + 1, max_id + 1 + len(warning_data_df))
+            else:
+                warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
+            warning_data_df.to_sql(
+                table_name,  con=engine, if_exists='append', index=False)
             if is_update_warning_data:
-                upload_warning_info(10)
+                upload_warning_info(len(warning_data_df))
     except:
         logger.info('上传预警信息到数据库失败')
 
@@ -228,76 +311,75 @@ def predict_main():
     row, col = df.shape
 
     now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
-    # ex_Model_Juxiting(df,
-    #          horizon=horizon,
-    #          input_size=input_size,
-    #          train_steps=train_steps,
-    #          val_check_steps=val_check_steps,
-    #          early_stop_patience_steps=early_stop_patience_steps,
-    #          is_debug=is_debug,
-    #          dataset=dataset,
-    #          is_train=is_train,
-    #          is_fivemodels=is_fivemodels,
-    #          val_size=val_size,
-    #          test_size=test_size,
-    #          settings=settings,
-    #          now=now,
-    #          etadata=etadata,
-    #          modelsindex=modelsindex,
-    #          data=data,
-    #          is_eta=is_eta,
-    #          end_time=end_time,
-    #          )
-
+    ex_Model(df,
+             horizon=global_config['horizon'],
+             input_size=global_config['input_size'],
+             train_steps=global_config['train_steps'],
+             val_check_steps=global_config['val_check_steps'],
+             early_stop_patience_steps=global_config['early_stop_patience_steps'],
+             is_debug=global_config['is_debug'],
+             dataset=global_config['dataset'],
+             is_train=global_config['is_train'],
+             is_fivemodels=global_config['is_fivemodels'],
+             val_size=global_config['val_size'],
+             test_size=global_config['test_size'],
+             settings=global_config['settings'],
+             now=now,
+             etadata=global_config['etadata'],
+             modelsindex=global_config['modelsindex'],
+             data=data,
+             is_eta=global_config['is_eta'],
+             end_time=global_config['end_time'],
+             )
 
     logger.info('模型训练完成')
- 
+
     logger.info('训练数据绘图ing')
-    model_results3 = model_losss_juxiting(sqlitedb)
+    model_results3 = model_losss(sqlitedb, end_time=end_time)
     logger.info('训练数据绘图end')
-    
-    # 模型报告
-    logger.info('制作报告ing')
-    title = f'{settings}--{end_time}-预测报告' # 报告标题
-    reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
-    reportname = reportname.replace(':', '-') # 替换冒号
-    pp_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')
+    # # 模型报告
+    # logger.info('制作报告ing')
+    # title = f'{settings}--{end_time}-预测报告'  # 报告标题
+    # reportname = f'Brent原油大模型月度预测--{end_time}.pdf'  # 报告文件名
+    # reportname = reportname.replace(':', '-')  # 替换冒号
+    # 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')
 
     # # LSTM 单变量模型
     # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
-    
+
     # # lstm 多变量模型
     # ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
-    
+
     # # GRU 模型
     # # ex_GRU(df)
 
     # 发送邮件
-    m = SendMail(
-        username=username,
-        passwd=passwd,
-        recv=recv,
-        title=title,
-        content=content,
-        file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
-        ssl=ssl,
-    )
-    # m.send_mail()   
+    # m = SendMail(
+    #     username=username,
+    #     passwd=passwd,
+    #     recv=recv,
+    #     title=title,
+    #     content=content,
+    #     file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
+    #     ssl=ssl,
+    # )
+    # m.send_mail()
 
 
 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('2025-1-20', '2025-2-6', freq='B'):
-    #     end_time = i_time.strftime('%Y-%m-%d')
+    # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
+    # for i_time in pd.date_range('2022-1-1', '2025-3-26', freq='M'):
     #     try:
+    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
     #         predict_main()
-    #     except:
-    #         pass
+    #     except Exception as e:
+    #         logger.info(f'预测失败:{e}')
+    #         continue
 
-    predict_main()
\ No newline at end of file
+    predict_main()
diff --git a/main_yuanyou_yuedu.py b/main_yuanyou_yuedu.py
index d9b055b..c00a2b0 100644
--- a/main_yuanyou_yuedu.py
+++ b/main_yuanyou_yuedu.py
@@ -374,13 +374,12 @@ def predict_main():
 if __name__ == '__main__':
     # global end_time
     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    for i_time in pd.date_range('2022-1-1', '2025-3-26', freq='M'):
-        try:
-            global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-            predict_main()
-        except Exception as e:
-            logger.info(f'预测失败:{e}')
-            continue
+    # for i_time in pd.date_range('2022-1-1', '2025-3-26', freq='M'):
+    #     try:
+    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
+    #         predict_main()
+    #     except Exception as e:
+    #         logger.info(f'预测失败:{e}')
+    #         continue
 
-
-    # predict_main()
+    predict_main()