聚烯烃基础数据表更改
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workpc
parent
98836d7a4d
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@ -950,14 +950,14 @@ def main(start_date=None, token=None, token_push=None):
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date = start_date.strftime('%Y%m%d')
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print(date)
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logging.info("当前日期: %s", date)
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updateExcelData(date, token)
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# updateExcelData(date, token)
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queryDataListItemNos(token=token)
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update_e_value('定性模型数据项12-11.xlsx', 8, 1000)
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x = qualitativeModel()
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if x is not None:
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print('**************************************************预测结果：', x)
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logging.info("预测结果: %s", x)
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cur_time, cur_time2 = getNow(date)
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# cur_time, cur_time2 = getNow(date)
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pushData(cur_time, x, token_push)
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logging.info("主函数执行完成")
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except Exception as e:
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346
config_jingbo.py
346
config_jingbo.py
@ -93,163 +93,24 @@ data = {
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ClassifyId = 1214
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# 变量定义--线上环境
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server_host = '10.200.32.39'
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login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
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# 上传数据项值
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push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
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# 上传停更数据到市场信息平台
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push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# 获取预警数据中取消订阅指标ID
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get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
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login_data = {
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"data": {
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"account": "api_dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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upload_data = {
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"funcModule": '研究报告信息',
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"funcOperation": '上传原油价格预测报告',
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"data": {
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"groupNo": '', # 用户组id
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"ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
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"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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"fileName": '', # 文件名称
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"fileBase64": '', # 文件内容base64
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"categoryNo": 'yyjgycbg', # 研究报告分类编码
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"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
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"reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
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"reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
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"productGroupCode": "RAW_MATERIAL" # 商品分类
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}
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}
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warning_data = {
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"groupNo": '', # 用户组id
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"funcModule": '原油特征停更预警',
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"funcOperation": '原油特征停更预警',
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"data": {
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'WARNING_TYPE_NAME': '特征数据停更预警',
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'WARNING_CONTENT': '',
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'WARNING_DATE': ''
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}
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}
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query_data_list_item_nos_data = {
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"funcModule": "数据项",
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"funcOperation": "查询",
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"data": {
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"dateStart": "20200101",
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"dateEnd": "20241231",
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"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
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}
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}
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push_data_value_list_data = {
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"funcModule": "数据表信息列表",
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"funcOperation": "新增",
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"data": [
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{"dataItemNo": "91230600716676129",
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"dataDate": "20230113",
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"dataStatus": "add",
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"dataValue": 100.11
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},
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{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
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"dataDate": "20230113",
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"dataStatus": "add",
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"dataValue": 100.55
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},
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{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
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"dataDate": "20230113",
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"dataStatus": "add",
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"dataValue": 100.55
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}
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]
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}
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push_waring_data_value_list_data = {
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"data": {
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"crudeOilWarningDtoList": [
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{
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"lastUpdateDate": "20240501",
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"updateSuspensionCycle": 1,
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"dataSource": "8",
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"frequency": "1",
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"indicatorName": "美元指数",
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"indicatorId": "myzs001",
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"warningDate": "2024-05-13"
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}
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],
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"dataSource": "8"
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},
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"funcModule": "商品数据同步",
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"funcOperation": "同步"
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}
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get_waring_data_value_list_data = {
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"data": "8", "funcModule": "商品数据同步", "funcOperation": "同步"}
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# 八大维度数据项编码
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bdwd_items = {
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'ciri': '原油大数据预测|FORECAST|PRICE|T',
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'benzhou': '原油大数据预测|FORECAST|PRICE|W',
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'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
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'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
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'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
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'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
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'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
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'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
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}
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# 生产环境数据库
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host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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port = 3306
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dbusername = 'jingbo'
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password = 'shihua@123'
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dbname = 'jingbo'
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table_name = 'v_tbl_crude_oil_warning'
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# # # 变量定义--测试环境
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# server_host = '192.168.100.53' # 内网
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# # server_host = '183.242.74.28' # 外网
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# login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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# # 上传报告
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# upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# # 停更预警
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# upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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# # 查询数据项编码
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# query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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# # 变量定义--线上环境
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# server_host = '10.200.32.39'
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# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
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# # 上传数据项值
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# push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
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# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
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# # 上传停更数据到市场信息平台
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# push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# # 获取预警数据中取消订阅指标ID
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# get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
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# get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
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# login_data = {
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# "data": {
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# "account": "api_test",
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# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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# "account": "api_dev",
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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# "terminal": "API"
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# },
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@ -257,25 +118,24 @@ table_name = 'v_tbl_crude_oil_warning'
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# "funcOperation": "获取token"
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# }
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# upload_data = {
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# "groupNo": '', # 用户组id
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# "funcModule": '研究报告信息',
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# "funcOperation": '上传原油价格预测报告',
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# "data": {
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# "ownerAccount": 'arui', # 报告所属用户账号
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# "groupNo": '', # 用户组id
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# "ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
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# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
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# "fileName": '', # 文件名称
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# "fileBase64": '', # 文件内容base64
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# "categoryNo": 'yyjgycbg', # 研究报告分类编码
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# "smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
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# "reportEmployeeCode": "E40116", # 报告人
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# "reportDeptCode": "D0044", # 报告部门
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# "reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
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# "reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
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# "productGroupCode": "RAW_MATERIAL" # 商品分类
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# }
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# }
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# # 已弃用
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# warning_data = {
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# "groupNo": '', # 用户组id
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# "funcModule": '原油特征停更预警',
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@ -297,6 +157,7 @@ table_name = 'v_tbl_crude_oil_warning'
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# }
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# }
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# push_data_value_list_data = {
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# "funcModule": "数据表信息列表",
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# "funcOperation": "新增",
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@ -319,6 +180,7 @@ table_name = 'v_tbl_crude_oil_warning'
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# ]
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# }
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# push_waring_data_value_list_data = {
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# "data": {
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# "crudeOilWarningDtoList": [
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@ -342,27 +204,165 @@ table_name = 'v_tbl_crude_oil_warning'
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# get_waring_data_value_list_data = {
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# "data": "8", "funcModule": "商品数据同步", "funcOperation": "同步"}
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# # 八大维度数据项编码
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# bdwd_items = {
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# 'ciri': 'yyycbdwdcr',
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# 'benzhou': 'yyycbdwdbz',
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# 'cizhou': 'yyycbdwdcz',
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# 'gezhou': 'yyycbdwdgz',
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# 'ciyue': 'yyycbdwdcy',
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# 'cieryue': 'yyycbdwdcey',
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# 'cisanyue': 'yyycbdwdcsy',
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# 'cisiyue': 'yyycbdwdcsiy',
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# 'ciri': '原油大数据预测|FORECAST|PRICE|T',
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# 'benzhou': '原油大数据预测|FORECAST|PRICE|W',
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# 'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
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# 'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
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# 'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
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# 'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
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# 'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
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# 'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
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# }
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# # 北京环境数据库
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# host = '192.168.101.27'
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# # 生产环境数据库
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# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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# port = 3306
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# dbusername = 'root'
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# password = '123456'
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# dbname = 'jingbo_test'
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# dbusername = 'jingbo'
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# password = 'shihua@123'
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# dbname = 'jingbo'
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# table_name = 'v_tbl_crude_oil_warning'
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# # 变量定义--测试环境
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server_host = '192.168.100.53' # 内网
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# server_host = '183.242.74.28' # 外网
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login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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# 上传报告
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upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# 停更预警
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upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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# 查询数据项编码
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query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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# 上传数据项值
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push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
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# 上传停更数据到市场信息平台
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push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# 获取预警数据中取消订阅指标ID
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get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
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login_data = {
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"data": {
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"account": "api_test",
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# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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upload_data = {
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"groupNo": '', # 用户组id
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"funcModule": '研究报告信息',
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"funcOperation": '上传原油价格预测报告',
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"data": {
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"ownerAccount": 'arui', # 报告所属用户账号
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"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
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"fileBase64": '', # 文件内容base64
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"categoryNo": 'yyjgycbg', # 研究报告分类编码
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"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
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"reportEmployeeCode": "E40116", # 报告人
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"reportDeptCode": "D0044", # 报告部门
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"productGroupCode": "RAW_MATERIAL" # 商品分类
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}
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}
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# 已弃用
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warning_data = {
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"groupNo": '', # 用户组id
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"funcModule": '原油特征停更预警',
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"funcOperation": '原油特征停更预警',
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"data": {
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'WARNING_TYPE_NAME': '特征数据停更预警',
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'WARNING_CONTENT': '',
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'WARNING_DATE': ''
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}
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}
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query_data_list_item_nos_data = {
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"funcModule": "数据项",
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"funcOperation": "查询",
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"data": {
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"dateStart": "20200101",
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"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "91230600716676129",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.11
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
push_waring_data_value_list_data = {
|
||||
"data": {
|
||||
"crudeOilWarningDtoList": [
|
||||
{
|
||||
"lastUpdateDate": "20240501",
|
||||
"updateSuspensionCycle": 1,
|
||||
"dataSource": "8",
|
||||
"frequency": "1",
|
||||
"indicatorName": "美元指数",
|
||||
"indicatorId": "myzs001",
|
||||
"warningDate": "2024-05-13"
|
||||
}
|
||||
],
|
||||
"dataSource": "8"
|
||||
},
|
||||
"funcModule": "商品数据同步",
|
||||
"funcOperation": "同步"
|
||||
}
|
||||
|
||||
|
||||
get_waring_data_value_list_data = {
|
||||
"data": "8", "funcModule": "商品数据同步", "funcOperation": "同步"}
|
||||
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': 'yyycbdwdcr',
|
||||
'benzhou': 'yyycbdwdbz',
|
||||
'cizhou': 'yyycbdwdcz',
|
||||
'gezhou': 'yyycbdwdgz',
|
||||
'ciyue': 'yyycbdwdcy',
|
||||
'cieryue': 'yyycbdwdcey',
|
||||
'cisanyue': 'yyycbdwdcsy',
|
||||
'cisiyue': 'yyycbdwdcsiy',
|
||||
}
|
||||
|
||||
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
'feature_factor_frequency': 'D',
|
||||
'strategy_id': 1,
|
||||
@ -380,14 +380,14 @@ DEFAULT_CONFIG = {
|
||||
# 开关
|
||||
is_train = True # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = True # 是否使用eta接口
|
||||
is_eta = False # 是否使用eta接口
|
||||
is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = True # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_eta = False # 预测结果上传到eta
|
||||
is_update_report = False # 是否上传报告
|
||||
is_update_warning_data = True # 是否上传预警数据
|
||||
is_update_predict_value = True # 是否上传预测值到市场信息平台
|
||||
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
|
||||
@ -172,131 +172,19 @@ data = {
|
||||
ClassifyId = 1214
|
||||
|
||||
|
||||
# 变量定义--线上环境
|
||||
server_host = '10.200.32.39'
|
||||
login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
|
||||
upload_data = {
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传原油价格预测报告',
|
||||
"data": {
|
||||
"groupNo": '', # 用户组id
|
||||
"ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '', # 文件名称
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
"reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
warning_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '原油特征停更预警',
|
||||
"funcOperation": '原油特征停更预警',
|
||||
"data": {
|
||||
'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
'WARNING_CONTENT': '',
|
||||
'WARNING_DATE': ''
|
||||
}
|
||||
}
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": "20200101",
|
||||
"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "91230600716676129",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.11
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
}
|
||||
]
|
||||
}
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': '原油大数据预测|FORECAST|PRICE|T',
|
||||
'benzhou': '原油大数据预测|FORECAST|PRICE|W',
|
||||
'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
|
||||
'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
|
||||
'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
|
||||
'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
|
||||
'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
|
||||
'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
|
||||
}
|
||||
|
||||
# 报告中八大维度数据项重命名
|
||||
columnsrename = {
|
||||
'原油大数据预测|FORECAST|PRICE|T': '次日', '原油大数据预测|FORECAST|PRICE|W': '本周',
|
||||
'原油大数据预测|FORECAST|PRICE|W_1': '次周', '原油大数据预测|FORECAST|PRICE|W_2': '隔周',
|
||||
'原油大数据预测|FORECAST|PRICE|M_1': '次月', '原油大数据预测|FORECAST|PRICE|M_2': '次二月',
|
||||
'原油大数据预测|FORECAST|PRICE|M_3': '次三月', '原油大数据预测|FORECAST|PRICE|M_4': '次四月'
|
||||
}
|
||||
|
||||
# 生产环境数据库
|
||||
host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
port = 3306
|
||||
dbusername = 'jingbo'
|
||||
password = 'shihua@123'
|
||||
dbname = 'jingbo'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# # 变量定义--测试环境
|
||||
# server_host = '192.168.100.53:8080' # 内网
|
||||
# # server_host = '183.242.74.28' # 外网
|
||||
# login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
|
||||
# upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
# query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# # 变量定义--线上环境
|
||||
# server_host = '10.200.32.39'
|
||||
# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# # 上传数据项值
|
||||
# push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
# login_data = {
|
||||
# "data": {
|
||||
# "account": "api_test",
|
||||
# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
# "account": "api_dev",
|
||||
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
# "terminal": "API"
|
||||
# },
|
||||
@ -304,24 +192,24 @@ table_name = 'v_tbl_crude_oil_warning'
|
||||
# "funcOperation": "获取token"
|
||||
# }
|
||||
|
||||
|
||||
# upload_data = {
|
||||
# "groupNo": '', # 用户组id
|
||||
# "funcModule": '研究报告信息',
|
||||
# "funcOperation": '上传原油价格预测报告',
|
||||
# "data": {
|
||||
# "ownerAccount": 'arui', # 报告所属用户账号
|
||||
# "groupNo": '', # 用户组id
|
||||
# "ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
|
||||
# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
# "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
# "fileName": '', # 文件名称
|
||||
# "fileBase64": '', # 文件内容base64
|
||||
# "categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
# "smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
# "reportEmployeeCode": "E40116", # 报告人
|
||||
# "reportDeptCode": "D0044", # 报告部门
|
||||
# "reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
# "reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
|
||||
# "productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
# }
|
||||
# }
|
||||
|
||||
|
||||
# warning_data = {
|
||||
# "groupNo": '', # 用户组id
|
||||
# "funcModule": '原油特征停更预警',
|
||||
@ -343,6 +231,7 @@ table_name = 'v_tbl_crude_oil_warning'
|
||||
# }
|
||||
# }
|
||||
|
||||
|
||||
# push_data_value_list_data = {
|
||||
# "funcModule": "数据表信息列表",
|
||||
# "funcOperation": "新增",
|
||||
@ -366,27 +255,138 @@ table_name = 'v_tbl_crude_oil_warning'
|
||||
# }
|
||||
# # 八大维度数据项编码
|
||||
# bdwd_items = {
|
||||
# 'ciri': 'yyycbdwdcr',
|
||||
# 'benzhou': 'yyycbdwdbz',
|
||||
# 'cizhou': 'yyycbdwdcz',
|
||||
# 'gezhou': 'yyycbdwdgz',
|
||||
# 'ciyue': 'yyycbdwdcy',
|
||||
# 'cieryue': 'yyycbdwdcey',
|
||||
# 'cisanyue': 'yyycbdwdcsy',
|
||||
# 'cisiyue': 'yyycbdwdcsiy',
|
||||
# 'ciri': '原油大数据预测|FORECAST|PRICE|T',
|
||||
# 'benzhou': '原油大数据预测|FORECAST|PRICE|W',
|
||||
# 'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
|
||||
# 'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
|
||||
# 'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
|
||||
# 'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
|
||||
# 'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
|
||||
# 'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
|
||||
# }
|
||||
|
||||
# # 报告中八大维度数据项重命名
|
||||
# columnsrename = {'yyycbdwdbz': '本周', 'yyycbdwdcey': '次二月', 'yyycbdwdcr': '次日', 'yyycbdwdcsiy': '次四月',
|
||||
# 'yyycbdwdcsy': '次三月', 'yyycbdwdcy': '次月', 'yyycbdwdcz': '次周', 'yyycbdwdgz': '隔周', }
|
||||
# # 北京环境数据库
|
||||
# host = '192.168.101.27'
|
||||
# columnsrename = {
|
||||
# '原油大数据预测|FORECAST|PRICE|T': '次日', '原油大数据预测|FORECAST|PRICE|W': '本周',
|
||||
# '原油大数据预测|FORECAST|PRICE|W_1': '次周', '原油大数据预测|FORECAST|PRICE|W_2': '隔周',
|
||||
# '原油大数据预测|FORECAST|PRICE|M_1': '次月', '原油大数据预测|FORECAST|PRICE|M_2': '次二月',
|
||||
# '原油大数据预测|FORECAST|PRICE|M_3': '次三月', '原油大数据预测|FORECAST|PRICE|M_4': '次四月'
|
||||
# }
|
||||
|
||||
# # 生产环境数据库
|
||||
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
# port = 3306
|
||||
# dbusername = 'root'
|
||||
# password = '123456'
|
||||
# dbname = 'jingbo_test'
|
||||
# dbusername = 'jingbo'
|
||||
# password = 'shihua@123'
|
||||
# dbname = 'jingbo'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# 变量定义--测试环境
|
||||
server_host = '192.168.100.53:8080' # 内网
|
||||
# server_host = '183.242.74.28' # 外网
|
||||
login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
|
||||
upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_test",
|
||||
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
upload_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传原油价格预测报告',
|
||||
"data": {
|
||||
"ownerAccount": 'arui', # 报告所属用户账号
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40116", # 报告人
|
||||
"reportDeptCode": "D0044", # 报告部门
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
warning_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '原油特征停更预警',
|
||||
"funcOperation": '原油特征停更预警',
|
||||
"data": {
|
||||
'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
'WARNING_CONTENT': '',
|
||||
'WARNING_DATE': ''
|
||||
}
|
||||
}
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": "20200101",
|
||||
"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "91230600716676129",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.11
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
}
|
||||
]
|
||||
}
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': 'yyycbdwdcr',
|
||||
'benzhou': 'yyycbdwdbz',
|
||||
'cizhou': 'yyycbdwdcz',
|
||||
'gezhou': 'yyycbdwdgz',
|
||||
'ciyue': 'yyycbdwdcy',
|
||||
'cieryue': 'yyycbdwdcey',
|
||||
'cisanyue': 'yyycbdwdcsy',
|
||||
'cisiyue': 'yyycbdwdcsiy',
|
||||
}
|
||||
|
||||
# 报告中八大维度数据项重命名
|
||||
columnsrename = {'yyycbdwdbz': '本周', 'yyycbdwdcey': '次二月', 'yyycbdwdcr': '次日', 'yyycbdwdcsiy': '次四月',
|
||||
'yyycbdwdcsy': '次三月', 'yyycbdwdcy': '次月', 'yyycbdwdcz': '次周', 'yyycbdwdgz': '隔周', }
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
'feature_factor_frequency': 'D',
|
||||
'strategy_id': 1,
|
||||
@ -404,14 +404,14 @@ DEFAULT_CONFIG = {
|
||||
# 开关
|
||||
is_train = True # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = True # 是否使用eta接口
|
||||
is_eta = False # 是否使用eta接口
|
||||
is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = True # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_report = False # 是否上传报告
|
||||
is_update_warning_data = False # 是否上传预警数据
|
||||
is_update_predict_value = True # 是否上传预测值到市场信息平台
|
||||
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
|
||||
@ -119,125 +119,19 @@ data = {
|
||||
ClassifyId = 1214
|
||||
|
||||
|
||||
# 变量定义--线上环境
|
||||
server_host = '10.200.32.39'
|
||||
login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
|
||||
upload_data = {
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传原油价格预测报告',
|
||||
"data": {
|
||||
"groupNo": '', # 用户组id
|
||||
"ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '', # 文件名称
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
"reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
warning_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '原油特征停更预警',
|
||||
"funcOperation": '原油特征停更预警',
|
||||
"data": {
|
||||
'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
'WARNING_CONTENT': '',
|
||||
'WARNING_DATE': ''
|
||||
}
|
||||
}
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": "20200101",
|
||||
"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "91230600716676129",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.11
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
}
|
||||
]
|
||||
}
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': '原油大数据预测|FORECAST|PRICE|T',
|
||||
'benzhou': '原油大数据预测|FORECAST|PRICE|W',
|
||||
'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
|
||||
'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
|
||||
'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
|
||||
'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
|
||||
'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
|
||||
'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
|
||||
}
|
||||
|
||||
|
||||
# 生产环境数据库
|
||||
host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
port = 3306
|
||||
dbusername = 'jingbo'
|
||||
password = 'shihua@123'
|
||||
dbname = 'jingbo'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# # # 变量定义--测试环境
|
||||
# server_host = '192.168.100.53:8080' # 内网
|
||||
# # server_host = '183.242.74.28' # 外网
|
||||
|
||||
# login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
|
||||
# upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
# query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# # 变量定义--线上环境
|
||||
# server_host = '10.200.32.39'
|
||||
# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# # 上传数据项值
|
||||
# push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
# login_data = {
|
||||
# "data": {
|
||||
# "account": "api_test",
|
||||
# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
# "account": "api_dev",
|
||||
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
# "terminal": "API"
|
||||
# },
|
||||
@ -245,24 +139,26 @@ table_name = 'v_tbl_crude_oil_warning'
|
||||
# "funcOperation": "获取token"
|
||||
# }
|
||||
|
||||
|
||||
# upload_data = {
|
||||
# "funcModule": '研究报告信息',
|
||||
# "funcOperation": '上传原油价格预测报告',
|
||||
# "data": {
|
||||
# "ownerAccount": 'arui', # 报告所属用户账号
|
||||
# "groupNo": '', # 用户组id
|
||||
# "ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
|
||||
# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
# "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
# "fileName": '', # 文件名称
|
||||
# "fileBase64": '', # 文件内容base64
|
||||
# "categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
# "smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
# "reportEmployeeCode": "E40116", # 报告人
|
||||
# "reportDeptCode": "D0044", # 报告部门
|
||||
# "reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
# "reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
|
||||
# "productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
# }
|
||||
# }
|
||||
|
||||
|
||||
# warning_data = {
|
||||
# "groupNo": '', # 用户组id
|
||||
# "funcModule": '原油特征停更预警',
|
||||
# "funcOperation": '原油特征停更预警',
|
||||
# "data": {
|
||||
@ -282,6 +178,7 @@ table_name = 'v_tbl_crude_oil_warning'
|
||||
# }
|
||||
# }
|
||||
|
||||
|
||||
# push_data_value_list_data = {
|
||||
# "funcModule": "数据表信息列表",
|
||||
# "funcOperation": "新增",
|
||||
@ -305,26 +202,129 @@ table_name = 'v_tbl_crude_oil_warning'
|
||||
# }
|
||||
# # 八大维度数据项编码
|
||||
# bdwd_items = {
|
||||
# 'ciri': 'yyycbdwdcr',
|
||||
# 'benzhou': 'yyycbdwdbz',
|
||||
# 'cizhou': 'yyycbdwdcz',
|
||||
# 'gezhou': 'yyycbdwdgz',
|
||||
# 'ciyue': 'yyycbdwdcy',
|
||||
# 'cieryue': 'yyycbdwdcey',
|
||||
# 'cisanyue': 'yyycbdwdcsy',
|
||||
# 'cisiyue': 'yyycbdwdcsiy',
|
||||
# 'ciri': '原油大数据预测|FORECAST|PRICE|T',
|
||||
# 'benzhou': '原油大数据预测|FORECAST|PRICE|W',
|
||||
# 'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
|
||||
# 'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
|
||||
# 'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
|
||||
# 'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
|
||||
# 'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
|
||||
# 'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
|
||||
# }
|
||||
|
||||
|
||||
# # 北京环境数据库
|
||||
# host = '192.168.101.27'
|
||||
# # 生产环境数据库
|
||||
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
# port = 3306
|
||||
# dbusername = 'root'
|
||||
# password = '123456'
|
||||
# dbname = 'jingbo_test'
|
||||
# dbusername = 'jingbo'
|
||||
# password = 'shihua@123'
|
||||
# dbname = 'jingbo'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# # 变量定义--测试环境
|
||||
server_host = '192.168.100.53:8080' # 内网
|
||||
# server_host = '183.242.74.28' # 外网
|
||||
|
||||
login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
|
||||
upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_test",
|
||||
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
upload_data = {
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传原油价格预测报告',
|
||||
"data": {
|
||||
"ownerAccount": 'arui', # 报告所属用户账号
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40116", # 报告人
|
||||
"reportDeptCode": "D0044", # 报告部门
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
warning_data = {
|
||||
"funcModule": '原油特征停更预警',
|
||||
"funcOperation": '原油特征停更预警',
|
||||
"data": {
|
||||
'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
'WARNING_CONTENT': '',
|
||||
'WARNING_DATE': ''
|
||||
}
|
||||
}
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": "20200101",
|
||||
"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "91230600716676129",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.11
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
}
|
||||
]
|
||||
}
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': 'yyycbdwdcr',
|
||||
'benzhou': 'yyycbdwdbz',
|
||||
'cizhou': 'yyycbdwdcz',
|
||||
'gezhou': 'yyycbdwdgz',
|
||||
'ciyue': 'yyycbdwdcy',
|
||||
'cieryue': 'yyycbdwdcey',
|
||||
'cisanyue': 'yyycbdwdcsy',
|
||||
'cisiyue': 'yyycbdwdcsiy',
|
||||
}
|
||||
|
||||
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
'feature_factor_frequency': 'D',
|
||||
'strategy_id': 1,
|
||||
@ -342,14 +342,14 @@ DEFAULT_CONFIG = {
|
||||
# 开关
|
||||
is_train = True # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = True # 是否使用eta接口
|
||||
is_eta = False # 是否使用eta接口
|
||||
is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = True # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_report = False # 是否上传报告
|
||||
is_update_warning_data = False # 是否上传预警数据
|
||||
is_update_predict_value = True # 是否上传预测值到市场信息平台
|
||||
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
|
||||
@ -432,8 +432,8 @@ is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = True # 预测结果上传到eta
|
||||
is_update_report = False # 是否上传报告
|
||||
is_update_eta = False # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_warning_data = True # 是否上传预警数据
|
||||
is_update_predict_value = True # 是否上传预测值到市场信息平台
|
||||
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
from decimal import Decimal
|
||||
import logging
|
||||
import os
|
||||
import logging.handlers
|
||||
@ -416,18 +417,32 @@ password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
'feature_factor_frequency': 'D',
|
||||
'strategy_id': 2,
|
||||
'model_evaluation_id': 1,
|
||||
'tenant_code': '',
|
||||
'version_num': Decimal(1),
|
||||
'delete_flag': '0',
|
||||
'create_user': 'admin',
|
||||
'create_date': datetime.datetime.now(),
|
||||
'update_user': 'admin',
|
||||
'update_date': datetime.datetime.now(),
|
||||
'oil_code': 'PP',
|
||||
'oil_name': 'PP期货',
|
||||
}
|
||||
|
||||
# 开关
|
||||
is_train = True # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = True # 是否使用eta接口
|
||||
is_eta = False # 是否使用eta接口
|
||||
is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = True # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_report = False # 是否上传报告
|
||||
is_update_warning_data = False # 是否上传预警数据
|
||||
is_update_predict_value = True # 是否上传预测值到市场信息平台
|
||||
is_del_corr = 0 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
@ -452,16 +467,20 @@ add_kdj = False # 是否添加kdj指标
|
||||
if add_kdj and is_edbnamelist:
|
||||
edbnamelist = edbnamelist+['K', 'D', 'J']
|
||||
|
||||
|
||||
# 模型参数
|
||||
y = 'AVG-金能大唐久泰青州'
|
||||
avg_cols = [
|
||||
'PP：拉丝：1102K：出厂价：青州：国家能源宁煤（日）',
|
||||
'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
|
||||
'PP：拉丝：L5E89：出厂价：河北、鲁北：大唐内蒙多伦（日）',
|
||||
'PP：拉丝：HP550J：市场价：青岛：金能化学（日）'
|
||||
]
|
||||
offsite = 80
|
||||
offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
|
||||
# y = 'AVG-金能大唐久泰青州'
|
||||
# avg_cols = [
|
||||
# 'PP：拉丝：1102K：出厂价：青州：国家能源宁煤（日）',
|
||||
# 'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
|
||||
# 'PP：拉丝：L5E89：出厂价：河北、鲁北：大唐内蒙多伦（日）',
|
||||
# 'PP：拉丝：HP550J：市场价：青岛：金能化学（日）'
|
||||
# ]
|
||||
# offsite = 80
|
||||
# offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
|
||||
|
||||
y = 'MAIN_CONFT_SETTLE_PRICE'
|
||||
|
||||
horizon = 4 # 预测的步长
|
||||
input_size = 16 # 输入序列长度
|
||||
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
|
||||
|
||||
@ -450,15 +450,15 @@ DEFAULT_CONFIG = {
|
||||
# 开关
|
||||
is_train = True # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = False # 是否使用eta接口
|
||||
is_eta = True # 是否使用eta接口
|
||||
is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = True # 预测结果上传到eta
|
||||
is_update_report = False # 是否上传报告
|
||||
is_update_warning_data = True # 是否上传预警数据
|
||||
is_update_eta = False # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_warning_data = False # 是否上传预警数据
|
||||
is_update_predict_value = True # 是否上传预测值到市场信息平台
|
||||
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
is_del_tow_month = True # 是否删除两个月不更新的特征
|
||||
@ -475,7 +475,7 @@ print("数据库连接成功", host, dbname, dbusername)
|
||||
# 数据截取日期
|
||||
start_year = 2015 # 数据开始年份
|
||||
end_time = '' # 数据截取日期
|
||||
freq = 'WW' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日 "WW" 自定义周
|
||||
freq = 'W' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日 "WW" 自定义周
|
||||
delweekenday = True if freq == 'B' else False # 是否删除周末数据
|
||||
is_corr = False # 特征是否参与滞后领先提升相关系数
|
||||
add_kdj = False # 是否添加kdj指标
|
||||
|
||||
@ -518,6 +518,7 @@ def featureAnalysis(df, dataset, y):
|
||||
X = df.drop(['ds', 'y'], axis=1) # 特征集，排除时间戳和标签列
|
||||
yy = df['y'] # 标签集
|
||||
|
||||
|
||||
# 标签集自相关函数分析
|
||||
from statsmodels.graphics.tsaplots import plot_acf
|
||||
plot_acf(yy, lags=30)
|
||||
@ -2439,6 +2440,15 @@ def get_high_low_data(df):
|
||||
df = pd.merge(df, df1, how='left', on='date')
|
||||
return df
|
||||
|
||||
def get_shujuxiang_data(df):
|
||||
# 读取excel 从第五行开始
|
||||
df1 = pd.read_excel(os.path.join(config.dataset, '数据项下载.xls'), header=5, names=[
|
||||
'numid', 'date', 'MAIN_CONFT_SETTLE_PRICE'])
|
||||
df1['MAIN_CONFT_SETTLE_PRICE'] = df1['MAIN_CONFT_SETTLE_PRICE'].str.replace(',', '').astype(float)
|
||||
# 合并数据
|
||||
df = pd.merge(df, df1, how='left', on='date')
|
||||
return df
|
||||
|
||||
|
||||
def get_waring_data():
|
||||
'''获取取消订阅的指标数据'''
|
||||
|
||||
@ -191,7 +191,7 @@ def sql_inset_predict(global_config):
|
||||
]
|
||||
|
||||
insert_query = f"""
|
||||
INSERT INTO v_tbl_predict_prediction_results (
|
||||
INSERT INTO v_tbl_predict_pp_prediction_results (
|
||||
feature_factor_frequency, strategy_id, oil_code, oil_name, data_date,
|
||||
market_price, day_price, week_price, second_week_price, next_week_price,
|
||||
next_month_price, next_february_price, next_march_price, next_april_price,
|
||||
@ -300,18 +300,17 @@ def predict_main():
|
||||
|
||||
if is_market:
|
||||
logger.info('从市场信息平台获取数据...')
|
||||
try:
|
||||
# try:
|
||||
# 如果是测试环境，最高价最低价取excel文档
|
||||
if server_host == '192.168.100.53':
|
||||
logger.info('从excel文档获取市场信息平台指标')
|
||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
df_zhibiaoshuju = get_shujuxiang_data(df_zhibiaoshuju)
|
||||
else:
|
||||
logger.info('从市场信息平台获取数据')
|
||||
df_zhibiaoshuju = get_market_data(
|
||||
end_time, df_zhibiaoshuju)
|
||||
|
||||
except:
|
||||
logger.info('市场信息平台数据项-eta数据项 拼接失败')
|
||||
# except:
|
||||
# logger.info('市场信息平台数据项-eta数据项 拼接失败')
|
||||
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
@ -503,21 +502,20 @@ def predict_main():
|
||||
|
||||
logger.info('模型训练完成')
|
||||
|
||||
logger.info('训练数据绘图ing')
|
||||
model_results3 = model_losss_juxiting(
|
||||
sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
|
||||
logger.info('训练数据绘图end')
|
||||
# logger.info('训练数据绘图ing')
|
||||
# model_results3 = model_losss_juxiting(
|
||||
# sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
|
||||
# logger.info('训练数据绘图end')
|
||||
|
||||
# # 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'聚烯烃PP大模型日度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
reportname=reportname, sqlitedb=sqlitedb),
|
||||
# # # 模型报告
|
||||
# logger.info('制作报告ing')
|
||||
# title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
# reportname = f'聚烯烃PP大模型日度预测--{end_time}.pdf' # 报告文件名
|
||||
# reportname = reportname.replace(':', '-') # 替换冒号
|
||||
# pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
# reportname=reportname, sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
# logger.info('制作报告end')
|
||||
|
||||
push_market_value()
|
||||
sql_inset_predict(global_config)
|
||||
@ -547,15 +545,17 @@ def predict_main():
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2025-4-14', '2025-4-15', freq='B'):
|
||||
# try:
|
||||
# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
# except Exception as e:
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
|
||||
for i_time in pd.date_range('2025-6-2', '2025-7-23', freq='B'):
|
||||
try:
|
||||
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
global_config['db_mysql'].connect()
|
||||
predict_main()
|
||||
except Exception as e:
|
||||
logger.info(f'预测失败：{e}')
|
||||
continue
|
||||
|
||||
# predict_main()
|
||||
|
||||
|
||||
# push_market_value()
|
||||
# sql_inset_predict(global_config)
|
||||
|
||||
@ -2,8 +2,8 @@
|
||||
|
||||
from lib.dataread import *
|
||||
from config_juxiting_yuedu import *
|
||||
from lib.tools import SendMail, exception_logger
|
||||
from models.nerulforcastmodels import ex_Model, model_losss_juxiting, tansuanli_export_pdf, pp_export_pdf
|
||||
from lib.tools import SendMail, convert_df_to_pydantic, exception_logger, get_modelsname
|
||||
from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_export_pdf
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
@ -13,9 +13,9 @@ global_config.update({
|
||||
'logger': logger,
|
||||
'dataset': dataset,
|
||||
'y': y,
|
||||
'offsite_col': offsite_col,
|
||||
'avg_cols': avg_cols,
|
||||
'offsite': offsite,
|
||||
# 'offsite_col': offsite_col,
|
||||
# 'avg_cols': avg_cols,
|
||||
# 'offsite': offsite,
|
||||
'edbcodenamedict': edbcodenamedict,
|
||||
'is_debug': is_debug,
|
||||
'is_train': is_train,
|
||||
@ -23,6 +23,7 @@ global_config.update({
|
||||
'is_update_report': is_update_report,
|
||||
'settings': settings,
|
||||
'bdwdname': bdwdname,
|
||||
'columnsrename': columnsrename,
|
||||
|
||||
|
||||
# 模型参数
|
||||
@ -84,7 +85,8 @@ global_config.update({
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
'is_bdwd': is_bdwd,
|
||||
'columnsrename':columnsrename,
|
||||
'db_mysql': db_mysql,
|
||||
'DEFAULT_CONFIG': DEFAULT_CONFIG,
|
||||
})
|
||||
|
||||
|
||||
@ -160,6 +162,97 @@ def push_market_value():
|
||||
logger.error(f"推送数据失败: {e}")
|
||||
|
||||
|
||||
def sql_inset_predict(global_config):
|
||||
df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
|
||||
df['created_dt'] = pd.to_datetime(df['created_dt'])
|
||||
df['ds'] = pd.to_datetime(df['ds'])
|
||||
# 获取次月预测结果
|
||||
next_month_price_df = df[df['ds'] == df['ds'].min()]
|
||||
# 获取次二月预测结果
|
||||
next_february_price_df = df.iloc[[1]]
|
||||
# 获取次三月预测结果
|
||||
next_march_price_df = df.iloc[[2]]
|
||||
# 获取次四月预测结果
|
||||
next_april_price_df = df[df['ds'] == df['ds'].max()]
|
||||
|
||||
wd = ['next_month_price', 'next_february_price',
|
||||
'next_march_price', 'next_april_price']
|
||||
model_name_list, model_id_name_dict = get_modelsname(df, global_config)
|
||||
|
||||
PRICE_COLUMNS = [
|
||||
'day_price', 'week_price', 'second_week_price', 'next_week_price',
|
||||
'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
|
||||
]
|
||||
|
||||
params_list = []
|
||||
for df, price_type in zip([next_month_price_df, next_february_price_df, next_march_price_df, next_april_price_df], wd):
|
||||
|
||||
update_columns = [
|
||||
"feature_factor_frequency = VALUES(feature_factor_frequency)",
|
||||
"oil_code = VALUES(oil_code)",
|
||||
"oil_name = VALUES(oil_name)",
|
||||
"data_date = VALUES(data_date)",
|
||||
"market_price = VALUES(market_price)",
|
||||
f"{price_type} = VALUES({price_type})",
|
||||
"model_evaluation_id = VALUES(model_evaluation_id)",
|
||||
"tenant_code = VALUES(tenant_code)",
|
||||
"version_num = VALUES(version_num)",
|
||||
"delete_flag = VALUES(delete_flag)",
|
||||
"update_user = VALUES(update_user)",
|
||||
"update_date = VALUES(update_date)"
|
||||
]
|
||||
|
||||
insert_query = f"""
|
||||
INSERT INTO v_tbl_predict_pp_prediction_results (
|
||||
feature_factor_frequency, strategy_id, oil_code, oil_name, data_date,
|
||||
market_price, day_price, week_price, second_week_price, next_week_price,
|
||||
next_month_price, next_february_price, next_march_price, next_april_price,
|
||||
model_evaluation_id, model_id, tenant_code, version_num, delete_flag,
|
||||
create_user, create_date, update_user, update_date
|
||||
) VALUES (
|
||||
%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
|
||||
)
|
||||
ON DUPLICATE KEY UPDATE
|
||||
{', '.join(update_columns)}
|
||||
"""
|
||||
|
||||
next_day_df = df[['ds', 'created_dt'] + model_name_list]
|
||||
|
||||
pydantic_results = convert_df_to_pydantic(
|
||||
next_day_df, model_id_name_dict, global_config)
|
||||
if pydantic_results:
|
||||
|
||||
for result in pydantic_results:
|
||||
price_values = [None] * len(PRICE_COLUMNS)
|
||||
price_index = PRICE_COLUMNS.index(price_type)
|
||||
price_values[price_index] = next_day_df[model_id_name_dict[result.model_id]].values[0]
|
||||
|
||||
params = (
|
||||
result.feature_factor_frequency,
|
||||
result.strategy_id,
|
||||
result.oil_code,
|
||||
result.oil_name,
|
||||
next_day_df['created_dt'].values[0],
|
||||
result.market_price,
|
||||
*price_values,
|
||||
result.model_evaluation_id,
|
||||
result.model_id,
|
||||
result.tenant_code,
|
||||
1,
|
||||
'0',
|
||||
result.create_user,
|
||||
result.create_date,
|
||||
result.update_user,
|
||||
result.update_date
|
||||
)
|
||||
params_list.append(params)
|
||||
affected_rows = config.db_mysql.execute_batch_insert(
|
||||
insert_query, params_list)
|
||||
config.logger.info(f"成功插入或更新 {affected_rows} 条记录")
|
||||
config.db_mysql.close()
|
||||
|
||||
|
||||
|
||||
def predict_main():
|
||||
"""
|
||||
主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
|
||||
@ -392,15 +485,16 @@ def predict_main():
|
||||
|
||||
# push_market_value()
|
||||
# # 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'聚烯烃PP大模型月度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
reportname=reportname, sqlitedb=sqlitedb),
|
||||
# logger.info('制作报告ing')
|
||||
# title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
# reportname = f'聚烯烃PP大模型月度预测--{end_time}.pdf' # 报告文件名
|
||||
# reportname = reportname.replace(':', '-') # 替换冒号
|
||||
# pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
# reportname=reportname, sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
# logger.info('制作报告end')
|
||||
# logger.info('模型训练完成')
|
||||
sql_inset_predict(global_config)
|
||||
|
||||
# # LSTM 单变量模型
|
||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||
|
||||
@ -158,12 +158,12 @@ def sql_inset_predict(global_config):
|
||||
df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
|
||||
df['created_dt'] = pd.to_datetime(df['created_dt'])
|
||||
df['ds'] = pd.to_datetime(df['ds'])
|
||||
# 获取次日预测结果
|
||||
next_day_df = df[df['ds'] == df['ds'].min()]
|
||||
# 获取本周预测结果
|
||||
this_week_df = df[df['ds'] == df['ds'].max()]
|
||||
# 获取次周预测结果
|
||||
second_week_price_df = df[df['ds'] == df['ds'].min()]
|
||||
# 获取隔周周预测结果
|
||||
next_week_price_df = df[df['ds'] == df['ds'].max()]
|
||||
|
||||
wd = ['day_price', 'week_price']
|
||||
wd = ['second_week_price', 'next_week_price']
|
||||
model_name_list, model_id_name_dict = get_modelsname(df, global_config)
|
||||
|
||||
PRICE_COLUMNS = [
|
||||
@ -172,7 +172,7 @@ def sql_inset_predict(global_config):
|
||||
]
|
||||
|
||||
params_list = []
|
||||
for df, price_type in zip([next_day_df, this_week_df], wd):
|
||||
for df, price_type in zip([second_week_price_df, next_week_price_df], wd):
|
||||
|
||||
update_columns = [
|
||||
"feature_factor_frequency = VALUES(feature_factor_frequency)",
|
||||
@ -190,7 +190,7 @@ def sql_inset_predict(global_config):
|
||||
]
|
||||
|
||||
insert_query = f"""
|
||||
INSERT INTO v_tbl_predict_prediction_results (
|
||||
INSERT INTO v_tbl_predict_pp_prediction_results (
|
||||
feature_factor_frequency, strategy_id, oil_code, oil_name, data_date,
|
||||
market_price, day_price, week_price, second_week_price, next_week_price,
|
||||
next_month_price, next_february_price, next_march_price, next_april_price,
|
||||
@ -238,7 +238,6 @@ def sql_inset_predict(global_config):
|
||||
config.logger.info(f"成功插入或更新 {affected_rows} 条记录")
|
||||
config.db_mysql.close()
|
||||
|
||||
|
||||
def predict_main():
|
||||
"""
|
||||
主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
|
||||
@ -303,7 +302,7 @@ def predict_main():
|
||||
# 如果是测试环境，最高价最低价取excel文档
|
||||
if server_host == '192.168.100.53':
|
||||
logger.info('从excel文档获取市场信息平台指标')
|
||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
df_zhibiaoshuju = get_shujuxiang_data(df_zhibiaoshuju)
|
||||
else:
|
||||
logger.info('从市场信息平台获取数据')
|
||||
df_zhibiaoshuju = get_market_data(
|
||||
@ -318,7 +317,7 @@ def predict_main():
|
||||
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
# 数据处理
|
||||
df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
df = zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
end_time=end_time)
|
||||
|
||||
else:
|
||||
@ -465,21 +464,20 @@ def predict_main():
|
||||
|
||||
logger.info('模型训练完成')
|
||||
|
||||
logger.info('训练数据绘图ing')
|
||||
model_results3 = model_losss_juxiting(
|
||||
sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
|
||||
logger.info('训练数据绘图end')
|
||||
# logger.info('训练数据绘图ing')
|
||||
# model_results3 = model_losss_juxiting(
|
||||
# sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
|
||||
# logger.info('训练数据绘图end')
|
||||
|
||||
# # 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'聚烯烃PP大模型周度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
reportname=reportname, sqlitedb=sqlitedb),
|
||||
# # # 模型报告
|
||||
# logger.info('制作报告ing')
|
||||
# title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
# reportname = f'聚烯烃PP大模型周度预测--{end_time}.pdf' # 报告文件名
|
||||
# reportname = reportname.replace(':', '-') # 替换冒号
|
||||
# pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
# reportname=reportname, sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
# logger.info('制作报告end')
|
||||
|
||||
push_market_value()
|
||||
sql_inset_predict(global_config)
|
||||
@ -509,15 +507,16 @@ def predict_main():
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2025-4-14', '2025-4-15', freq='B'):
|
||||
# try:
|
||||
# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
# except Exception as e:
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
for i_time in pd.date_range('2025-7-18', '2025-7-23', freq='B'):
|
||||
try:
|
||||
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
global_config['db_mysql'].connect()
|
||||
predict_main()
|
||||
except Exception as e:
|
||||
logger.info(f'预测失败：{e}')
|
||||
continue
|
||||
|
||||
# predict_main()
|
||||
|
||||
# push_market_value()
|
||||
sql_inset_predict(global_config)
|
||||
# sql_inset_predict(global_config)
|
||||
|
||||
@ -580,8 +580,8 @@ def predict_main():
|
||||
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2025-6-19', '2025-6-28', freq='B'):
|
||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2024-9-12', '2024-10-7', freq='B'):
|
||||
# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
# global_config['db_mysql'].connect()
|
||||
# predict_main()
|
||||
|
||||
@ -590,7 +590,7 @@ def predict_main():
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
for i_time in pd.date_range('2025-6-4', '2025-6-30', freq='B'):
|
||||
for i_time in pd.date_range('2025-3-17', '2025-3-31', freq='B'):
|
||||
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
global_config['db_mysql'].connect()
|
||||
predict_main()
|
||||
|
||||
@ -492,7 +492,7 @@ def predict_main():
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
for i_time in pd.date_range('2025-6-23', '2025-6-30', freq='B'):
|
||||
for i_time in pd.date_range('2025-3-5', '2025-3-18', freq='B'):
|
||||
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
global_config['db_mysql'].connect()
|
||||
predict_main()
|
||||
|
||||
@ -165,8 +165,8 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
|
||||
|
||||
# VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
|
||||
# Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
|
||||
# NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
|
||||
# NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
|
||||
NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
|
||||
# NBEATSx(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
|
||||
# HINT(h=horizon),
|
||||
|
||||
]
|
||||
@ -439,12 +439,12 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
|
||||
config.dataset, '*.joblib')), key=os.path.getctime)
|
||||
config.logger.info('读取模型：' + filename)
|
||||
nf = load(filename)
|
||||
# 测试集预测
|
||||
nf_test_preds = nf.cross_validation(
|
||||
df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
|
||||
# 测试集预测结果保存
|
||||
nf_test_preds.to_csv(os.path.join(
|
||||
config.dataset, "cross_validation.csv"), index=False)
|
||||
# # 测试集预测
|
||||
# nf_test_preds = nf.cross_validation(
|
||||
# df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
|
||||
# # 测试集预测结果保存
|
||||
# nf_test_preds.to_csv(os.path.join(
|
||||
# config.dataset, "cross_validation.csv"), index=False)
|
||||
|
||||
df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
|
||||
|
||||
@ -2344,6 +2344,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
# 去掉created_dt 列
|
||||
df4 = df4.drop(columns=['created_dt'])
|
||||
# 计算模型偏差率
|
||||
try:
|
||||
# 计算各列对于y列的差值百分比
|
||||
df3 = pd.DataFrame() # 存储偏差率
|
||||
|
||||
@ -2376,6 +2377,9 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
data = df4.values.tolist()
|
||||
col_width = 500/len(df4.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
except:
|
||||
content.append(Graphs.draw_text('暂无'))
|
||||
config.logger.info('偏差率计算错误，跳过')
|
||||
|
||||
content.append(Graphs.draw_little_title('三、预测过程解析：'))
|
||||
# 特征、模型、参数配置
|
||||
|
||||
140
test/demo.py
Normal file
140
test/demo.py
Normal file
@ -0,0 +1,140 @@
|
||||
import requests
|
||||
import json
|
||||
|
||||
|
||||
def get_cptcha():
|
||||
url = 'https://marketinfo.jbshihua.com/jingbo-api/captcha'
|
||||
res = requests.get(url)
|
||||
res = json.loads(res.text)
|
||||
res = res["data"]["img"]
|
||||
imgurl = 'data:image/jpeg;base64,/' + res
|
||||
res = requests.get(imgurl)
|
||||
with open('cptcha.png', 'wb') as f:
|
||||
f.write(res.content)
|
||||
|
||||
|
||||
def main() -> dict:
|
||||
login_url = "http://marketinfo.jbshihua.com/jingbo-api/api/server/login"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
query_data_list_item_nos_url = f"http://marketinfo.jbshihua.com/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": "20200101",
|
||||
"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentjsj"] # 数据项编码,代表 brent结算价
|
||||
}
|
||||
}
|
||||
login_res = requests.post(
|
||||
url=login_url, json=login_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
print('获取到的token', token)
|
||||
headers = {"Authorization": token}
|
||||
print('获取数据中...')
|
||||
items_res = requests.post(url=query_data_list_item_nos_url, headers=headers,
|
||||
json=query_data_list_item_nos_data, timeout=(3, 35))
|
||||
json_data = json.loads(items_res.text)
|
||||
print(json_data)
|
||||
return text
|
||||
|
||||
return {
|
||||
"result": token,
|
||||
}
|
||||
|
||||
|
||||
class AuthHandler:
|
||||
def __init__(self):
|
||||
# 初始化登录表单数据
|
||||
self.loginForm = {
|
||||
'captchaToken': '',
|
||||
'src': ''
|
||||
}
|
||||
self.loading = False
|
||||
|
||||
def loadCaptcha(self):
|
||||
"""加载验证码图片"""
|
||||
try:
|
||||
self.loading = True
|
||||
|
||||
# 发送 POST 请求获取验证码
|
||||
response = requests.post(
|
||||
url="https://marketinfo.jbshihua.com/jingbo-api/captcha",
|
||||
json={}, # 发送空 JSON 数据体
|
||||
timeout=10 # 设置超时时间为 10 秒
|
||||
)
|
||||
|
||||
# 检查响应状态码
|
||||
response.raise_for_status()
|
||||
|
||||
# 处理响应数据
|
||||
data = response.json()
|
||||
if data:
|
||||
self.loginForm['captchaToken'] = data.get('token', '')
|
||||
self.loginForm['src'] = "data:image/jpeg;base64," + \
|
||||
data.get('img', '')
|
||||
|
||||
# 保存图片
|
||||
with open('cptcha.png', 'wb') as f:
|
||||
f.write(res.content)
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
print(f"请求出错: {e}")
|
||||
finally:
|
||||
self.loading = False
|
||||
|
||||
|
||||
def main2() -> dict:
|
||||
import requests
|
||||
import json
|
||||
login_url = "http://marketinfo.jbshihua.com/jingbo-api/api/server/login"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "admin",
|
||||
"password": "OWZlYjcyNDAwZDRkYjEwZjE1ZjA0MTIwNDAwOGI5NjI=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "PC"
|
||||
},
|
||||
"funcModule": "登录页面",
|
||||
"funcOperation": "登录"
|
||||
}
|
||||
login_res = requests.post(
|
||||
url=login_url, json=login_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
print(text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
|
||||
return {
|
||||
"accessToken": token,
|
||||
}
|
||||
return {
|
||||
"accessToken": "",
|
||||
}
|
||||
|
||||
|
||||
# 使用示例
|
||||
if __name__ == "__main__":
|
||||
# auth = AuthHandler()
|
||||
# auth.loadCaptcha()
|
||||
|
||||
# # 打印获取到的验证码信息
|
||||
# print(f"验证码 Token: {auth.loginForm['captchaToken']}")
|
||||
# print(f"验证码图片: {auth.loginForm['src']}...") # 只显示前50个字符
|
||||
# main2()
|
||||
main()
|
||||
Loading…
Reference in New Issue
Block a user