Merge branch 'main' of https://gitea.jayhgq.cn/liurui/PriceForecast
This commit is contained in:
liurui
commit
e39d90a503
123
config_jingbo.py
123
config_jingbo.py
@ -86,50 +86,13 @@ ClassifyId = 1214
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### 报告上传配置
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# 变量定义--线上环境
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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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# 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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# "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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# # 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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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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login_data = {
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"data": {
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"account": "api_test",
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"password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=",
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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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@ -137,48 +100,85 @@ login_data = {
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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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"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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### 线上开关
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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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# 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 = True # 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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### 开关
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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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# login_data = {
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# "data": {
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# "account": "api_test",
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# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=",
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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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### 线上开关
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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 = False # 是否使用eta接口
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is_eta = True # 是否使用eta接口
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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 = False # 是否上传报告
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is_update_eta = True # 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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### 开关
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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 = False # 是否使用eta接口
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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 = False # 是否上传报告
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# 数据截取日期
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end_time = '2024-10-29' # 数据截取日期
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end_time = '' # 数据截取日期
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delweekenday = True
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is_corr = False # 特征是否参与滞后领先提升相关系数
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add_kdj = False # 是否添加kdj指标
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@ -212,7 +212,8 @@ db_name = os.path.join(dataset,'jbsh_yuanyou.db')
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settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
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# 获取日期时间
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now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
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# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
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now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
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reportname = f'Brent原油大模型预测--{now}.pdf' # 报告文件名
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reportname = reportname.replace(':', '-') # 替换冒号
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BIN
jbsh_yuanyou线上数据库10.7备份.db
Normal file
BIN
jbsh_yuanyou线上数据库10.7备份.db
Normal file
Binary file not shown.
49
main.py
49
main.py
@ -1,4 +1,4 @@
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# 读取配置
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# 读取配置
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from config_jingbo import *
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# from config_tansuanli import *
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# from config_juxiting import *
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@ -75,34 +75,29 @@ def predict_main():
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import datetime
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# 判断当前日期是不是周一
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is_weekday = datetime.datetime.now().weekday() == 4
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is_weekday = datetime.datetime.now().weekday() == 0
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if is_weekday:
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logger.info('今天是周一，更新预测模型')
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try:
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# 计算最近20天预测残差最低的模型名称
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# 计算最近20天预测残差最低的模型名称
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model_results = sqlitedb.select_data('trueandpredict',order_by = "ds DESC",limit = "20")
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model_results = model_results.dropna()
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modelnames = model_results.columns.to_list()[2:]
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for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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model_results[col] = model_results[col].astype(np.float32)
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# 计算每个预测值与真实值之间的偏差率
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for model in modelnames:
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model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
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model_results = sqlitedb.select_data('trueandpredict',order_by = "ds DESC",limit = "20")
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model_results = model_results.dropna()
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modelnames = model_results.columns.to_list()[2:]
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for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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model_results[col] = model_results[col].astype(np.float32)
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# 计算每个预测值与真实值之间的偏差率
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for model in modelnames:
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model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
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# 获取每行对应的最小偏差率值
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min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# 获取每行对应的最小偏差率值对应的列名
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min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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# 将列名索引转换为列名
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min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
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# 取出现次数最多的模型名称
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most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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logger.info(f"最近20天预测残差最低的模型名称：{most_common_model}")
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except Exception as e:
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logger.error(f"最近20天预测残差最低的模型名称计算失败：{e}")
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# 取txt中的模型名称
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most_common_model = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()[0]
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# 获取每行对应的最小偏差率值
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min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# 获取每行对应的最小偏差率值对应的列名
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min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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# 将列名索引转换为列名
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min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
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# 取出现次数最多的模型名称
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most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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logger.info(f"最近20天预测残差最低的模型名称：{most_common_model}")
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# 保存结果到数据库
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@ -148,7 +143,7 @@ def predict_main():
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# # 模型评估
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logger.info('训练数据绘图ing')
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model_results3 = model_losss_juxiting(sqlitedb)
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model_results3 = model_losss(sqlitedb)
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logger.info('训练数据绘图end')
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# 模型报告
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@ -183,7 +178,7 @@ def predict_main():
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file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
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ssl=ssl,
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)
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m.send_mail()
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# m.send_mail()
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if __name__ == '__main__':
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@ -11,4 +11,5 @@ while True:
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predict_main()
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time.sleep(60)
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except:
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pass
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time.sleep(3600)
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predict_main()
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