PriceForecast/config_jingbo.py
2024-11-15 17:15:32 +08:00

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import logging
import os
import logging.handlers
import datetime
# 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']
# 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'
]
# 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": "",
"Frequency": "日度",
"SourceName": f"价格预测",
"Remark": 'ddd',
"DataList": [
{
"Date": "2024-05-02",
"Value": 333444
}
]
}
# eta 分类
# level3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
### 报告上传配置
# 变量定义--线上环境
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"
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":{
"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
}
}
# # 变量定义--测试环境
# login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login"
# 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
# login_data = {
# "data": {
# "account": "api_test",
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=",
# "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
# }
# }
### 线上开关
# 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_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
### 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_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 = False # 是否上传报告
# 数据截取日期
end_time = '' # 数据截取日期
delweekenday = True
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活跃合约' # 原油指标数据的目标变量
# y = '期货结算价(连续):布伦特原油:前一个观测值' # ineoil的目标变量
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 = 200 # 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
### 文件
data_set = '原油指标数据.xlsx' # 数据集文件
# data_set = 'INE_OIL(1).csv'
### 文件夹
dataset = 'dataset' # 数据集文件夹
# 数据库名称
db_name = os.path.join(dataset,'jbsh_yuanyou.db')
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
### 邮件配置
username='1321340118@qq.com'
passwd='wgczgyhtyyyyjghi'
# recv=['liurui_test@163.com','52585119@qq.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)