雍安环境代码拆分
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workpc
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234
config_jingbo.py
234
config_jingbo.py
@ -106,6 +106,8 @@ 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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@ -162,17 +164,17 @@ ClassifyId = 1214
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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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# ## 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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# 预生产环境
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# ## 预生产环境
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# host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
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# port = 3306
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# dbusername ='jingbo'
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@ -182,7 +184,7 @@ ClassifyId = 1214
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# ## 线上开关
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# # 线上开关备份
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# is_train = True # 是否训练
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# is_debug = False # 是否调试
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# is_eta = True # 是否使用eta接口
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@ -196,125 +198,39 @@ ClassifyId = 1214
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# 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
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################################################################################################################ 变量定义--测试环境
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login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login"
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upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
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upload_warning_url = "http://192.168.100.53:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = "http://192.168.100.53:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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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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"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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warning_data = {
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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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# 北京环境数据库
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host = '192.168.101.27'
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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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table_name = 'v_tbl_crude_oil_warning'
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### 开关
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is_train = False # 是否训练
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is_debug = False # 是否调试
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is_eta = True # 是否使用eta接口
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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
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is_timefurture = True # 是否使用时间特征
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is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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is_update_report = True # 是否上传报告
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is_update_warning_data = True # 是否上传预警数据
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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
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is_del_tow_month = True # 是否删除两个月不更新的特征
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################################################################################################################ 变量定义--雍安测试环境
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# login_pushreport_url = "http://192.168.100.115:9090/dom-api/api/server/login"
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# upload_url = "http://192.168.100.115:9090/dom-api/api/analysis/reportInfo/researchUploadReportSave"
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# # upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
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# upload_warning_url = "http://192.168.100.115:9090/dom-api/api/basicBuiness/crudeOilWarning/save"
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# query_data_list_item_nos_url = "http://192.168.100.115:9090/dom-api/api/warehouse/dwDataItem/queryDataListItemNos"
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# server_host = '192.168.100.53'
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# login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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# upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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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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# 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": "1eb24ab5a6af12e30daf78af276664f1",
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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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# "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":'rui.liu', #报告所属用户账号
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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":'1', #分析报告分类编码
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# "reportEmployeeCode":"U270018", # 报告人
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# "reportDeptCode" :"D270001" ,# 报告部门
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# # "reportDeptCode" :"000001" ,# 报告部门
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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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@ -361,11 +277,99 @@ is_del_tow_month = True # 是否删除两个月不更新的特征
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# is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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# is_update_eta = False # 预测结果上传到eta
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# is_update_report = True # 是否上传报告
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# is_update_warning_data = False # 是否上传预警数据
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# is_update_warning_data = True # 是否上传预警数据
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# 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
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# is_del_tow_month = True # 是否删除两个月不更新的特征
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################################################################################################################ 变量定义--雍安测试环境
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login_pushreport_url = "http://192.168.100.115:9090/dom-api/api/server/login"
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upload_url = "http://192.168.100.115:9090/dom-api/api/analysis/reportInfo/researchUploadReportSave"
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# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
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upload_warning_url = "http://192.168.100.115:9090/dom-api/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = "http://192.168.100.115:9090/dom-api/api/warehouse/dwDataItem/queryDataListItemNos"
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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": "1eb24ab5a6af12e30daf78af276664f1",
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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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"ownerAccount":'rui.liu', #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":'1', #分析报告分类编码
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"reportEmployeeCode":"U270018", # 报告人
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"reportDeptCode" :"D270001" ,# 报告部门
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# "reportDeptCode" :"000001" ,# 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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warning_data = {
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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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# 北京环境数据库
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host = '192.168.101.27'
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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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table_name = 'v_tbl_crude_oil_warning'
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### 开关
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is_train = False # 是否训练
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is_debug = False # 是否调试
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is_eta = False # 是否使用eta接口
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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
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is_timefurture = True # 是否使用时间特征
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is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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is_update_report = True # 是否上传报告
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is_update_warning_data = False # 是否上传预警数据
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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
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is_del_tow_month = True # 是否删除两个月不更新的特征
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# 连接到数据库
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db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
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@ -424,8 +428,8 @@ if end_time == '':
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username='1321340118@qq.com'
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passwd='wgczgyhtyyyyjghi'
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# recv=['liurui_test@163.com','52585119@qq.com']
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recv=['liurui_test@163.com','jin.wang@chambroad.com']
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# recv=['liurui_test@163.com']
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# recv=['liurui_test@163.com','jin.wang@chambroad.com']
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recv=['liurui_test@163.com']
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title='reportname'
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content='brent价格预测报告请看附件'
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file=os.path.join(dataset,'reportname')
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@ -457,3 +461,9 @@ logger.addHandler(file_handler)
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logger.addHandler(console_handler)
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# logger.info('当前配置：'+settings)
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if __name__ == '__main__':
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import pandas as pd
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df1 = pd.read_excel(os.path.join(dataset,'数据项下载.xls'),header=5, names=['numid','date', 'Brentzdj', 'Brentzgj'])
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print(df1)
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287
config_yongan.py
Normal file
287
config_yongan.py
Normal file
@ -0,0 +1,287 @@
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import logging
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import os
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import logging.handlers
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import datetime
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from lib.tools import MySQLDB,SQLiteHandler
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# eta 接口token
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APPID = "XNLDvxZHHugj7wJ7"
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SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
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# eta 接口url
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sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
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classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
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uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
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classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
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edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
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edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
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edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
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edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
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edbcodelist = ['CO1 Comdty', 'ovx index', 'C2404194834', 'C2404199738', 'dxy curncy', 'C2403128043', 'C2403150124',
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'DOESCRUD Index', 'WTRBM1 EEGC Index', 'FVHCM1 INDEX', 'doedtprd index', 'CFFDQMMN INDEX',
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'C2403083739', 'C2404167878', 'C2403250571', 'lmcads03 lme comdty', 'GC1 COMB Comdty',
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'C2404171822','C2404167855',
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# 'W000825','W000826','G.IPE', # 美国汽柴油
|
||||
# 'S5131019','ID00135604','FSGAM1 Index','S5120408','ID00136724', # 新加坡汽柴油
|
||||
]
|
||||
|
||||
# 临时写死用指定的列,与上面的edbcode对应，后面更改
|
||||
edbnamelist = [
|
||||
'ds','y',
|
||||
'Brent c1-c6','Brent c1-c3','Brent-WTI','美国商业原油库存',
|
||||
'DFL','美国汽油裂解价差','ovx index','dxy curncy','lmcads03 lme comdty',
|
||||
'C2403128043','C2403150124','FVHCM1 INDEX','doedtprd index','CFFDQMMN INDEX',
|
||||
'C2403083739','C2404167878',
|
||||
'GC1 COMB Comdty','C2404167855',
|
||||
# 'A汽油价格','W000826','ICE柴油价格',
|
||||
# '新加坡(含硫0.05%) 柴油现货价','柴油：10ppm：国际市场：FOB中间价：新加坡（日）','Bloomberg Commodity Fair Value Singapore Mogas 92 Swap Month 1','97#汽油FOB新加坡现货价','无铅汽油：97#：国际市场：FOB中间价：新加坡（日）'
|
||||
]
|
||||
|
||||
edbcodenamedict = {'ovx index': '原油波动率',
|
||||
'dxy curncy': '美元指数',
|
||||
'C2403128043': 'Brent连1合约价格拟合残差/美元指数',
|
||||
'C2403150124': 'Brent连1合约价格拟合残差/Brent 连2-连3',
|
||||
'DOESCRUD Index': '美国商业原油库存',
|
||||
'FVHCM1 INDEX': '美国取暖油裂解C1',
|
||||
'doedtprd index': '美国成品油表需',
|
||||
'CFFDQMMN INDEX': 'WTI管理资金净多持仓',
|
||||
'C2403083739': 'WTI基金多空持仓比',
|
||||
'C2404167878': 'WTI基金净持仓COT指标（代码运算）',
|
||||
'lmcads03 lme comdty': 'LME铜价',
|
||||
'GC1 COMB Comdty': '黄金连1合约',
|
||||
'C2404167855': '金油比'}
|
||||
|
||||
|
||||
|
||||
|
||||
# eta自有数据指标编码
|
||||
modelsindex = {
|
||||
'NHITS': 'SELF0000001',
|
||||
'Informer':'SELF0000057',
|
||||
'LSTM':'SELF0000058',
|
||||
'iTransformer':'SELF0000059',
|
||||
'TSMixer':'SELF0000060',
|
||||
'TSMixerx':'SELF0000061',
|
||||
'PatchTST':'SELF0000062',
|
||||
'RNN':'SELF0000063',
|
||||
'GRU':'SELF0000064',
|
||||
'TCN':'SELF0000065',
|
||||
'BiTCN':'SELF0000066',
|
||||
'DilatedRNN':'SELF0000067',
|
||||
'MLP':'SELF0000068',
|
||||
'DLinear':'SELF0000069',
|
||||
'NLinear':'SELF0000070',
|
||||
'TFT':'SELF0000071',
|
||||
'FEDformer':'SELF0000072',
|
||||
'StemGNN':'SELF0000073',
|
||||
'MLPMultivariate':'SELF0000074',
|
||||
'TiDE':'SELF0000075',
|
||||
'DeepNPTS':'SELF0000076'
|
||||
}
|
||||
|
||||
# eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据
|
||||
data = {
|
||||
"IndexCode": "",
|
||||
"IndexName": "价格预测模型",
|
||||
"Unit": "无",
|
||||
"Frequency": "日度",
|
||||
"SourceName": f"价格预测",
|
||||
"Remark": 'ddd',
|
||||
"DataList": [
|
||||
{
|
||||
"Date": "2024-05-02",
|
||||
"Value": 333444
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# eta 分类
|
||||
# level：3才可以获取到数据，所以需要人工把能源化工下所有的level3级都找到
|
||||
# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
|
||||
#ParentId ":1160, 能源化工
|
||||
# ClassifyId ":1214,原油
|
||||
#ParentId ":1214,"，就是原油下所有的数据。
|
||||
ClassifyId = 1214
|
||||
|
||||
################################################################################################################ 变量定义--雍安测试环境
|
||||
login_pushreport_url = "http://192.168.100.115:9090/dom-api/api/server/login"
|
||||
upload_url = "http://192.168.100.115:9090/dom-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
|
||||
upload_warning_url = "http://192.168.100.115:9090/dom-api/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = "http://192.168.100.115:9090/dom-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api-dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "1eb24ab5a6af12e30daf78af276664f1",
|
||||
"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":'rui.liu', #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":'1', #分析报告分类编码
|
||||
"reportEmployeeCode":"U270018", # 报告人
|
||||
"reportDeptCode" :"D270001" ,# 报告部门
|
||||
# "reportDeptCode" :"000001" ,# 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
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# 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'
|
||||
|
||||
|
||||
### 开关
|
||||
is_train = False # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = False # 是否使用eta接口
|
||||
is_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 = 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_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
|
||||
is_del_tow_month = True # 是否删除两个月不更新的特征
|
||||
|
||||
|
||||
|
||||
# 连接到数据库
|
||||
db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
|
||||
db_mysql.connect()
|
||||
print("数据库连接成功",host,dbname,dbusername)
|
||||
|
||||
|
||||
# 数据截取日期
|
||||
start_year = 2020 # 数据开始年份
|
||||
end_time = '' # 数据截取日期
|
||||
freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
|
||||
delweekenday = True if freq == 'B' else False # 是否删除周末数据
|
||||
is_corr = False # 特征是否参与滞后领先提升相关系数
|
||||
add_kdj = False # 是否添加kdj指标
|
||||
if add_kdj and is_edbnamelist:
|
||||
edbnamelist = edbnamelist+['K','D','J']
|
||||
|
||||
### 模型参数
|
||||
y = 'Brent连1合约价格' # 原油指标数据的目标变量 Brent连1合约价格 Brent活跃合约
|
||||
horizon =5 # 预测的步长
|
||||
input_size = 40 # 输入序列长度
|
||||
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
|
||||
val_check_steps = 30 # 评估频率
|
||||
early_stop_patience_steps = 5 # 早停的耐心步数
|
||||
# --- 交叉验证用的参数
|
||||
test_size = 100 # 测试集大小，定义100，后面使用的时候重新赋值
|
||||
val_size = test_size # 验证集大小,同测试集大小
|
||||
|
||||
### 特征筛选用到的参数
|
||||
k = 100 # 特征筛选数量，如果是0或者值比特征数量大，代表全部特征
|
||||
corr_threshold = 0.6 # 相关性大于0.6的特征
|
||||
rote = 0.06 # 绘图上下界阈值
|
||||
|
||||
### 计算准确率
|
||||
weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
|
||||
|
||||
|
||||
### 文件
|
||||
data_set = '原油指标数据.xlsx' # 数据集文件
|
||||
dataset = 'yuanyoudataset' # 数据集文件夹
|
||||
|
||||
# 数据库名称
|
||||
db_name = os.path.join(dataset,'jbsh_yuanyou.db')
|
||||
sqlitedb = SQLiteHandler(db_name)
|
||||
sqlitedb.connect()
|
||||
|
||||
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
|
||||
# 获取日期时间
|
||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
|
||||
now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
|
||||
reportname = f'Brent原油大模型预测--{now}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
if end_time == '':
|
||||
end_time = now
|
||||
### 邮件配置
|
||||
username='1321340118@qq.com'
|
||||
passwd='wgczgyhtyyyyjghi'
|
||||
# recv=['liurui_test@163.com','52585119@qq.com']
|
||||
# recv=['liurui_test@163.com','jin.wang@chambroad.com']
|
||||
recv=['liurui_test@163.com']
|
||||
title='reportname'
|
||||
content='brent价格预测报告请看附件'
|
||||
file=os.path.join(dataset,'reportname')
|
||||
# file=os.path.join(dataset,'14-7-50--100-原油指标数据.xlsx-Brent连1合约价格--20240731175936-预测报告.pdf')
|
||||
ssl=True
|
||||
|
||||
|
||||
### 日志配置
|
||||
|
||||
# 创建日志目录（如果不存在）
|
||||
log_dir = 'logs'
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir)
|
||||
|
||||
# 配置日志记录器
|
||||
logger = logging.getLogger('my_logger')
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
# 配置文件处理器，将日志记录到文件
|
||||
file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
|
||||
file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
|
||||
|
||||
# 配置控制台处理器，将日志打印到控制台
|
||||
console_handler = logging.StreamHandler()
|
||||
console_handler.setFormatter(logging.Formatter('%(message)s'))
|
||||
|
||||
# 将处理器添加到日志记录器
|
||||
logger.addHandler(file_handler)
|
||||
logger.addHandler(console_handler)
|
||||
|
||||
# logger.info('当前配置：'+settings)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import pandas as pd
|
||||
df1 = pd.read_excel(os.path.join(dataset,'数据项下载.xls'),header=5, names=['numid','date', 'Brentzdj', 'Brentzgj'])
|
||||
print(df1)
|
||||
@ -41,7 +41,8 @@ plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
|
||||
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
|
||||
|
||||
# from config_jingbo_pro import *
|
||||
from config_jingbo import *
|
||||
# from config_jingbo import *
|
||||
from config_yongan import *
|
||||
# from config_juxiting import *
|
||||
|
||||
|
||||
@ -1682,6 +1683,15 @@ def get_market_data(end_time,df):
|
||||
df = pd.merge(df, df2, how='left',on='date')
|
||||
return df
|
||||
|
||||
|
||||
def get_high_low_data(df):
|
||||
# 读取excel 从第五行开始
|
||||
df1 = pd.read_excel(os.path.join(dataset,'数据项下载.xls'),header=5, names=['numid','date', 'Brentzdj', 'Brentzgj'])
|
||||
# 合并数据
|
||||
df = pd.merge(df, df1, how='left',on='date')
|
||||
return df
|
||||
|
||||
|
||||
# 时间特征，年，月，一年的多少天，周几，第几周，第几季度，每月的第几天， 每季度的第几天，是否每月的第一天，是否每月的最后一天，是否每季度的第一天，是否每季度的最后一天，是否每年的第一天，是否每年的最后一天
|
||||
def addtimecharacteristics(df,dataset):
|
||||
"""
|
||||
|
||||
1733
lib/dataread_yongan.py
Normal file
1733
lib/dataread_yongan.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,5 +1,5 @@
|
||||
# 读取配置
|
||||
from lib.dataread import *
|
||||
from lib.dataread_yongan import *
|
||||
from lib.tools import SendMail,exception_logger
|
||||
from models.nerulforcastmodels import ex_Model,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
|
||||
|
||||
@ -280,9 +280,11 @@ def predict_main():
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
global end_time
|
||||
is_on = True
|
||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
|
||||
end_time = i_time.strftime('%Y-%m-%d')
|
||||
# global end_time
|
||||
# is_on = True
|
||||
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
|
||||
# end_time = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
|
||||
predict_main()
|
||||
|
||||
@ -77,9 +77,16 @@ def predict_main():
|
||||
if is_market:
|
||||
logger.info('从市场信息平台获取数据...')
|
||||
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文档获取最高价最低价')
|
||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
else:
|
||||
logger.info('从市场信息平台获取数据')
|
||||
df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
|
||||
|
||||
except :
|
||||
logger.info('从市场信息平台获取数据失败')
|
||||
logger.info('最高最低价拼接失败')
|
||||
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
|
||||
@ -106,8 +113,6 @@ def predict_main():
|
||||
# 保存最新日期的y值到数据库
|
||||
# 取第一行数据存储到数据库中
|
||||
first_row = df[['ds', 'y']].tail(1)
|
||||
print(first_row['ds'].values[0])
|
||||
print(first_row['y'].values[0])
|
||||
# 判断y的类型是否为float
|
||||
if not isinstance(first_row['y'].values[0], float):
|
||||
logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||
@ -137,15 +142,15 @@ def predict_main():
|
||||
# 找到update_y 中ds且df中的y的行
|
||||
update_y = update_y[update_y['ds']<=end_time]
|
||||
logger.info(f'要更新y的信息：{update_y}')
|
||||
try:
|
||||
# try:
|
||||
for row in update_y.itertuples(index=False):
|
||||
row_dict = row._asdict()
|
||||
yy = df[df['ds']==row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
except Exception as e:
|
||||
logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
# except Exception as e:
|
||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
import datetime
|
||||
# 判断当前日期是不是周一
|
||||
@ -276,7 +281,7 @@ def predict_main():
|
||||
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||
ssl=ssl,
|
||||
)
|
||||
m.send_mail()
|
||||
# m.send_mail()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
Loading…
Reference in New Issue
Block a user