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?=
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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"
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- },
- {
- "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"
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- },
- {
- "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()