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雍安环境代码拆分

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workpc 2024-12-30 15:29:36 +08:00
parent 98e0178da0
commit 4c097c0bb8
6 changed files with 2182 additions and 135 deletions
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234
config_jingbo.py
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@ -106,6 +106,8 @@ ClassifyId = 1214
################################################################################################################ 变量定义--线上环境 ################################################################################################################ 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" # 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_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" # upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
@ -162,17 +164,17 @@ ClassifyId = 1214
# # 生产环境数据库 # ## 生产环境数据库
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com' # # host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
# port = 3306 # # port = 3306
# dbusername ='jingbo' # # dbusername ='jingbo'
# password = 'shihua@123' # # password = 'shihua@123'
# dbname = 'jingbo' # # dbname = 'jingbo'
# table_name = 'v_tbl_crude_oil_warning' # # table_name = 'v_tbl_crude_oil_warning'
# 预生产环境 # ## 预生产环境
# host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com' # host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
# port = 3306 # port = 3306
# dbusername ='jingbo' # dbusername ='jingbo'
@ -182,7 +184,7 @@ ClassifyId = 1214
# ## 线上开关 # # 线上开关备份
# is_train = True # 是否训练 # is_train = True # 是否训练
# is_debug = False # 是否调试 # is_debug = False # 是否调试
# is_eta = True # 是否使用eta接口 # is_eta = True # 是否使用eta接口
@ -196,125 +198,39 @@ ClassifyId = 1214
# 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_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
################################################################################################################ 变量定义--测试环境 ################################################################################################################ 变量定义--测试环境
login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login" # server_host = '192.168.100.53'
upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
upload_warning_url = "http://192.168.100.53:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
query_data_list_item_nos_url = "http://192.168.100.53:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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
}
}
# 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 = 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 = False # 预测结果上传到eta
is_update_report = True # 是否上传报告
is_update_warning_data = True # 是否上传预警数据
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
is_del_tow_month = True # 是否删除两个月不更新的特征
################################################################################################################ 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_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
# upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
# upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
# query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
# login_data = { # login_data = {
# "data": { # "data": {
# "account": "api-dev", # "account": "api_test",
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
# "tenantHashCode": "1eb24ab5a6af12e30daf78af276664f1", # "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
# "terminal": "API" # "terminal": "API"
# }, # },
# "funcModule": "API", # "funcModule": "API",
# "funcOperation": "获取token" # "funcOperation": "获取token"
# } # }
# upload_data = { # upload_data = {
# "funcModule":'研究报告信息', # "funcModule":'研究报告信息',
# "funcOperation":'上传原油价格预测报告', # "funcOperation":'上传原油价格预测报告',
# "data":{ # "data":{
# "ownerAccount":'rui.liu', #报告所属用户账号 # "ownerAccount":'arui', #报告所属用户账号
# "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST # "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": '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 # "fileBase64": '' ,#文件内容base64
# "categoryNo":'yyjgycbg', # 研究报告分类编码 # "categoryNo":'yyjgycbg', # 研究报告分类编码
# "smartBusinessClassCode":'1', #分析报告分类编码 # "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
# "reportEmployeeCode":"U270018", # 报告人 # "reportEmployeeCode":"E40116", # 报告人
# "reportDeptCode" :"D270001" ,# 报告部门 # "reportDeptCode" :"D0044" ,# 报告部门
# # "reportDeptCode" :"000001" ,# 报告部门
# "productGroupCode":"RAW_MATERIAL" # 商品分类 # "productGroupCode":"RAW_MATERIAL" # 商品分类
# } # }
# } # }
@ -361,11 +277,99 @@ is_del_tow_month = True # 是否删除两个月不更新的特征
# is_edbnamelist = False # 自定义特征，对应上面的edbnamelist # 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_eta = False # 预测结果上传到eta
# is_update_report = True # 是否上传报告 # 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_warning_data = True # 是否上传预警数据
# is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征 # is_del_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 # is_del_tow_month = True # 是否删除两个月不更新的特征
################################################################################################################ 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 # 连接到数据库
db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname) db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
@ -424,8 +428,8 @@ if end_time == '':
username='1321340118@qq.com' username='1321340118@qq.com'
passwd='wgczgyhtyyyyjghi' passwd='wgczgyhtyyyyjghi'
# recv=['liurui_test@163.com','52585119@qq.com'] # recv=['liurui_test@163.com','52585119@qq.com']
recv=['liurui_test@163.com','jin.wang@chambroad.com'] # recv=['liurui_test@163.com','jin.wang@chambroad.com']
# recv=['liurui_test@163.com'] recv=['liurui_test@163.com']
title='reportname' title='reportname'
content='brent价格预测报告请看附件' content='brent价格预测报告请看附件'
file=os.path.join(dataset,'reportname') file=os.path.join(dataset,'reportname')
@ -457,3 +461,9 @@ logger.addHandler(file_handler)
logger.addHandler(console_handler) logger.addHandler(console_handler)
# logger.info('当前配置：'+settings) # 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)

287
config_yongan.py Normal file
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@ -0,0 +1,287 @@
import logging
import os
import logging.handlers
import datetime
from lib.tools import MySQLDB,SQLiteHandler
# eta 接口token
APPID = "XNLDvxZHHugj7wJ7"
SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
# eta 接口url
sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
edbcodelist = ['CO1 Comdty', 'ovx index', 'C2404194834', 'C2404199738', 'dxy curncy', 'C2403128043', 'C2403150124',
'DOESCRUD Index', 'WTRBM1 EEGC Index', 'FVHCM1 INDEX', 'doedtprd index', 'CFFDQMMN INDEX',
'C2403083739', 'C2404167878', 'C2403250571', 'lmcads03 lme comdty', 'GC1 COMB Comdty',
'C2404171822','C2404167855',
# '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)

12
lib/dataread.py
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@ -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 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
# from config_jingbo_pro import * # from config_jingbo_pro import *
from config_jingbo import * # from config_jingbo import *
from config_yongan import *
# from config_juxiting 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') df = pd.merge(df, df2, how='left',on='date')
return df 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 # 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): def addtimecharacteristics(df,dataset):
""" """

1733
lib/dataread_yongan.py Normal file
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16
main_yongan.py
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@ -1,5 +1,5 @@
# 读取配置 # 读取配置
from lib.dataread import * from lib.dataread_yongan import *
from lib.tools import SendMail,exception_logger 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 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__': if __name__ == '__main__':
global end_time # global end_time
is_on = True # is_on = True
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期 # # 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'): # for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
end_time = i_time.strftime('%Y-%m-%d') # end_time = i_time.strftime('%Y-%m-%d')
predict_main() # predict_main()
predict_main()

33
main_yuanyou.py
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@ -77,9 +77,16 @@ def predict_main():
if is_market: if is_market:
logger.info('从市场信息平台获取数据...') logger.info('从市场信息平台获取数据...')
try: try:
df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju) # 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 : 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 # 保存到xlsx文件的sheet表
with pd.ExcelWriter(os.path.join(dataset,data_set)) as file: with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
@ -106,8 +113,6 @@ def predict_main():
# 保存最新日期的y值到数据库 # 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 # 取第一行数据存储到数据库中
first_row = df[['ds', 'y']].tail(1) 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 # 判断y的类型是否为float
if not isinstance(first_row['y'].values[0], 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') 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 # 找到update_y 中ds且df中的y的行
update_y = update_y[update_y['ds']<=end_time] update_y = update_y[update_y['ds']<=end_time]
logger.info(f'要更新y的信息：{update_y}') logger.info(f'要更新y的信息：{update_y}')
try: # try:
for row in update_y.itertuples(index=False): for row in update_y.itertuples(index=False):
row_dict = row._asdict() row_dict = row._asdict()
yy = df[df['ds']==row_dict['ds']]['y'].values[0] yy = df[df['ds']==row_dict['ds']]['y'].values[0]
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0] LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].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']}'") sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
except Exception as e: # except Exception as e:
logger.info(f'更新accuracy表的y值失败：{e}') # logger.info(f'更新accuracy表的y值失败：{e}')
import datetime import datetime
# 判断当前日期是不是周一 # 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), file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
ssl=ssl, ssl=ssl,
) )
m.send_mail() # m.send_mail()
if __name__ == '__main__': if __name__ == '__main__':