diff --git a/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl b/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl index e4b4f9d..8d0a951 100644 Binary files a/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl and b/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl differ diff --git a/aisenzhecode/液化石油气/液化气数据.xlsx b/aisenzhecode/液化石油气/液化气数据.xlsx index 9162c21..4ddef63 100644 Binary files a/aisenzhecode/液化石油气/液化气数据.xlsx and b/aisenzhecode/液化石油气/液化气数据.xlsx differ diff --git a/aisenzhecode/液化石油气/液化气每日价格预测.py b/aisenzhecode/液化石油气/液化气每日价格预测.py index 44aff4b..d878c0d 100644 --- a/aisenzhecode/液化石油气/液化气每日价格预测.py +++ b/aisenzhecode/液化石油气/液化气每日价格预测.py @@ -1,7 +1,34 @@ +from statsmodels.tools.eval_measures import mse, rmse +from pandas import Series, DataFrame +import cufflinks as cf +from sklearn.metrics import r2_score +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error +import pickle +import warnings +from sklearn.model_selection import GridSearchCV +from sklearn.metrics import mean_absolute_error +from xgboost import plot_importance, plot_tree +import xgboost as xgb +import plotly.graph_objects as go +import plotly.express as px +import statsmodels.api as sm +from xgboost import XGBRegressor +from sklearn.linear_model import Lasso +import sklearn.datasets as datasets +from sklearn import preprocessing +from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot +from plotly import __version__ +import time +import random +import seaborn as sn +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd import requests import json -from datetime import datetime,timedelta +from datetime import datetime, timedelta # 变量定义 login_url = "http://10.200.32.39/jingbo-api/api/server/login" @@ -38,60 +65,24 @@ read_file_path_name = "液化气数据.xlsx" one_cols = [] two_cols = [] -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import seaborn as sn -import random -import time -from plotly import __version__ -from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot +# 导入机器学习算法模型 -from sklearn import preprocessing - -from pandas import Series,DataFrame - -import matplotlib.pyplot as plt - -import sklearn.datasets as datasets - -#导入机器学习算法模型 -from sklearn.linear_model import Lasso -from xgboost import XGBRegressor - -import statsmodels.api as sm try: from keras.preprocessing.sequence import TimeseriesGenerator except: from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator -import plotly.express as px -import plotly.graph_objects as go -import xgboost as xgb -from xgboost import plot_importance, plot_tree -from sklearn.metrics import mean_absolute_error -from statsmodels.tools.eval_measures import mse,rmse -from sklearn.model_selection import GridSearchCV -from xgboost import XGBRegressor -import warnings -import pickle +# 切割训练数据和样本数据 -from sklearn.metrics import mean_squared_error - -#切割训练数据和样本数据 -from sklearn.model_selection import train_test_split - -#用于模型评分 -from sklearn.metrics import r2_score +# 用于模型评分 le = preprocessing.LabelEncoder() # print(__version__) # requires version >= 1.9.0 -import cufflinks as cf cf.go_offline() random.seed(100) @@ -104,14 +95,14 @@ def get_head_auth(): text = json.loads(login_res.text) if text["status"]: token = text["data"]["accessToken"] - print('获取的token:',token) + print('获取的token:', token) return token else: print("获取认证失败") return None -def get_data_value(token, dataItemNoList,date): +def get_data_value(token, dataItemNoList, date): search_data = { "data": { "date": date, @@ -120,23 +111,23 @@ def get_data_value(token, dataItemNoList,date): "funcModule": "数据项", "funcOperation": "查询" } - - + headers = {"Authorization": token} - search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5)) + search_res = requests.post( + url=search_url, headers=headers, json=search_data, timeout=(3, 5)) print('数据项查询参数search_data：') print(search_data) print('数据项查询结果search_res：') print(search_res.text) - + try: search_value = json.loads(search_res.text)["data"] - + print("数据项查询结果:", search_value) except json.JSONDecodeError as e: print(f"Error decoding JSON: {e}") print("Response content:", search_res.text) - return None + return None if search_value: return search_value else: @@ -145,7 +136,8 @@ def get_data_value(token, dataItemNoList,date): def get_head_push_auth(): - login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5)) + login_res = requests.post( + url=login_push_url, json=login_push_data, timeout=(3, 5)) text = json.loads(login_res.text) if text["status"]: token = text["data"]["accessToken"] @@ -155,8 +147,7 @@ def get_head_push_auth(): return None - -def upload_data_to_system(token_push,date): +def upload_data_to_system(token_push, date): data = { "funcModule": "数据表信息列表", "funcOperation": "新增", @@ -170,94 +161,96 @@ def upload_data_to_system(token_push,date): ] } headers = {"Authorization": token_push} - res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5)) + res = requests.post(url=upload_url, headers=headers, + json=data, timeout=(3, 5)) print(res.text) - print('预测值：',data['data'][0]['dataValue']) + print('预测值：', data['data'][0]['dataValue']) + - price_list = [] - + + def forecast_price(): # df_test = pd.read_csv('定价模型数据收集0212.csv') df_test = pd.read_excel('液化气数据.xlsx') - df_test.drop([0],inplace=True) + df_test.drop([0], inplace=True) try: - df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True) + df_test['Date'] = pd.to_datetime( + df_test['Date'], format='%m/%d/%Y', infer_datetime_format=True) except: - df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True) - + df_test['Date'] = pd.to_datetime( + df_test['Date'], format=r'%Y-%m-%d', infer_datetime_format=True) df_test_1 = df_test - df_test_1=df_test_1.fillna(df_test.ffill()) - df_test_1=df_test_1.fillna(df_test_1.bfill()) + df_test_1 = df_test_1.fillna(df_test.ffill()) + df_test_1 = df_test_1.fillna(df_test_1.bfill()) # 选择用于模型训练的列名称 col_for_training = df_test_1.columns - - - import joblib Best_model_DalyLGPrice = joblib.load("日度价格预测_液化气最佳模型.pkl") # 最新的一天为最后一行的数据 - + df_test_1_Day = df_test_1.tail(1) # 移除不需要的列 df_test_1_Day.index = df_test_1_Day["Date"] - df_test_1_Day = df_test_1_Day.drop(["Date"], axis= 1) - df_test_1_Day=df_test_1_Day.drop('Price',axis=1) - df_test_1_Day=df_test_1_Day.dropna() + df_test_1_Day = df_test_1_Day.drop(["Date"], axis=1) + df_test_1_Day = df_test_1_Day.drop('Price', axis=1) + df_test_1_Day = df_test_1_Day.dropna() for col in df_test_1_Day.columns: - df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col],errors='coerce') - #预测今日价格，显示至小数点后两位 - Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day) + df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col], errors='coerce') + # 预测今日价格，显示至小数点后两位 + Ypredict_Today = Best_model_DalyLGPrice.predict(df_test_1_Day) - df_test_1_Day['日度预测价格']=Ypredict_Today + df_test_1_Day['日度预测价格'] = Ypredict_Today print(df_test_1_Day['日度预测价格']) a = df_test_1_Day['日度预测价格'] a = a[0] a = float(a) - a = round(a,2) + a = round(a, 2) price_list.append(a) return a + + def optimize_Model(): from sklearn.model_selection import train_test_split - from sklearn.impute import SimpleImputer + from sklearn.impute import SimpleImputer from sklearn.preprocessing import OrdinalEncoder from sklearn.feature_selection import SelectFromModel from sklearn.metrics import mean_squared_error, r2_score import pandas as pd - pd.set_option('display.max_rows',40) - pd.set_option('display.max_columns',40) + pd.set_option('display.max_rows', 40) + pd.set_option('display.max_columns', 40) df_test = pd.read_excel('液化气数据.xlsx') - df_test.drop([0],inplace=True) + df_test.drop([0], inplace=True) try: - df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True) + df_test['Date'] = pd.to_datetime( + df_test['Date'], format='%m/%d/%Y', infer_datetime_format=True) except: - df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True) + df_test['Date'] = pd.to_datetime( + df_test['Date'], format=r'%Y-%m-%d', infer_datetime_format=True) - - #将缺失值补为前一个或者后一个数值 + # 将缺失值补为前一个或者后一个数值 df_test_1 = df_test - df_test_1=df_test_1.fillna(df_test.ffill()) - df_test_1=df_test_1.fillna(df_test_1.bfill()) + df_test_1 = df_test_1.fillna(df_test.ffill()) + df_test_1 = df_test_1.fillna(df_test_1.bfill()) df_test_1["Date"] = pd.to_datetime(df_test_1["Date"]) df_test_1.index = df_test_1["Date"] - df_test_1 = df_test_1.drop(["Date"], axis= 1) + df_test_1 = df_test_1.drop(["Date"], axis=1) df_test_1 = df_test_1.astype('float') - - + import numpy as np import pandas as pd - from pandas import Series,DataFrame + from pandas import Series, DataFrame import matplotlib.pyplot as plt import sklearn.datasets as datasets - #导入机器学习算法模型 + # 导入机器学习算法模型 from sklearn.linear_model import Lasso from xgboost import XGBRegressor @@ -273,7 +266,7 @@ def optimize_Model(): import xgboost as xgb from xgboost import plot_importance, plot_tree from sklearn.metrics import mean_absolute_error - from statsmodels.tools.eval_measures import mse,rmse + from statsmodels.tools.eval_measures import mse, rmse from sklearn.model_selection import GridSearchCV from xgboost import XGBRegressor import warnings @@ -281,63 +274,67 @@ def optimize_Model(): from sklearn.metrics import mean_squared_error - #切割训练数据和样本数据 + # 切割训练数据和样本数据 from sklearn.model_selection import train_test_split - #用于模型评分 + # 用于模型评分 from sklearn.metrics import r2_score - dataset1=df_test_1.drop('Price',axis=1)#.astype(float) + dataset1 = df_test_1.drop('Price', axis=1) # .astype(float) - y=df_test_1['Price'] + y = df_test_1['Price'] - x=dataset1 + x = dataset1 train = x target = y - #切割数据样本集合测试集 - X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0) + # 切割数据样本集合测试集 + X_train, x_test, y_train, y_true = train_test_split( + train, target, test_size=0.2, random_state=0) - #模型缩写 - Lasso = Lasso(random_state=0) + # 模型缩写 + Lasso = Lasso(random_state=0) XGBR = XGBRegressor(random_state=0) - #训练模型 - Lasso.fit(X_train,y_train) - XGBR.fit(X_train,y_train) - #模型拟合 + # 训练模型 + Lasso.fit(X_train, y_train) + XGBR.fit(X_train, y_train) + # 模型拟合 y_pre_Lasso = Lasso.predict(x_test) y_pre_XGBR = XGBR.predict(x_test) - #计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R² - Lasso_score = r2_score(y_true,y_pre_Lasso) - XGBR_score=r2_score(y_true,y_pre_XGBR) + # 计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R² + Lasso_score = r2_score(y_true, y_pre_Lasso) + XGBR_score = r2_score(y_true, y_pre_XGBR) - #计算Lasso、XGBR的MSE和RMSE - Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso) - XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR) + # 计算Lasso、XGBR的MSE和RMSE + Lasso_MSE = mean_squared_error(y_true, y_pre_Lasso) + XGBR_MSE = mean_squared_error(y_true, y_pre_XGBR) - Lasso_RMSE=np.sqrt(Lasso_MSE) - XGBR_RMSE=np.sqrt(XGBR_MSE) + Lasso_RMSE = np.sqrt(Lasso_MSE) + XGBR_RMSE = np.sqrt(XGBR_MSE) # 将不同模型的不同误差值整合成一个表格 model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score], ['XgBoost', XGBR_RMSE, XGBR_score]], - columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score']) - #将模型名称(Model)列设置为索引 - model_results1=model_results.set_index('模型(Model)') + columns=['模型(Model)', '均方根误差(RMSE)', 'R^2 score']) + # 将模型名称(Model)列设置为索引 + model_results1 = model_results.set_index('模型(Model)') model_results1 - #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 - def plot_feature_importance(importance,names,model_type): + # 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 + + def plot_feature_importance(importance, names, model_type): feature_importance = np.array(importance) feature_names = np.array(names) - data={'feature_names':feature_names,'feature_importance':feature_importance} + data = {'feature_names': feature_names, + 'feature_importance': feature_importance} fi_df = pd.DataFrame(data) - fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True) + fi_df.sort_values(by=['feature_importance'], + ascending=False, inplace=True) - plt.figure(figsize=(10,8)) + plt.figure(figsize=(10, 8)) sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names']) plt.title(model_type + " "+'FEATURE IMPORTANCE') @@ -345,55 +342,57 @@ def optimize_Model(): plt.ylabel('FEATURE NAMES') from pylab import mpl mpl.rcParams['font.sans-serif'] = ['SimHei'] - ## Xgboost 模型参数优化-初步 - #参考: https://juejin.im/post/6844903661013827598 - #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） + # Xgboost 模型参数优化-初步 + # 参考: https://juejin.im/post/6844903661013827598 + # 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） from xgboost import XGBRegressor from sklearn.model_selection import GridSearchCV estimator = XGBRegressor(random_state=0, - nthread=4, - seed=0 - ) + nthread=4, + seed=0 + ) parameters = { - 'max_depth': range (2, 11, 2), # 树的最大深度 - 'n_estimators': range (50, 101, 10), # 迭代次数 + 'max_depth': range(2, 11, 2), # 树的最大深度 + 'n_estimators': range(50, 101, 10), # 迭代次数 'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1] } grid_search_XGB = GridSearchCV( estimator=estimator, param_grid=parameters, - # n_jobs = 10, - cv = 3, + # n_jobs = 10, + cv=3, verbose=True ) grid_search_XGB.fit(X_train, y_train) - #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 + # 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 best_parameters = grid_search_XGB.best_estimator_.get_params() y_pred = grid_search_XGB.predict(x_test) - op_XGBR_score = r2_score(y_true,y_pred) - op_XGBR_MSE= mean_squared_error(y_true, y_pred) - op_XGBR_RMSE= np.sqrt(op_XGBR_MSE) + op_XGBR_score = r2_score(y_true, y_pred) + op_XGBR_MSE = mean_squared_error(y_true, y_pred) + op_XGBR_RMSE = np.sqrt(op_XGBR_MSE) model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]], - columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score']) - model_results2=model_results2.set_index('模型(Model)') + columns=['模型(Model)', '均方根误差(RMSE)', 'R^2 score']) + model_results2 = model_results2.set_index('模型(Model)') try: - results = model_results1.append(model_results2, ignore_index = False) + results = model_results1.append(model_results2, ignore_index=False) except: - results = pd.concat([model_results1,model_results2],ignore_index=True) + results = pd.concat( + [model_results1, model_results2], ignore_index=True) import pickle - Pkl_Filename = "日度价格预测_液化气最佳模型.pkl" + Pkl_Filename = "日度价格预测_液化气最佳模型.pkl" + + with open(Pkl_Filename, 'wb') as file: + pickle.dump(grid_search_XGB, file) - with open(Pkl_Filename, 'wb') as file: - pickle.dump(grid_search_XGB, file) def read_xls_data(): """获取特征项ID""" @@ -411,10 +410,10 @@ def start(date=''): token = get_head_auth() if not token: return - - cur_time,cur_time2 = getNow(date) + + cur_time, cur_time2 = getNow(date) print(f"获取{cur_time}数据") - datas = get_data_value(token, one_cols,date=cur_time) + datas = get_data_value(token, one_cols, date=cur_time) if not datas: return @@ -425,14 +424,15 @@ def start(date=''): print(data_value) dataItemNo_dataValue[data_value["dataItemNo"]] = "" else: - dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] - + dataItemNo_dataValue[data_value["dataItemNo"] + ] = data_value["dataValue"] + for value in one_cols: if value in dataItemNo_dataValue: append_rows.append(dataItemNo_dataValue[value]) else: append_rows.append("") - print('添加的行：',append_rows) + print('添加的行：', append_rows) save_xls_2(append_rows) @@ -448,7 +448,7 @@ def getNow(date='', offset=0): tuple: (紧凑日期字符串, 标准日期字符串) """ # 日期解析逻辑 - from datetime import datetime,timedelta + from datetime import datetime, timedelta if isinstance(date, datetime): now = date else: @@ -466,22 +466,23 @@ def getNow(date='', offset=0): # 应用日期偏移 now = now - timedelta(days=offset) - + # 统一格式化输出 date_str = now.strftime("%Y-%m-%d") compact_date = date_str.replace("-", "") return compact_date, date_str + def start_1(date=''): """补充昨日数据""" read_xls_data() token = get_head_auth() if not token: return - - cur_time,cur_time2 = getNow(date,offset=1) + + cur_time, cur_time2 = getNow(date, offset=1) print(f"补充{cur_time}数据") - datas = get_data_value(token, one_cols,date=cur_time) + datas = get_data_value(token, one_cols, date=cur_time) if not datas: print(f"{cur_time}没有数据") return @@ -493,14 +494,15 @@ def start_1(date=''): print(data_value) dataItemNo_dataValue[data_value["dataItemNo"]] = "" else: - dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] - + dataItemNo_dataValue[data_value["dataItemNo"] + ] = data_value["dataValue"] + for value in one_cols: if value in dataItemNo_dataValue: append_rows.append(dataItemNo_dataValue[value]) else: append_rows.append("") - print('添加的行：',append_rows) + print('添加的行：', append_rows) save_xls_2(append_rows) @@ -513,10 +515,10 @@ def save_xls_2(append_rows): # 读取现有数据（假设第一行为列名） df = pd.read_excel('液化气数据.xlsx', sheet_name=0) # 转换append_rows为DataFrame - append_rows = pd.DataFrame([append_rows],columns=df.columns) + append_rows = pd.DataFrame([append_rows], columns=df.columns) # 创建新数据行 new_date = append_rows['Date'].values[0] - + dates = df['Date'].to_list() # 判断日期是否存在 if new_date in dates: @@ -531,16 +533,18 @@ def save_xls_2(append_rows): print(df.head()) print(df.tail()) print(f"插入 {new_date} 新数据") - + # 保存更新后的数据 df.to_excel('液化气数据.xlsx', index=False, engine='openpyxl') - + except FileNotFoundError: # 如果文件不存在则创建新文件 - pd.DataFrame([append_rows]).to_excel('液化气数据.xlsx', index=False, engine='openpyxl') + pd.DataFrame([append_rows]).to_excel( + '液化气数据.xlsx', index=False, engine='openpyxl') except Exception as e: print(f"保存数据时发生错误: {str(e)}") + def check_data(dataItemNo): token = get_head_auth() if not token: @@ -550,6 +554,7 @@ def check_data(dataItemNo): if not datas: return + def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd): search_data = { @@ -563,14 +568,16 @@ def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEn } headers = {"Authorization": token} - search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5)) + search_res = requests.post( + url=url, headers=headers, json=search_data, timeout=(3, 5)) search_value = json.loads(search_res.text)["data"] if search_value: return search_value else: return None -def save_queryDataListItemNos_xls(data_df,dataItemNoList): + +def save_queryDataListItemNos_xls(data_df, dataItemNoList): from datetime import datetime, timedelta current_year_month = datetime.now().strftime('%Y-%m') grouped = data_df.groupby("dataDate") @@ -581,11 +588,11 @@ def save_queryDataListItemNos_xls(data_df,dataItemNoList): # 创建新工作簿 new_workbook = load_workbook('液化气数据.xlsx') - + for sheetname in workbook.sheetnames: sheet = workbook[sheetname] new_sheet = new_workbook[sheetname] - + current_year_month_row = 0 # 查找当前月份数据起始行 for row_idx, row in enumerate(sheet.iter_rows(values_only=True), 1): @@ -599,14 +606,14 @@ def save_queryDataListItemNos_xls(data_df,dataItemNoList): new_sheet.cell(row=row_idx, column=1, value=date) for j, dataItemNo in enumerate(dataItemNoList, start=2): if group[group["dataItemNo"] == dataItemNo]["dataValue"].values: - new_sheet.cell(row=row_idx, column=j, - value=group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0]) + new_sheet.cell(row=row_idx, column=j, + value=group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0]) # 保存修改后的xlsx文件 new_workbook.save("液化气数据.xlsx") -def queryDataListItemNos(date=None,token=None): +def queryDataListItemNos(date=None, token=None): df = pd.read_excel('液化气数据.xlsx') dataItemNoList = df.iloc[0].tolist()[1:] if token is None: @@ -624,17 +631,17 @@ def queryDataListItemNos(date=None,token=None): # 格式化为 YYYYMMDD 格式 dateEnd = current_date.strftime('%Y%m%d') dateStart = first_day_of_month.strftime('%Y%m%d') - search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd) + # dateStart = '20250604' + search_value = get_queryDataListItemNos_value( + token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd) data_df = pd.DataFrame(search_value) data_df["dataDate"] = pd.to_datetime(data_df["dataDate"]) data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d') - save_queryDataListItemNos_xls(data_df,dataItemNoList) + save_queryDataListItemNos_xls(data_df, dataItemNoList) print('当月数据更新完成') - - -def main(start_date=None,token=None,token_push=None): +def main(start_date=None, token=None, token_push=None): from datetime import datetime, timedelta if start_date is None: start_date = datetime.now() @@ -646,7 +653,7 @@ def main(start_date=None,token=None,token_push=None): print(date) try: # 更新当月数据 - queryDataListItemNos(start_date,token) + queryDataListItemNos(start_date, token) except: print('当月数据更新失败,单日更新') start(date) @@ -654,10 +661,18 @@ def main(start_date=None,token=None,token_push=None): # start(date) # 训练模型 optimize_Model() - # # 预测&上传预测结果 - upload_data_to_system(token_push,start_date) + # 预测&上传预测结果 + upload_data_to_system(token_push, start_date) + # forecast_price() if __name__ == "__main__": print("运行中ing...") - main() + # 遍历2024-11-25 到 2024-12-3 之间的工作日日期 + # for i_time in pd.date_range('2025-7-1', '2025-7-8', freq='D'): + # # try: + # print(i_time) + # main(start_date=i_time) + # except Exception as e: + # continue + main()