PriceForecast/aisenzhecode/石油苯/纯苯价格预测-自定义日期ytj.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import json\n",
    "import xlrd\n",
    "import xlwt\n",
    "from datetime import datetime\n",
    "import time\n",
    "# 变量定义\n",
    "login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
    "search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\"\n",
    "\n",
    "login_push_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
    "upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n",
    "\n",
    "login_data = {\n",
    "    \"data\": {\n",
    "        \"account\": \"api_dev\",\n",
    "        \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
    "        \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
    "        \"terminal\": \"API\"\n",
    "    },\n",
    "    \"funcModule\": \"API\",\n",
    "    \"funcOperation\": \"获取token\"\n",
    "}\n",
    "\n",
    "login_push_data = {\n",
    "    \"data\": {\n",
    "        \"account\": \"api_dev\",\n",
    "        \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
    "        \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
    "        \"terminal\": \"API\"\n",
    "    },\n",
    "    \"funcModule\": \"API\",\n",
    "    \"funcOperation\": \"获取token\"\n",
    "}\n",
    "\n",
    "read_file_path_name = \"纯苯数据项.xls\"\n",
    "one_cols = []\n",
    "two_cols = []\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import random\n",
    "import time\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sklearn.datasets as datasets\n",
    "\n",
    "#导入机器学习算法模型\n",
    "from sklearn.linear_model import Lasso\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "import datetime\n",
    "import statsmodels.api as sm\n",
    "try:\n",
    "    from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "except:\n",
    "    from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "import xgboost as xgb\n",
    "from xgboost import plot_importance, plot_tree\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from statsmodels.tools.eval_measures import mse,rmse\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBRegressor\n",
    "import warnings\n",
    "import pickle\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#切割训练数据和样本数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#用于模型评分\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "# print(__version__) # requires version >= 1.9.0\n",
    "\n",
    "\n",
    "import cufflinks as cf\n",
    "cf.go_offline()\n",
    "\n",
    "random.seed(100)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# 数据获取\n",
    "\n",
    "def get_head_auth():\n",
    "    login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
    "    text = json.loads(login_res.text)\n",
    "    if text[\"status\"]:\n",
    "        token = text[\"data\"][\"accessToken\"]\n",
    "        return token\n",
    "    else:\n",
    "        print(\"获取认证失败\")\n",
    "        return None\n",
    "\n",
    "\n",
    "def get_data_value(token, dataItemNoList,date):\n",
    "    search_data = {\n",
    "        \"data\": {\n",
    "            \"date\": get_cur_time(date)[0],\n",
    "            \"dataItemNoList\": dataItemNoList\n",
    "        },\n",
    "        \"funcModule\": \"数据项\",\n",
    "        \"funcOperation\": \"查询\"\n",
    "    }\n",
    "    headers = {\"Authorization\": token}\n",
    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
    "    search_value = json.loads(search_res.text)[\"data\"]\n",
    "    if search_value:\n",
    "        return search_value\n",
    "    else:\n",
    "        print(\"今天没有新数据\")\n",
    "        return search_value\n",
    "\n",
    "\n",
    "# xls文件处理\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def write_xls(data,date):\n",
    "    # 创建一个Workbook对象\n",
    "    workbook = xlwt.Workbook()\n",
    "\n",
    "    # 创建一个Sheet对象，可指定名称\n",
    "    sheet = workbook.load('Sheet1')\n",
    "\n",
    "    # 写入数据行\n",
    "    for row_index, row_data in enumerate(data):\n",
    "        for col_index, cell_data in enumerate(row_data):\n",
    "            sheet.write(row_index, col_index, cell_data)\n",
    "\n",
    "    # 保存Workbook到文件\n",
    "    workbook.save(get_cur_time(date)[0] + '.xls')\n",
    "\n",
    "\n",
    "def get_cur_time(date=''):\n",
    "    if date == '':\n",
    "        import datetime\n",
    "        now = datetime.datetime.now()\n",
    "    else:\n",
    "        now = date\n",
    "    year = now.year\n",
    "    month = now.month\n",
    "    day = now.day\n",
    "\n",
    "    if month < 10:\n",
    "        month = \"0\" + str(month)\n",
    "    if day < 10:\n",
    "        day = \"0\" + str(day)\n",
    "    cur_time = str(year) + str(month) + str(day)\n",
    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
    "    return cur_time, cur_time2\n",
    "\n",
    "\n",
    "def get_head_push_auth():\n",
    "    login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))\n",
    "    text = json.loads(login_res.text)\n",
    "    if text[\"status\"]:\n",
    "        token = text[\"data\"][\"accessToken\"]\n",
    "        return token\n",
    "    else:\n",
    "        print(\"获取认证失败\")\n",
    "        return None\n",
    "\n",
    "\n",
    "\n",
    "def upload_data_to_system(token_push,date):\n",
    "    datavalue = forecast_price()\n",
    "    data = {\n",
    "        \"funcModule\": \"数据表信息列表\",\n",
    "        \"funcOperation\": \"新增\",\n",
    "        \"data\": [\n",
    "            {\"dataItemNo\": \"C01100047|FORECAST_PRICE\",\n",
    "             \"dataDate\": get_cur_time(date)[0],\n",
    "             \"dataStatus\": \"add\",\n",
    "             \"dataValue\": datavalue\n",
    "             }\n",
    "\n",
    "        ]\n",
    "    }\n",
    "    print(data)\n",
    "    headers = {\"Authorization\": token_push}\n",
    "    res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
    "    print(res.text)\n",
    "\n",
    "    \n",
    "# def upload_data_to_system(token):\n",
    "#     data = {\n",
    "#         \"funcModule\": \"数据表信息列表\",\n",
    "#         \"funcOperation\": \"新增\",\n",
    "#         \"data\": [\n",
    "#             {\"dataItemNo\": \"C01100036|Forecast_ Price|ACN\",\n",
    "#              \"dataDate\": '20230706',\n",
    "#              \"dataStatus\": \"add\",\n",
    "#              \"dataValue\": 3780.0\n",
    "#              }\n",
    "\n",
    "#         ]\n",
    "#     }\n",
    "#     headers = {\"Authorization\": token}\n",
    "#     res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
    "#     print(res.text)\n",
    "\n",
    "    \n",
    "    \n",
    "def forecast_price():\n",
    "    # df_test = pd.read_csv('定价模型数据收集0212.csv')\n",
    "    df_test = pd.read_excel('纯苯数据项.xls',sheet_name='Sheet1')\n",
    "    df_test.drop([0],inplace=True)\n",
    "    # df_test['Date']=pd.to_datetime(df_test['Date'],  format='%m/%d/%Y',infer_datetime_format=True)\n",
    "    df_test['Date']=pd.to_datetime(df_test['Date'],  format=r'%Y-%m-%d',infer_datetime_format=True)\n",
    "\n",
    "\n",
    "    df_test_1 = df_test\n",
    "    df_test_1=df_test_1.fillna(df_test.ffill())\n",
    "    df_test_1=df_test_1.fillna(df_test_1.bfill())\n",
    "\n",
    "    # 选择用于模型训练的列名称\n",
    "    col_for_training = df_test_1.columns\n",
    "\n",
    "\n",
    "    import joblib\n",
    "    Best_model_DalyLGPrice = joblib.load(\"日度价格预测_最佳模型.pkl\")\n",
    "    # 最新的一天为最后一行的数据\n",
    "   \n",
    "    df_test_1_Day = df_test_1.tail(1)\n",
    "    # 移除不需要的列\n",
    "    df_test_1_Day.index = df_test_1_Day[\"Date\"]\n",
    "    df_test_1_Day = df_test_1_Day.drop([\"Date\"], axis= 1)\n",
    "    df_test_1_Day=df_test_1_Day.drop('Price',axis=1)\n",
    "    df_test_1_Day=df_test_1_Day.dropna()\n",
    "\n",
    "    for col in df_test_1_Day.columns:\n",
    "        df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col],errors='coerce')\n",
    "    #预测今日价格，显示至小数点后两位\n",
    "    Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)\n",
    "\n",
    "    df_test_1_Day['日度预测价格']=Ypredict_Today\n",
    "    print(df_test_1_Day['日度预测价格'])\n",
    "    a = df_test_1_Day['日度预测价格']\n",
    "    a = a[0]\n",
    "    a = float(a)\n",
    "    a = round(a,2)\n",
    "    return a\n",
    "def optimize_Model():\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    from sklearn.impute  import SimpleImputer\n",
    "    from sklearn.preprocessing import OrdinalEncoder\n",
    "    from sklearn.feature_selection import SelectFromModel\n",
    "    from sklearn.metrics import mean_squared_error, r2_score\n",
    "    import pandas as pd\n",
    "\n",
    "    pd.set_option('display.max_rows',40)  \n",
    "    pd.set_option('display.max_columns',40)  \n",
    "    df_test = pd.read_excel('纯苯数据项.xls')\n",
    "    df_test.drop([0],inplace=True)\n",
    "    # df_test['Date']=pd.to_datetime(df_test['Date'],  format='%m/%d/%Y',infer_datetime_format=True)\n",
    "    df_test['Date']=pd.to_datetime(df_test['Date'],  format='%Y-%m-%d',infer_datetime_format=True)\n",
    "\n",
    "    \n",
    "    #将缺失值补为前一个或者后一个数值\n",
    "    df_test_1 = df_test\n",
    "    df_test_1=df_test_1.fillna(df_test.ffill())\n",
    "    df_test_1=df_test_1.fillna(df_test_1.bfill())\n",
    "    df_test_1[\"Date\"] = pd.to_datetime(df_test_1[\"Date\"])\n",
    "    df_test_1.index = df_test_1[\"Date\"]\n",
    "    df_test_1 = df_test_1.drop([\"Date\"], axis= 1)\n",
    "    df_test_1 = df_test_1.astype('float')\n",
    "    \n",
    "    \n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    from pandas import Series,DataFrame\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    import sklearn.datasets as datasets\n",
    "\n",
    "    #导入机器学习算法模型\n",
    "    from sklearn.linear_model import Lasso\n",
    "    from xgboost import XGBRegressor\n",
    "\n",
    "    from datetime import datetime\n",
    "    import statsmodels.api as sm\n",
    "    try:\n",
    "        from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "    except:\n",
    "        from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
    "\n",
    "    import plotly.express as px\n",
    "    import plotly.graph_objects as go\n",
    "\n",
    "    import xgboost as xgb\n",
    "    from xgboost import plot_importance, plot_tree\n",
    "    from sklearn.metrics import mean_absolute_error\n",
    "    from statsmodels.tools.eval_measures import mse,rmse\n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    from xgboost import XGBRegressor\n",
    "    import warnings\n",
    "    import pickle\n",
    "\n",
    "    from sklearn.metrics import mean_squared_error\n",
    "\n",
    "    #切割训练数据和样本数据\n",
    "    from sklearn.model_selection import train_test_split\n",
    "\n",
    "    #用于模型评分\n",
    "    from sklearn.metrics import r2_score\n",
    "\n",
    "    dataset1=df_test_1.drop('Price',axis=1)#.astype(float)\n",
    "\n",
    "    y=df_test_1['Price']\n",
    "\n",
    "    x=dataset1 \n",
    "\n",
    "    train = x\n",
    "    target = y\n",
    "\n",
    "    #切割数据样本集合测试集\n",
    "    X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)\n",
    "\n",
    "    #模型缩写\n",
    "    Lasso =  Lasso(random_state=0)\n",
    "    XGBR = XGBRegressor(random_state=0)\n",
    "    #训练模型\n",
    "    Lasso.fit(X_train,y_train)\n",
    "    XGBR.fit(X_train,y_train)\n",
    "    #模型拟合\n",
    "    y_pre_Lasso = Lasso.predict(x_test)\n",
    "    y_pre_XGBR = XGBR.predict(x_test)\n",
    "\n",
    "    #计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²\n",
    "    Lasso_score = r2_score(y_true,y_pre_Lasso)\n",
    "    XGBR_score=r2_score(y_true,y_pre_XGBR)\n",
    "\n",
    "    #计算Lasso、XGBR的MSE和RMSE\n",
    "    Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)\n",
    "    XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)\n",
    "\n",
    "    Lasso_RMSE=np.sqrt(Lasso_MSE)\n",
    "    XGBR_RMSE=np.sqrt(XGBR_MSE)\n",
    "    # 将不同模型的不同误差值整合成一个表格\n",
    "    model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],\n",
    "                                 ['XgBoost', XGBR_RMSE, XGBR_score]],\n",
    "                   columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])\n",
    "    #将模型名称(Model)列设置为索引\n",
    "    model_results1=model_results.set_index('模型(Model)')\n",
    "\n",
    "    model_results1\n",
    "    #定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整\n",
    "    def plot_feature_importance(importance,names,model_type):\n",
    "        feature_importance = np.array(importance)\n",
    "        feature_names = np.array(names)\n",
    "\n",
    "        data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
    "        fi_df = pd.DataFrame(data)\n",
    "\n",
    "        fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
    "\n",
    "        plt.figure(figsize=(10,8))\n",
    "        sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n",
    "\n",
    "        plt.title(model_type + \" \"+'FEATURE IMPORTANCE')\n",
    "        plt.xlabel('FEATURE IMPORTANCE')\n",
    "        plt.ylabel('FEATURE NAMES')\n",
    "        from pylab import mpl\n",
    "    %pylab\n",
    "    mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    ## Xgboost 模型参数优化-初步\n",
    "    #参考: https://juejin.im/post/6844903661013827598 \n",
    "    #每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）\n",
    "\n",
    "    from xgboost import XGBRegressor\n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "    estimator = XGBRegressor(random_state=0,\n",
    "        nthread=4,\n",
    "        seed=0\n",
    "    )\n",
    "    parameters = {\n",
    "        'max_depth': range (2, 11, 2),     # 树的最大深度\n",
    "        'n_estimators': range (50, 101, 10),  # 迭代次数\n",
    "        'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]\n",
    "    }\n",
    "\n",
    "    grid_search_XGB = GridSearchCV(\n",
    "        estimator=estimator,\n",
    "        param_grid=parameters,\n",
    "    #     n_jobs = 10,\n",
    "        cv = 3,\n",
    "        verbose=True\n",
    "    )\n",
    "\n",
    "    grid_search_XGB.fit(X_train, y_train)\n",
    "    #如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行\n",
    "\n",
    "    best_parameters = grid_search_XGB.best_estimator_.get_params()\n",
    "    y_pred = grid_search_XGB.predict(x_test)\n",
    "\n",
    "    op_XGBR_score = r2_score(y_true,y_pred)\n",
    "    op_XGBR_MSE= mean_squared_error(y_true, y_pred)\n",
    "    op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)\n",
    "\n",
    "    model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],\n",
    "                   columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])\n",
    "    model_results2=model_results2.set_index('模型(Model)')\n",
    "\n",
    "    # results = model_results1.append(model_results2, ignore_index = False)\n",
    "    results = pd.concat([model_results1,model_results2],ignore_index=True)\n",
    "\n",
    "\n",
    "\n",
    "    import pickle\n",
    "\n",
    "    Pkl_Filename = \"日度价格预测_最佳模型.pkl\"  \n",
    "\n",
    "    with open(Pkl_Filename, 'wb') as file:  \n",
    "       pickle.dump(grid_search_XGB, file)\n",
    "\n",
    "\n",
    "   \n",
    "\n",
    "def read_xls_data():\n",
    "    global one_cols, two_cols\n",
    "    # 打开 XLS 文件\n",
    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
    "\n",
    "    # 获取所有表格名称\n",
    "    # sheet_names = workbook.sheet_names()\n",
    "\n",
    "    # 选择第一个表格\n",
    "    sheet = workbook.sheet_by_index(0)\n",
    "\n",
    "    # 获取行数和列数\n",
    "    num_rows = sheet.nrows\n",
    "    # num_cols = sheet.ncols\n",
    "\n",
    "    # 遍历每一行，获取单元格数据\n",
    "    # for i in range(num_rows):\n",
    "    #     row_data = sheet.row_values(i)\n",
    "    #     one_cols.append(row_data)\n",
    "    #     two_cols.append(row_data[1])\n",
    "\n",
    "    row_data = sheet.row_values(1)\n",
    "    one_cols = row_data\n",
    "\n",
    "    # 关闭 XLS 文件\n",
    "    # workbook.close()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def start(date):\n",
    "    read_xls_data()\n",
    "\n",
    "    token = get_head_auth()\n",
    "    if not token:\n",
    "        return\n",
    "    token_push = get_head_push_auth()\n",
    "    if not token_push:\n",
    "        return\n",
    "\n",
    "    datas = get_data_value(token, one_cols[1:],date)\n",
    "#     if not datas:\n",
    "#         return\n",
    "\n",
    "    # data_list = [two_cols, one_cols]\n",
    "    append_rows = [get_cur_time(date)[1]]\n",
    "    dataItemNo_dataValue = {}\n",
    "    for data_value in datas:\n",
    "        if \"dataValue\" not in data_value:\n",
    "            print(data_value)\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
    "        else:\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "       \n",
    "    for value in one_cols[1:]:\n",
    "        if value in dataItemNo_dataValue:\n",
    "            append_rows.append(dataItemNo_dataValue[value])\n",
    "        else:\n",
    "            append_rows.append(\"\")\n",
    "    save_xls(append_rows)\n",
    "    optimize_Model()\n",
    "    upload_data_to_system(token_push,date)\n",
    "    # data_list.append(three_cols)\n",
    "    # write_xls(data_list)\n",
    "\n",
    "\n",
    "def start_1(date):\n",
    "    read_xls_data()\n",
    "\n",
    "    token = get_head_auth()\n",
    "    if not token:\n",
    "        return\n",
    "    \n",
    "\n",
    "    datas = get_data_value(token, one_cols[1:])\n",
    "#     if not datas:\n",
    "#         return\n",
    "\n",
    "    # data_list = [two_cols, one_cols]\n",
    "    append_rows = [get_cur_time(date)[1]]\n",
    "    dataItemNo_dataValue = {}\n",
    "    for data_value in datas:\n",
    "        if \"dataValue\" not in data_value:\n",
    "            print(data_value)\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
    "        else:\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "       \n",
    "    for value in one_cols[1:]:\n",
    "        if value in dataItemNo_dataValue:\n",
    "            append_rows.append(dataItemNo_dataValue[value])\n",
    "        else:\n",
    "            append_rows.append(\"\")\n",
    "    save_xls_1(append_rows)\n",
    "\n",
    "    \n",
    "    # data_list.append(three_cols)\n",
    "    # write_xls(data_list)\n",
    "    \n",
    "def save_xls_1(append_rows):\n",
    "\n",
    "    # 打开xls文件\n",
    "    workbook = xlrd.open_workbook('纯苯数据项.xls')\n",
    "\n",
    "    # 获取所有sheet的个数\n",
    "    sheet_count = len(workbook.sheet_names())\n",
    "\n",
    "    # 获取所有sheet的名称\n",
    "    sheet_names = workbook.sheet_names()\n",
    "\n",
    "    new_workbook = xlwt.Workbook()\n",
    "    for i in range(sheet_count):\n",
    "        # 获取当前sheet\n",
    "        sheet = workbook.sheet_by_index(i)\n",
    "\n",
    "        # 获取sheet的行数和列数\n",
    "        row_count = sheet.nrows - 1\n",
    "        col_count = sheet.ncols\n",
    "        # 获取原有数据\n",
    "        data = []\n",
    "        for row in range(row_count):\n",
    "            row_data = []\n",
    "            for col in range(col_count):\n",
    "                row_data.append(sheet.cell_value(row, col))\n",
    "            data.append(row_data)\n",
    "        # 创建xlwt的Workbook对象\n",
    "        # 创建sheet\n",
    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
    "\n",
    "        # 将原有的数据写入新的sheet\n",
    "        for row in range(row_count):\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row, col, data[row][col])\n",
    "\n",
    "        if i == 0:\n",
    "            # 在新的sheet中添加数据\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row_count, col, append_rows[col])\n",
    "\n",
    "    # 保存新的xls文件\n",
    "    new_workbook.save(\"纯苯数据项.xls\")    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "def check_data(dataItemNo):\n",
    "    token = get_head_auth()\n",
    "    if not token:\n",
    "        return\n",
    "\n",
    "    datas = get_data_value(token, dataItemNo)\n",
    "    if not datas:\n",
    "        return\n",
    "\n",
    "\n",
    "def save_xls(append_rows):\n",
    "\n",
    "    # 打开xls文件\n",
    "    workbook = xlrd.open_workbook('纯苯数据项.xls')\n",
    "\n",
    "    # 获取所有sheet的个数\n",
    "    sheet_count = len(workbook.sheet_names())\n",
    "\n",
    "    # 获取所有sheet的名称\n",
    "    sheet_names = workbook.sheet_names()\n",
    "\n",
    "    new_workbook = xlwt.Workbook()\n",
    "    for i in range(sheet_count):\n",
    "        # 获取当前sheet\n",
    "        sheet = workbook.sheet_by_index(i)\n",
    "\n",
    "        # 获取sheet的行数和列数\n",
    "        row_count = sheet.nrows\n",
    "        col_count = sheet.ncols\n",
    "        # 获取原有数据\n",
    "        data = []\n",
    "        for row in range(row_count):\n",
    "            row_data = []\n",
    "            for col in range(col_count):\n",
    "                row_data.append(sheet.cell_value(row, col))\n",
    "            data.append(row_data)\n",
    "        # 创建xlwt的Workbook对象\n",
    "        # 创建sheet\n",
    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
    "\n",
    "        # 将原有的数据写入新的sheet\n",
    "        for row in range(row_count):\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row, col, data[row][col])\n",
    "\n",
    "        if i == 0:\n",
    "            # 在新的sheet中添加数据\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row_count, col, append_rows[col])\n",
    "\n",
    "    # 保存新的xls文件\n",
    "    new_workbook.save(\"纯苯数据项.xls\")\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    pass\n",
    "    # 需要单独运行放开\n",
    "    #  start_1()\n",
    "\n",
    "    # 每天定时12点运行\n",
    "    # while True:\n",
    "    #     # 获取当前时间\n",
    "    #     current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
    "    #     current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n",
    "\n",
    "    #     # 判断当前时间是否为执行任务的时间点\n",
    "    #     if current_time == \"09:15:00\":\n",
    "    #         print(\"执行定时任务\")\n",
    "    #         start()\n",
    "\n",
    "    #     # 休眠1秒钟，避免过多占用CPU资源\n",
    "    #         time.sleep(1)\n",
    "        \n",
    "    #     elif current_time_1 == \"20:00:00\":\n",
    "    #         print(\"更新数据\")\n",
    "    #         start_1()\n",
    "    #     time.sleep(1)\n",
    "\n",
    "\n",
    "#     # 检测数据准确性, 需要检测放开\n",
    "#     # check_data(\"100028098|LISTING_PRICE\")\n",
    "#     # check_data(\"9137070016544622XB|DAY_Yield\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20250201\n",
      "{'dataDate': '20250201', 'dataItemNo': 'C01100047|TURNOVER'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.321e+05, tolerance: 4.074e+04\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: <object object at 0x0000028F59DBAF30>\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: ['datetime', '__version__', 'plot', 'random']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "\n",
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
      "2025-02-01    7738.433105\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250201', 'dataStatus': 'add', 'dataValue': 7738.43}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250202\n",
      "{'dataDate': '20250202', 'dataItemNo': 'C01100047|TURNOVER'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.506e+05, tolerance: 4.089e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
      "2025-02-02    7700.021484\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250202', 'dataStatus': 'add', 'dataValue': 7700.02}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250203\n",
      "{'dataDate': '20250203', 'dataItemNo': 'C01100047|TURNOVER'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.696e+05, tolerance: 4.096e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
      "2025-02-03    7693.463379\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250203', 'dataStatus': 'add', 'dataValue': 7693.46}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250204\n",
      "{'dataDate': '20250204', 'dataItemNo': 'C01100047|TURNOVER'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.780e+05, tolerance: 4.200e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7798.116211\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250204', 'dataStatus': 'add', 'dataValue': 7798.12}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250205\n",
      "{'dataDate': '20250205', 'dataItemNo': 'C01100047|TURNOVER'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.551e+05, tolerance: 4.144e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7865.974609\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250205', 'dataStatus': 'add', 'dataValue': 7865.97}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250206\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.553e+05, tolerance: 4.144e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7896.265137\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250206', 'dataStatus': 'add', 'dataValue': 7896.27}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250207\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.955e+05, tolerance: 4.168e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7841.537109\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250207', 'dataStatus': 'add', 'dataValue': 7841.54}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+05, tolerance: 4.197e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7814.474609\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250208', 'dataStatus': 'add', 'dataValue': 7814.47}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250209\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.675e+05, tolerance: 4.062e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7832.284668\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250209', 'dataStatus': 'add', 'dataValue': 7832.28}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250210\n",
      "{'dataDate': '20250210', 'dataItemNo': 'C01100047|TURNOVER'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.531e+05, tolerance: 4.103e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7917.837891\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250210', 'dataStatus': 'add', 'dataValue': 7917.84}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250211\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.307e+05, tolerance: 4.073e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7919.563965\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250211', 'dataStatus': 'add', 'dataValue': 7919.56}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250212\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+05, tolerance: 4.133e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7902.145508\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250212', 'dataStatus': 'add', 'dataValue': 7902.15}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250213\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.711e+05, tolerance: 4.105e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    8001.087891\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250213', 'dataStatus': 'add', 'dataValue': 8001.09}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250214\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.949e+05, tolerance: 4.129e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    8032.705566\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250214', 'dataStatus': 'add', 'dataValue': 8032.71}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250215\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.966e+05, tolerance: 4.129e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    8040.26709\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250215', 'dataStatus': 'add', 'dataValue': 8040.27}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250216\n",
      "{'dataDate': '20250216', 'dataItemNo': 'C01100047|TURNOVER'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.442e+05, tolerance: 4.138e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    8044.537109\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250216', 'dataStatus': 'add', 'dataValue': 8044.54}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250217\n",
      "{'dataDate': '20250217', 'dataItemNo': 'C01100047|TURNOVER'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.555e+05, tolerance: 4.072e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7998.32373\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250217', 'dataStatus': 'add', 'dataValue': 7998.32}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n",
      "20250218\n",
      "{'dataDate': '20250218', 'dataItemNo': 'C01100047|TURNOVER'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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",
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.680e+05, tolerance: 4.121e+04\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_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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    7988.078125\n",
      "Name: 日度预测价格, dtype: float32\n",
      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250218', 'dataStatus': 'add', 'dataValue': 7988.08}]}\n",
      "{\"confirmFlg\":false,\"status\":true}\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "start_date = datetime(2025, 2, 1)\n",
    "end_date = datetime(2025, 2, 19)\n",
    "\n",
    "while start_date < end_date:\n",
    "    print(start_date.strftime('%Y%m%d'))\n",
    "    start(start_date)\n",
    "    # time.sleep(2)\n",
    "    # start_1(start_date)\n",
    "    start_date += timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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