八大维度自有指标
This commit is contained in:
workpc
parent
05bfeebcb0
commit
8074647329
@ -89,8 +89,7 @@ data = {
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ClassifyId = 1214
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ClassifyId = 1214
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# 变量定义--线上环境
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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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# 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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# 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_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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@ -111,7 +110,6 @@ ClassifyId = 1214
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# }
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# }
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# upload_data = {
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# upload_data = {
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# "funcModule":'研究报告信息',
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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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# "funcOperation":'上传原油价格预测报告',
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@ -151,7 +149,6 @@ ClassifyId = 1214
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# }
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# }
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# push_data_value_list_data = {
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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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# "funcModule": "数据表信息列表",
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# "funcOperation": "新增",
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# "funcOperation": "新增",
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@ -186,9 +183,6 @@ ClassifyId = 1214
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# }
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# }
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# # 生产环境数据库
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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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# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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# port = 3306
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# port = 3306
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@ -198,8 +192,6 @@ ClassifyId = 1214
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# table_name = 'v_tbl_crude_oil_warning'
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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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# # 变量定义--测试环境
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server_host = '192.168.100.53:8080' # 内网
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server_host = '192.168.100.53:8080' # 内网
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# server_host = '183.242.74.28' # 外网
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# server_host = '183.242.74.28' # 外网
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@ -307,7 +299,7 @@ table_name = 'v_tbl_crude_oil_warning'
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# 开关
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# 开关
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is_train = True # 是否训练
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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_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_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_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_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_fivemodels = False # 是否使用之前保存的最佳的5个模型
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@ -415,4 +407,3 @@ logger.addHandler(file_handler)
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logger.addHandler(console_handler)
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logger.addHandler(console_handler)
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# logger.info('当前配置：'+settings)
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# logger.info('当前配置：'+settings)
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@ -89,8 +89,7 @@ data = {
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ClassifyId = 1214
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ClassifyId = 1214
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# 变量定义--线上环境
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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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# 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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# 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_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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@ -111,7 +110,6 @@ ClassifyId = 1214
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# }
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# }
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# upload_data = {
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# upload_data = {
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# "funcModule":'研究报告信息',
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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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# "funcOperation":'上传原油价格预测报告',
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@ -151,7 +149,6 @@ ClassifyId = 1214
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# }
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# }
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# push_data_value_list_data = {
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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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# "funcModule": "数据表信息列表",
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# "funcOperation": "新增",
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# "funcOperation": "新增",
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@ -195,9 +192,6 @@ ClassifyId = 1214
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# table_name = 'v_tbl_crude_oil_warning'
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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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# # 变量定义--测试环境
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server_host = '192.168.100.53:8080' # 内网
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server_host = '192.168.100.53:8080' # 内网
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# server_host = '183.242.74.28' # 外网
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# server_host = '183.242.74.28' # 外网
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@ -304,7 +298,7 @@ table_name = 'v_tbl_crude_oil_warning'
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# 开关
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# 开关
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is_train = True # 是否训练
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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_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_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_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_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_fivemodels = False # 是否使用之前保存的最佳的5个模型
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@ -265,7 +265,7 @@ def upload_report_data(token, upload_data):
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config.logger.info(f"token:{token}")
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config.logger.info(f"token:{token}")
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# 打印日志，显示要上传的报告数据
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# 打印日志，显示要上传的报告数据
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config.logger.info(f"upload_data:{upload_data}")
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# config.logger.info(f"upload_data:{upload_data}")
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# 发送POST请求，上传报告数据
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# 发送POST请求，上传报告数据
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upload_res = requests.post(
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upload_res = requests.post(
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@ -275,7 +275,7 @@ def upload_report_data(token, upload_data):
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upload_res = json.loads(upload_res.text)
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upload_res = json.loads(upload_res.text)
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# 打印日志，显示响应内容
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# 打印日志，显示响应内容
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config.logger.info(upload_res)
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# config.logger.info(upload_res)
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# 如果上传成功，返回响应对象
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# 如果上传成功，返回响应对象
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if upload_res:
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if upload_res:
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@ -808,7 +808,8 @@ def datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time='', y
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# 判断对应的 'ds' 是否大于 start_date
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# 判断对应的 'ds' 是否大于 start_date
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if df.loc[first_valid_index, 'ds'] > start_date:
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if df.loc[first_valid_index, 'ds'] > start_date:
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df.drop(columns=[col], inplace=True)
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df.drop(columns=[col], inplace=True)
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config.logger.info(f'删除开始时间没有数据的列：{col},第一条数据日期为：{df.loc[first_valid_index, "ds"]}')
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config.logger.info(
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f'删除开始时间没有数据的列：{col},第一条数据日期为：{df.loc[first_valid_index, "ds"]}')
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config.logger.info(f'删除开始时间没有数据的列后数据量：{df.shape}')
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config.logger.info(f'删除开始时间没有数据的列后数据量：{df.shape}')
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@ -1200,6 +1201,7 @@ class Config:
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@property
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@property
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def warning_data(self): return global_config['warning_data']
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def warning_data(self): return global_config['warning_data']
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# 查询接口
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# 查询接口
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@property
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@property
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def query_data_list_item_nos_url(
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def query_data_list_item_nos_url(
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self): return global_config['query_data_list_item_nos_url']
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self): return global_config['query_data_list_item_nos_url']
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@ -1219,8 +1221,8 @@ class Config:
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@property
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@property
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def bdwd_items(self): return global_config['bdwd_items']
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def bdwd_items(self): return global_config['bdwd_items']
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# 字段映射
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# 字段映射
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@property
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@property
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def offsite_col(self): return global_config['offsite_col']
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def offsite_col(self): return global_config['offsite_col']
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@property
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@property
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@ -1247,7 +1249,6 @@ class Config:
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def is_bdwd(self): return global_config['is_bdwd']
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def is_bdwd(self): return global_config['is_bdwd']
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config = Config()
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config = Config()
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@ -2303,7 +2304,6 @@ def get_baichuan_data(baichuanidnamedict):
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return df1
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return df1
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def get_bdwd_predict_data():
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def get_bdwd_predict_data():
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# 获取认证头部信息
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# 获取认证头部信息
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token = get_head_auth_report()
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token = get_head_auth_report()
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@ -2315,21 +2315,20 @@ def get_bdwd_predict_data():
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config.logger.info("获取八大维度数据...")
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config.logger.info("获取八大维度数据...")
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# 打印日志，显示上传的URL
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# 打印日志，显示上传的URL
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config.logger.info(f"query_data_list_item_nos_url:{config.query_data_list_item_nos_url}")
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config.logger.info(
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f"query_data_list_item_nos_url:{config.query_data_list_item_nos_url}")
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# 打印日志，显示认证头部信息
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# 打印日志，显示认证头部信息
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config.logger.info(f"token:{token}")
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config.logger.info(f"token:{token}")
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# 打印日志，显示要查询的数据项
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# 打印日志，显示要查询的数据项
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config.logger.info(f"query_data_list_item_nos_data:{query_data_list_item_nos_data}")
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config.logger.info(
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f"query_data_list_item_nos_data:{query_data_list_item_nos_data}")
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# 发送POST请求，上传预警数据
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# 发送POST请求，上传预警数据
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respose = requests.post(
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respose = requests.post(
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url=config.upload_warning_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 15))
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url=config.upload_warning_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 15))
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# 如果上传成功，返回响应对象
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# 如果上传成功，返回响应对象
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if respose:
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if respose:
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# 处理返回结果为df
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# 处理返回结果为df
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@ -2381,11 +2380,13 @@ def get_bdwd_predict_data():
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df2['date'] = df2['date'].dt.strftime('%Y-%m-%d')
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df2['date'] = df2['date'].dt.strftime('%Y-%m-%d')
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# df = pd.merge(df, df2, how='left', on='date')
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# df = pd.merge(df, df2, how='left', on='date')
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# 更改列名：
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# 更改列名：
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df2.rename(columns={'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',}, inplace=True)
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df2.rename(columns={'yyycbdwdbz': '本周', 'yyycbdwdcey': '次二月', 'yyycbdwdcr': '次日', 'yyycbdwdcsiy': '次四月',
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'yyycbdwdcsy': '次三月', 'yyycbdwdcy': '次月', 'yyycbdwdcz': '次周', 'yyycbdwdgz': '隔周', }, inplace=True)
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# df2.rename(columns={'原油大数据预测|FORECAST|PRICE|W':'本周','原油大数据预测|FORECAST|PRICE|M_2':'次二月','原油大数据预测|FORECAST|PRICE|T':'次日','原油大数据预测|FORECAST|PRICE|M_4':'次四月','原油大数据预测|FORECAST|PRICE|M_3':'次三月','原油大数据预测|FORECAST|PRICE|M_1':'次月','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',}, inplace=True)
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# df2.rename(columns={'原油大数据预测|FORECAST|PRICE|W':'本周','原油大数据预测|FORECAST|PRICE|M_2':'次二月','原油大数据预测|FORECAST|PRICE|T':'次日','原油大数据预测|FORECAST|PRICE|M_4':'次四月','原油大数据预测|FORECAST|PRICE|M_3':'次三月','原油大数据预测|FORECAST|PRICE|M_1':'次月','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',}, inplace=True)
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# 更改显示顺序
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# 更改显示顺序
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# 过滤掉不存在的列
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# 过滤掉不存在的列
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desired_columns = ['date','次日','本周','次周','隔周','次月','次二月','次三月','次四月']
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desired_columns = ['date', '次日', '本周',
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'次周', '隔周', '次月', '次二月', '次三月', '次四月']
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existing_columns = [col for col in desired_columns if col in df2.columns]
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existing_columns = [col for col in desired_columns if col in df2.columns]
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# 更改显示顺序
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# 更改显示顺序
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})
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})
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def push_market_value():
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def push_market_value():
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config.logger.info('发送预测结果到市场信息平台')
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config.logger.info('发送预测结果到市场信息平台')
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# 读取预测数据和模型评估数据
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# 读取预测数据和模型评估数据
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@ -142,8 +139,6 @@ def push_market_value():
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config.logger.error(f"推送数据失败: {e}")
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config.logger.error(f"推送数据失败: {e}")
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def predict_main():
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def predict_main():
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"""
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"""
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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。
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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。
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@ -450,15 +445,14 @@ def predict_main():
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if __name__ == '__main__':
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if __name__ == '__main__':
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# global end_time
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# global end_time
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# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
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# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
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for i_time in pd.date_range('2025-4-7', '2025-4-8', freq='B'):
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# for i_time in pd.date_range('2025-4-14', '2025-4-15', freq='B'):
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try:
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# try:
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global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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predict_main()
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except Exception as e:
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logger.info(f'预测失败：{e}')
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continue
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# predict_main()
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# predict_main()
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# except Exception as e:
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# logger.info(f'预测失败：{e}')
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# continue
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predict_main()
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||||||
|
|
||||||
# push_market_value()
|
# push_market_value()
|
||||||
|
|
||||||
|
|||||||
@ -415,7 +415,7 @@ def predict_main():
|
|||||||
end_time=global_config['end_time'],
|
end_time=global_config['end_time'],
|
||||||
)
|
)
|
||||||
|
|
||||||
logger.info('模型训练完成')
|
# logger.info('模型训练完成')
|
||||||
|
|
||||||
logger.info('训练数据绘图ing')
|
logger.info('训练数据绘图ing')
|
||||||
model_results3 = model_losss(sqlitedb, end_time=end_time)
|
model_results3 = model_losss(sqlitedb, end_time=end_time)
|
||||||
@ -423,21 +423,19 @@ def predict_main():
|
|||||||
|
|
||||||
push_market_value()
|
push_market_value()
|
||||||
|
|
||||||
# 模型报告
|
# # 模型报告
|
||||||
logger.info('制作报告ing')
|
# logger.info('制作报告ing')
|
||||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
# title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||||
reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
|
# reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
|
||||||
reportname = reportname.replace(':', '-') # 替换冒号
|
# reportname = reportname.replace(':', '-') # 替换冒号
|
||||||
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
# brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||||
reportname=reportname,
|
# reportname=reportname,
|
||||||
inputsize = global_config['horizon'],
|
# inputsize = global_config['horizon'],
|
||||||
sqlitedb=sqlitedb
|
# 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')
|
||||||
|
|
||||||
# # LSTM 单变量模型
|
# # LSTM 单变量模型
|
||||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||||
@ -464,7 +462,7 @@ def predict_main():
|
|||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
# global end_time
|
# global end_time
|
||||||
# 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('2025-3-29', '2025-4-8', freq='B'):
|
for i_time in pd.date_range('2025-3-13', '2025-3-31', freq='B'):
|
||||||
try:
|
try:
|
||||||
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||||
predict_main()
|
predict_main()
|
||||||
@ -473,4 +471,3 @@ if __name__ == '__main__':
|
|||||||
continue
|
continue
|
||||||
|
|
||||||
# predict_main()
|
# predict_main()
|
||||||
|
|
||||||
|
|||||||
@ -176,234 +176,234 @@ def predict_main():
|
|||||||
返回:
|
返回:
|
||||||
None
|
None
|
||||||
"""
|
"""
|
||||||
# end_time = global_config['end_time']
|
end_time = global_config['end_time']
|
||||||
|
|
||||||
# signature = BinanceAPI(APPID, SECRET)
|
signature = BinanceAPI(APPID, SECRET)
|
||||||
# etadata = EtaReader(signature=signature,
|
etadata = EtaReader(signature=signature,
|
||||||
# classifylisturl=global_config['classifylisturl'],
|
classifylisturl=global_config['classifylisturl'],
|
||||||
# classifyidlisturl=global_config['classifyidlisturl'],
|
classifyidlisturl=global_config['classifyidlisturl'],
|
||||||
# edbcodedataurl=global_config['edbcodedataurl'],
|
edbcodedataurl=global_config['edbcodedataurl'],
|
||||||
# edbcodelist=global_config['edbcodelist'],
|
edbcodelist=global_config['edbcodelist'],
|
||||||
# edbdatapushurl=global_config['edbdatapushurl'],
|
edbdatapushurl=global_config['edbdatapushurl'],
|
||||||
# edbdeleteurl=global_config['edbdeleteurl'],
|
edbdeleteurl=global_config['edbdeleteurl'],
|
||||||
# edbbusinessurl=global_config['edbbusinessurl'],
|
edbbusinessurl=global_config['edbbusinessurl'],
|
||||||
# classifyId=global_config['ClassifyId'],
|
classifyId=global_config['ClassifyId'],
|
||||||
# )
|
)
|
||||||
# # 获取数据
|
# 获取数据
|
||||||
# if is_eta:
|
if is_eta:
|
||||||
# logger.info('从eta获取数据...')
|
logger.info('从eta获取数据...')
|
||||||
|
|
||||||
# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
|
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
|
||||||
# data_set=data_set, dataset=dataset) # 原始数据，未处理
|
data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||||
|
|
||||||
# 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:
|
||||||
# # 如果是测试环境，最高价最低价取excel文档
|
# 如果是测试环境，最高价最低价取excel文档
|
||||||
# if server_host == '192.168.100.53':
|
if server_host == '192.168.100.53':
|
||||||
# logger.info('从excel文档获取最高价最低价')
|
logger.info('从excel文档获取最高价最低价')
|
||||||
# df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||||
# else:
|
else:
|
||||||
# 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')
|
||||||
# df_zhibiaoshuju = get_market_data(
|
df_zhibiaoshuju = get_market_data(
|
||||||
# end_time, df_zhibiaoshuju)
|
end_time, df_zhibiaoshuju)
|
||||||
|
|
||||||
# except:
|
except:
|
||||||
# logger.info('最高最低价拼接失败')
|
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:
|
||||||
# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||||
# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||||
|
|
||||||
# # 数据处理
|
# 数据处理
|
||||||
# df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||||
# end_time=end_time)
|
end_time=end_time)
|
||||||
|
|
||||||
# else:
|
else:
|
||||||
# # 读取数据
|
# 读取数据
|
||||||
# logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||||
# df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||||
# is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||||
|
|
||||||
# # 更改预测列名称
|
# 更改预测列名称
|
||||||
# df.rename(columns={y: 'y'}, inplace=True)
|
df.rename(columns={y: 'y'}, inplace=True)
|
||||||
|
|
||||||
# if is_edbnamelist:
|
if is_edbnamelist:
|
||||||
# df = df[edbnamelist]
|
df = df[edbnamelist]
|
||||||
# df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
||||||
# # 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)
|
||||||
# # 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')
|
||||||
# return None
|
return None
|
||||||
|
|
||||||
# # 将最新真实值保存到数据库
|
# 将最新真实值保存到数据库
|
||||||
# if not sqlitedb.check_table_exists('trueandpredict'):
|
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||||
# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||||
# else:
|
else:
|
||||||
# for row in first_row.itertuples(index=False):
|
for row in first_row.itertuples(index=False):
|
||||||
# row_dict = row._asdict()
|
row_dict = row._asdict()
|
||||||
# config.logger.info(f'要保存的真实值：{row_dict}')
|
config.logger.info(f'要保存的真实值：{row_dict}')
|
||||||
# # 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
# 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
||||||
# if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
||||||
|
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||||
|
elif not isinstance(row_dict['ds'], str):
|
||||||
|
try:
|
||||||
|
row_dict['ds'] = pd.to_datetime(
|
||||||
|
row_dict['ds']).strftime('%Y-%m-%d')
|
||||||
|
except:
|
||||||
|
logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
||||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||||
# elif not isinstance(row_dict['ds'], str):
|
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||||
|
check_query = sqlitedb.select_data(
|
||||||
|
'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||||
|
if len(check_query) > 0:
|
||||||
|
set_clause = ", ".join(
|
||||||
|
[f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||||
|
sqlitedb.update_data(
|
||||||
|
'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||||
|
continue
|
||||||
|
sqlitedb.insert_data('trueandpredict', tuple(
|
||||||
|
row_dict.values()), columns=row_dict.keys())
|
||||||
|
|
||||||
|
# 更新accuracy表的y值
|
||||||
|
if not sqlitedb.check_table_exists('accuracy'):
|
||||||
|
pass
|
||||||
|
else:
|
||||||
|
update_y = sqlitedb.select_data(
|
||||||
|
'accuracy', where_condition="y is null")
|
||||||
|
if len(update_y) > 0:
|
||||||
|
logger.info('更新accuracy表的y值')
|
||||||
|
# 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:
|
||||||
# row_dict['ds'] = pd.to_datetime(
|
for row in update_y.itertuples(index=False):
|
||||||
# row_dict['ds']).strftime('%Y-%m-%d')
|
try:
|
||||||
# except:
|
row_dict = row._asdict()
|
||||||
# logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
||||||
# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
||||||
# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
||||||
# check_query = sqlitedb.select_data(
|
sqlitedb.update_data(
|
||||||
# 'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||||
# if len(check_query) > 0:
|
except:
|
||||||
# set_clause = ", ".join(
|
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||||
# [f"{key} = '{value}'" for key, value in row_dict.items()])
|
# except Exception as e:
|
||||||
# sqlitedb.update_data(
|
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||||
# 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
|
||||||
# continue
|
|
||||||
# sqlitedb.insert_data('trueandpredict', tuple(
|
|
||||||
# row_dict.values()), columns=row_dict.keys())
|
|
||||||
|
|
||||||
# # 更新accuracy表的y值
|
# 判断当前日期是不是周一
|
||||||
# if not sqlitedb.check_table_exists('accuracy'):
|
is_weekday = datetime.datetime.now().weekday() == 0
|
||||||
# pass
|
if is_weekday:
|
||||||
# else:
|
logger.info('今天是周一，更新预测模型')
|
||||||
# update_y = sqlitedb.select_data(
|
# 计算最近60天预测残差最低的模型名称
|
||||||
# 'accuracy', where_condition="y is null")
|
model_results = sqlitedb.select_data(
|
||||||
# if len(update_y) > 0:
|
'trueandpredict', order_by="ds DESC", limit="60")
|
||||||
# logger.info('更新accuracy表的y值')
|
# 删除空值率为90%以上的列
|
||||||
# # 找到update_y 中ds且df中的y的行
|
if len(model_results) > 10:
|
||||||
# update_y = update_y[update_y['ds'] <= end_time]
|
model_results = model_results.dropna(
|
||||||
# logger.info(f'要更新y的信息：{update_y}')
|
thresh=len(model_results)*0.1, axis=1)
|
||||||
# # try:
|
# 删除空行
|
||||||
# for row in update_y.itertuples(index=False):
|
model_results = model_results.dropna()
|
||||||
# try:
|
modelnames = model_results.columns.to_list()[2:-2]
|
||||||
# row_dict = row._asdict()
|
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||||
# yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
model_results[col] = model_results[col].astype(np.float32)
|
||||||
# LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
# 计算每个预测值与真实值之间的偏差率
|
||||||
# HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
for model in modelnames:
|
||||||
# sqlitedb.update_data(
|
model_results[f'{model}_abs_error_rate'] = abs(
|
||||||
# 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
model_results['y'] - model_results[model]) / model_results['y']
|
||||||
# except:
|
# 获取每行对应的最小偏差率值
|
||||||
# logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
min_abs_error_rate_values = model_results.apply(
|
||||||
# # except Exception as e:
|
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||||
# # logger.info(f'更新accuracy表的y值失败：{e}')
|
# 获取每行对应的最小偏差率值对应的列名
|
||||||
|
min_abs_error_rate_column_name = model_results.apply(
|
||||||
|
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||||
|
# 将列名索引转换为列名
|
||||||
|
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||||
|
lambda x: x.split('_')[0])
|
||||||
|
# 取出现次数最多的模型名称
|
||||||
|
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||||
|
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||||
|
# 保存结果到数据库
|
||||||
|
if not sqlitedb.check_table_exists('most_model'):
|
||||||
|
sqlitedb.create_table(
|
||||||
|
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||||
|
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||||
|
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||||
|
|
||||||
# # 判断当前日期是不是周一
|
try:
|
||||||
# is_weekday = datetime.datetime.now().weekday() == 0
|
if is_weekday:
|
||||||
# if is_weekday:
|
# if True:
|
||||||
# logger.info('今天是周一，更新预测模型')
|
logger.info('今天是周一，发送特征预警')
|
||||||
# # 计算最近60天预测残差最低的模型名称
|
# 上传预警信息到数据库
|
||||||
# model_results = sqlitedb.select_data(
|
warning_data_df = df_zhibiaoliebiao.copy()
|
||||||
# 'trueandpredict', order_by="ds DESC", limit="60")
|
warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
||||||
# # 删除空值率为90%以上的列
|
'指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
||||||
# if len(model_results) > 10:
|
# 重命名列名
|
||||||
# model_results = model_results.dropna(
|
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
||||||
# thresh=len(model_results)*0.1, axis=1)
|
'更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||||
# # 删除空行
|
from sqlalchemy import create_engine
|
||||||
# model_results = model_results.dropna()
|
import urllib
|
||||||
# modelnames = model_results.columns.to_list()[2:-2]
|
global password
|
||||||
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
if '@' in password:
|
||||||
# model_results[col] = model_results[col].astype(np.float32)
|
password = urllib.parse.quote_plus(password)
|
||||||
# # 计算每个预测值与真实值之间的偏差率
|
|
||||||
# for model in modelnames:
|
|
||||||
# model_results[f'{model}_abs_error_rate'] = abs(
|
|
||||||
# model_results['y'] - model_results[model]) / model_results['y']
|
|
||||||
# # 获取每行对应的最小偏差率值
|
|
||||||
# min_abs_error_rate_values = model_results.apply(
|
|
||||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
|
||||||
# # 获取每行对应的最小偏差率值对应的列名
|
|
||||||
# min_abs_error_rate_column_name = model_results.apply(
|
|
||||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
|
||||||
# # 将列名索引转换为列名
|
|
||||||
# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
|
||||||
# lambda x: x.split('_')[0])
|
|
||||||
# # 取出现次数最多的模型名称
|
|
||||||
# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
|
||||||
# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
|
||||||
# # 保存结果到数据库
|
|
||||||
# if not sqlitedb.check_table_exists('most_model'):
|
|
||||||
# sqlitedb.create_table(
|
|
||||||
# 'most_model', columns="ds datetime, most_common_model TEXT")
|
|
||||||
# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
|
||||||
# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
|
||||||
|
|
||||||
# try:
|
engine = create_engine(
|
||||||
# if is_weekday:
|
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||||
# # if True:
|
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||||
# logger.info('今天是周一，发送特征预警')
|
"%Y-%m-%d %H:%M:%S")
|
||||||
# # 上传预警信息到数据库
|
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||||
# warning_data_df = df_zhibiaoliebiao.copy()
|
# 插入数据之前查询表数据然后新增id列
|
||||||
# warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||||
# '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
if not existing_data.empty:
|
||||||
# # 重命名列名
|
max_id = existing_data['ID'].astype(int).max()
|
||||||
# warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
warning_data_df['ID'] = range(
|
||||||
# '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
max_id + 1, max_id + 1 + len(warning_data_df))
|
||||||
# from sqlalchemy import create_engine
|
else:
|
||||||
# import urllib
|
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||||
# global password
|
warning_data_df.to_sql(
|
||||||
# if '@' in password:
|
table_name, con=engine, if_exists='append', index=False)
|
||||||
# password = urllib.parse.quote_plus(password)
|
if is_update_warning_data:
|
||||||
|
upload_warning_info(len(warning_data_df))
|
||||||
|
except:
|
||||||
|
logger.info('上传预警信息到数据库失败')
|
||||||
|
|
||||||
# engine = create_engine(
|
if is_corr:
|
||||||
# f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
df = corr_feature(df=df)
|
||||||
# warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
|
||||||
# "%Y-%m-%d %H:%M:%S")
|
|
||||||
# warning_data_df['TENANT_CODE'] = 'T0004'
|
|
||||||
# # 插入数据之前查询表数据然后新增id列
|
|
||||||
# existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
|
||||||
# if not existing_data.empty:
|
|
||||||
# max_id = existing_data['ID'].astype(int).max()
|
|
||||||
# warning_data_df['ID'] = range(
|
|
||||||
# max_id + 1, max_id + 1 + len(warning_data_df))
|
|
||||||
# else:
|
|
||||||
# warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
|
||||||
# warning_data_df.to_sql(
|
|
||||||
# table_name, con=engine, if_exists='append', index=False)
|
|
||||||
# if is_update_warning_data:
|
|
||||||
# upload_warning_info(len(warning_data_df))
|
|
||||||
# except:
|
|
||||||
# logger.info('上传预警信息到数据库失败')
|
|
||||||
|
|
||||||
# if is_corr:
|
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||||
# df = corr_feature(df=df)
|
logger.info(f"开始训练模型...")
|
||||||
|
row, col = df.shape
|
||||||
|
|
||||||
# df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||||
# logger.info(f"开始训练模型...")
|
ex_Model(df,
|
||||||
# row, col = df.shape
|
horizon=global_config['horizon'],
|
||||||
|
input_size=global_config['input_size'],
|
||||||
|
train_steps=global_config['train_steps'],
|
||||||
|
val_check_steps=global_config['val_check_steps'],
|
||||||
|
early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||||
|
is_debug=global_config['is_debug'],
|
||||||
|
dataset=global_config['dataset'],
|
||||||
|
is_train=global_config['is_train'],
|
||||||
|
is_fivemodels=global_config['is_fivemodels'],
|
||||||
|
val_size=global_config['val_size'],
|
||||||
|
test_size=global_config['test_size'],
|
||||||
|
settings=global_config['settings'],
|
||||||
|
now=now,
|
||||||
|
etadata=etadata,
|
||||||
|
modelsindex=global_config['modelsindex'],
|
||||||
|
data=data,
|
||||||
|
is_eta=global_config['is_eta'],
|
||||||
|
end_time=global_config['end_time'],
|
||||||
|
)
|
||||||
|
|
||||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
logger.info('模型训练完成')
|
||||||
# ex_Model(df,
|
|
||||||
# horizon=global_config['horizon'],
|
|
||||||
# input_size=global_config['input_size'],
|
|
||||||
# train_steps=global_config['train_steps'],
|
|
||||||
# val_check_steps=global_config['val_check_steps'],
|
|
||||||
# early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
|
||||||
# is_debug=global_config['is_debug'],
|
|
||||||
# dataset=global_config['dataset'],
|
|
||||||
# is_train=global_config['is_train'],
|
|
||||||
# is_fivemodels=global_config['is_fivemodels'],
|
|
||||||
# val_size=global_config['val_size'],
|
|
||||||
# test_size=global_config['test_size'],
|
|
||||||
# settings=global_config['settings'],
|
|
||||||
# now=now,
|
|
||||||
# etadata=etadata,
|
|
||||||
# modelsindex=global_config['modelsindex'],
|
|
||||||
# data=data,
|
|
||||||
# is_eta=global_config['is_eta'],
|
|
||||||
# end_time=global_config['end_time'],
|
|
||||||
# )
|
|
||||||
|
|
||||||
# logger.info('模型训练完成')
|
logger.info('训练数据绘图ing')
|
||||||
|
model_results3 = model_losss(sqlitedb, end_time=end_time)
|
||||||
# logger.info('训练数据绘图ing')
|
logger.info('训练数据绘图end')
|
||||||
# model_results3 = model_losss(sqlitedb, end_time=end_time)
|
|
||||||
# logger.info('训练数据绘图end')
|
|
||||||
|
|
||||||
# # 模型报告
|
# # 模型报告
|
||||||
logger.info('制作报告ing')
|
logger.info('制作报告ing')
|
||||||
@ -421,7 +421,7 @@ def predict_main():
|
|||||||
logger.info('制作报告end')
|
logger.info('制作报告end')
|
||||||
logger.info('模型训练完成')
|
logger.info('模型训练完成')
|
||||||
|
|
||||||
# push_market_value()
|
push_market_value()
|
||||||
|
|
||||||
# 发送邮件
|
# 发送邮件
|
||||||
# m = SendMail(
|
# m = SendMail(
|
||||||
@ -439,12 +439,12 @@ def predict_main():
|
|||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
# global end_time
|
# global end_time
|
||||||
# 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('2025-1-1', '2025-3-29', freq='B'):
|
for i_time in pd.date_range('2025-2-1', '2025-3-31', freq='B'):
|
||||||
# try:
|
try:
|
||||||
# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
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
|
|
||||||
|
|
||||||
predict_main()
|
predict_main()
|
||||||
|
except Exception as e:
|
||||||
|
logger.info(f'预测失败：{e}')
|
||||||
|
continue
|
||||||
|
|
||||||
|
# predict_main()
|
||||||
|
|||||||
@ -173,10 +173,14 @@ if __name__ == '__main__':
|
|||||||
# cal_time_series(df, 7) # 模型调用
|
# cal_time_series(df, 7) # 模型调用
|
||||||
# 数据测试2（从excel中读取）:
|
# 数据测试2（从excel中读取）:
|
||||||
path = r'D:\code\PriceForecast-svn\yuanyoudataset\指标数据.csv'
|
path = r'D:\code\PriceForecast-svn\yuanyoudataset\指标数据.csv'
|
||||||
y = 'Brent活跃合约'
|
# y = 'Brent活跃合约'
|
||||||
|
y = 'y'
|
||||||
df = pd.read_csv(path)
|
df = pd.read_csv(path)
|
||||||
df.rename(columns={f'{y}': 'deal_data'}, inplace=True)
|
print(df.columns)
|
||||||
df = df[['ds', f'{y}']]
|
# df.rename(columns={f'{y}': 'deal_data'}, inplace=True)
|
||||||
|
df.rename(columns={'y': 'deal_data'}, inplace=True)
|
||||||
|
# df = df[['ds', f'{y}']]
|
||||||
|
df = df[['ds', 'deal_data']]
|
||||||
print(df.tail())
|
print(df.tail())
|
||||||
df.set_index(['ds'], inplace=True) # 设置索引
|
df.set_index(['ds'], inplace=True) # 设置索引
|
||||||
cal_time_series(df, 7) # 模型调用
|
cal_time_series(df, 7) # 模型调用
|
||||||
@ -1243,7 +1243,11 @@ def model_losss(sqlitedb, end_time):
|
|||||||
df4.to_sql("accuracy_rote", con=sqlitedb.connection,
|
df4.to_sql("accuracy_rote", con=sqlitedb.connection,
|
||||||
if_exists='append', index=False)
|
if_exists='append', index=False)
|
||||||
create_dates, ds_dates = get_week_date(end_time)
|
create_dates, ds_dates = get_week_date(end_time)
|
||||||
|
try:
|
||||||
_get_accuracy_rate(df, create_dates, ds_dates)
|
_get_accuracy_rate(df, create_dates, ds_dates)
|
||||||
|
except Exception as e:
|
||||||
|
config.logger.info(f'准确率计算错误{e}')
|
||||||
|
|
||||||
|
|
||||||
def _add_abs_error_rate():
|
def _add_abs_error_rate():
|
||||||
# 计算每个预测值与真实值之间的偏差率
|
# 计算每个预测值与真实值之间的偏差率
|
||||||
@ -2502,16 +2506,16 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
|||||||
config.dataset, reportname), pagesize=letter)
|
config.dataset, reportname), pagesize=letter)
|
||||||
doc.build(content)
|
doc.build(content)
|
||||||
# pdf 上传到数字化信息平台
|
# pdf 上传到数字化信息平台
|
||||||
# try:
|
try:
|
||||||
# if config.is_update_report:
|
if config.is_update_report:
|
||||||
# with open(os.path.join(config.dataset, reportname), 'rb') as f:
|
with open(os.path.join(config.dataset, reportname), 'rb') as f:
|
||||||
# base64_data = base64.b64encode(f.read()).decode('utf-8')
|
base64_data = base64.b64encode(f.read()).decode('utf-8')
|
||||||
# config.upload_data["data"]["fileBase64"] = base64_data
|
config.upload_data["data"]["fileBase64"] = base64_data
|
||||||
# config.upload_data["data"]["fileName"] = reportname
|
config.upload_data["data"]["fileName"] = reportname
|
||||||
# token = get_head_auth_report()
|
token = get_head_auth_report()
|
||||||
# upload_report_data(token, config.upload_data)
|
upload_report_data(token, config.upload_data)
|
||||||
# except TimeoutError as e:
|
except TimeoutError as e:
|
||||||
# print(f"请求超时: {e}")
|
print(f"请求超时: {e}")
|
||||||
|
|
||||||
|
|
||||||
@exception_logger
|
@exception_logger
|
||||||
|
|||||||
173
test/pushdata.py
173
test/pushdata.py
@ -10,6 +10,143 @@ import glob
|
|||||||
import torch
|
import torch
|
||||||
torch.set_float32_matmul_precision("high")
|
torch.set_float32_matmul_precision("high")
|
||||||
|
|
||||||
|
|
||||||
|
names = [
|
||||||
|
'价格预测NHITS模型-次周',
|
||||||
|
'价格预测Informer模型-次周',
|
||||||
|
'价格预测LSTM模型-次周',
|
||||||
|
'价格预测iTransformer模型-次周',
|
||||||
|
'价格预测TSMixer模型-次周',
|
||||||
|
'价格预测TSMixerx模型-次周',
|
||||||
|
'价格预测PatchTST模型-次周',
|
||||||
|
'价格预测RNN模型-次周',
|
||||||
|
'价格预测GRU模型-次周',
|
||||||
|
'价格预测TCN模型-次周',
|
||||||
|
'价格预测BiTCN模型-次周',
|
||||||
|
'价格预测DilatedRNN模型-次周',
|
||||||
|
'价格预测MLP模型-次周',
|
||||||
|
'价格预测DLinear模型-次周',
|
||||||
|
'价格预测NLinear模型-次周',
|
||||||
|
'价格预测TFT模型-次周',
|
||||||
|
'价格预测FEDformer模型-次周',
|
||||||
|
'价格预测StemGNN模型-次周',
|
||||||
|
'价格预测MLPMultivariate模型-次周',
|
||||||
|
'价格预测TiDE模型-次周',
|
||||||
|
'价格预测DeepNPTS模型-次周',
|
||||||
|
'价格预测NBEATS模型-次周',
|
||||||
|
'价格预测NHITS模型-隔周',
|
||||||
|
'价格预测Informer模型-隔周',
|
||||||
|
'价格预测LSTM模型-隔周',
|
||||||
|
'价格预测iTransformer模型-隔周',
|
||||||
|
'价格预测TSMixer模型-隔周',
|
||||||
|
'价格预测TSMixerx模型-隔周',
|
||||||
|
'价格预测PatchTST模型-隔周',
|
||||||
|
'价格预测RNN模型-隔周',
|
||||||
|
'价格预测GRU模型-隔周',
|
||||||
|
'价格预测TCN模型-隔周',
|
||||||
|
'价格预测BiTCN模型-隔周',
|
||||||
|
'价格预测DilatedRNN模型-隔周',
|
||||||
|
'价格预测MLP模型-隔周',
|
||||||
|
'价格预测DLinear模型-隔周',
|
||||||
|
'价格预测NLinear模型-隔周',
|
||||||
|
'价格预测TFT模型-隔周',
|
||||||
|
'价格预测FEDformer模型-隔周',
|
||||||
|
'价格预测StemGNN模型-隔周',
|
||||||
|
'价格预测MLPMultivariate模型-隔周',
|
||||||
|
'价格预测TiDE模型-隔周',
|
||||||
|
'价格预测DeepNPTS模型-隔周',
|
||||||
|
'价格预测NBEATS模型-隔周',
|
||||||
|
'价格预测NHITS模型-次月',
|
||||||
|
'价格预测Informer模型-次月',
|
||||||
|
'价格预测LSTM模型-次月',
|
||||||
|
'价格预测iTransformer模型-次月',
|
||||||
|
'价格预测TSMixer模型-次月',
|
||||||
|
'价格预测TSMixerx模型-次月',
|
||||||
|
'价格预测PatchTST模型-次月',
|
||||||
|
'价格预测RNN模型-次月',
|
||||||
|
'价格预测GRU模型-次月',
|
||||||
|
'价格预测TCN模型-次月',
|
||||||
|
'价格预测BiTCN模型-次月',
|
||||||
|
'价格预测DilatedRNN模型-次月',
|
||||||
|
'价格预测MLP模型-次月',
|
||||||
|
'价格预测DLinear模型-次月',
|
||||||
|
'价格预测NLinear模型-次月',
|
||||||
|
'价格预测TFT模型-次月',
|
||||||
|
'价格预测FEDformer模型-次月',
|
||||||
|
'价格预测StemGNN模型-次月',
|
||||||
|
'价格预测MLPMultivariate模型-次月',
|
||||||
|
'价格预测TiDE模型-次月',
|
||||||
|
'价格预测DeepNPTS模型-次月',
|
||||||
|
'价格预测NBEATS模型-次月',
|
||||||
|
'价格预测NHITS模型-次二月',
|
||||||
|
'价格预测Informer模型-次二月',
|
||||||
|
'价格预测LSTM模型-次二月',
|
||||||
|
'价格预测iTransformer模型-次二月',
|
||||||
|
'价格预测TSMixer模型-次二月',
|
||||||
|
'价格预测TSMixerx模型-次二月',
|
||||||
|
'价格预测PatchTST模型-次二月',
|
||||||
|
'价格预测RNN模型-次二月',
|
||||||
|
'价格预测GRU模型-次二月',
|
||||||
|
'价格预测TCN模型-次二月',
|
||||||
|
'价格预测BiTCN模型-次二月',
|
||||||
|
'价格预测DilatedRNN模型-次二月',
|
||||||
|
'价格预测MLP模型-次二月',
|
||||||
|
'价格预测DLinear模型-次二月',
|
||||||
|
'价格预测NLinear模型-次二月',
|
||||||
|
'价格预测TFT模型-次二月',
|
||||||
|
'价格预测FEDformer模型-次二月',
|
||||||
|
'价格预测StemGNN模型-次二月',
|
||||||
|
'价格预测MLPMultivariate模型-次二月',
|
||||||
|
'价格预测TiDE模型-次二月',
|
||||||
|
'价格预测DeepNPTS模型-次二月',
|
||||||
|
'价格预测NBEATS模型-次二月',
|
||||||
|
'价格预测NHITS模型-次三月',
|
||||||
|
'价格预测Informer模型-次三月',
|
||||||
|
'价格预测LSTM模型-次三月',
|
||||||
|
'价格预测iTransformer模型-次三月',
|
||||||
|
'价格预测TSMixer模型-次三月',
|
||||||
|
'价格预测TSMixerx模型-次三月',
|
||||||
|
'价格预测PatchTST模型-次三月',
|
||||||
|
'价格预测RNN模型-次三月',
|
||||||
|
'价格预测GRU模型-次三月',
|
||||||
|
'价格预测TCN模型-次三月',
|
||||||
|
'价格预测BiTCN模型-次三月',
|
||||||
|
'价格预测DilatedRNN模型-次三月',
|
||||||
|
'价格预测MLP模型-次三月',
|
||||||
|
'价格预测DLinear模型-次三月',
|
||||||
|
'价格预测NLinear模型-次三月',
|
||||||
|
'价格预测TFT模型-次三月',
|
||||||
|
'价格预测FEDformer模型-次三月',
|
||||||
|
'价格预测StemGNN模型-次三月',
|
||||||
|
'价格预测MLPMultivariate模型-次三月',
|
||||||
|
'价格预测TiDE模型-次三月',
|
||||||
|
'价格预测DeepNPTS模型-次三月',
|
||||||
|
'价格预测NBEATS模型-次三月',
|
||||||
|
'价格预测NHITS模型-次四月',
|
||||||
|
'价格预测Informer模型-次四月',
|
||||||
|
'价格预测LSTM模型-次四月',
|
||||||
|
'价格预测iTransformer模型-次四月',
|
||||||
|
'价格预测TSMixer模型-次四月',
|
||||||
|
'价格预测TSMixerx模型-次四月',
|
||||||
|
'价格预测PatchTST模型-次四月',
|
||||||
|
'价格预测RNN模型-次四月',
|
||||||
|
'价格预测GRU模型-次四月',
|
||||||
|
'价格预测TCN模型-次四月',
|
||||||
|
'价格预测BiTCN模型-次四月',
|
||||||
|
'价格预测DilatedRNN模型-次四月',
|
||||||
|
'价格预测MLP模型-次四月',
|
||||||
|
'价格预测DLinear模型-次四月',
|
||||||
|
'价格预测NLinear模型-次四月',
|
||||||
|
'价格预测TFT模型-次四月',
|
||||||
|
'价格预测FEDformer模型-次四月',
|
||||||
|
'价格预测StemGNN模型-次四月',
|
||||||
|
'价格预测MLPMultivariate模型-次四月',
|
||||||
|
'价格预测TiDE模型-次四月',
|
||||||
|
'价格预测DeepNPTS模型-次四月',
|
||||||
|
'价格预测NBEATS模型-次四月',
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
|
|
||||||
signature = BinanceAPI(APPID, SECRET)
|
signature = BinanceAPI(APPID, SECRET)
|
||||||
@ -20,7 +157,8 @@ if __name__ == '__main__':
|
|||||||
edbcodelist=edbcodelist,
|
edbcodelist=edbcodelist,
|
||||||
edbdatapushurl=edbdatapushurl,
|
edbdatapushurl=edbdatapushurl,
|
||||||
edbdeleteurl=edbdeleteurl,
|
edbdeleteurl=edbdeleteurl,
|
||||||
edbbusinessurl = edbbusinessurl
|
edbbusinessurl=edbbusinessurl,
|
||||||
|
classifyId=ClassifyId,
|
||||||
)
|
)
|
||||||
|
|
||||||
models = [
|
models = [
|
||||||
@ -84,21 +222,32 @@ if __name__ == '__main__':
|
|||||||
# # print(data['DataList'])
|
# # print(data['DataList'])
|
||||||
# etadata.push_data(data)
|
# etadata.push_data(data)
|
||||||
|
|
||||||
|
# 新增eta自有指标
|
||||||
|
# list = [{'Date': '2025-04-21', 'Value': 100}]
|
||||||
|
# for name in names:
|
||||||
|
# data['DataList'] = list
|
||||||
|
# data['IndexName'] = name
|
||||||
|
# data['Remark'] = name
|
||||||
|
# # print(data['DataList'])
|
||||||
|
# etadata.push_data(data)
|
||||||
|
# time.sleep(1)
|
||||||
|
|
||||||
# 删除指标
|
# 删除指标
|
||||||
# IndexCodeList = ['SELF0000055']
|
# SELF0000098
|
||||||
# for i in range(1,57):
|
# IndexCodeList = ['SELF0000098']
|
||||||
# if i < 10 : i = f'0{i}'
|
# # for i in range(1,57):
|
||||||
# IndexCodeList.append(f'SELF00000{i}')
|
# # if i < 10 : i = f'0{i}'
|
||||||
|
# # IndexCodeList.append(f'SELF00000{i}')
|
||||||
# print(IndexCodeList)
|
# print(IndexCodeList)
|
||||||
# etadata.del_zhibiao(IndexCodeList)
|
# etadata.del_zhibiao(IndexCodeList)
|
||||||
|
|
||||||
# 删除特定日期的值
|
# 删除特定日期的值
|
||||||
indexcodelist = modelsindex.values()
|
# indexcodelist = modelsindex.values()
|
||||||
for indexcode in indexcodelist:
|
# for indexcode in indexcodelist:
|
||||||
data = {
|
# data = {
|
||||||
"IndexCode": indexcode, #指标编码
|
# "IndexCode": indexcode, # 指标编码
|
||||||
"StartDate": "2020-04-20", #指标需要删除的开始日期（>=），如果开始日期和结束日期相等，那么就是删除该日期
|
# "StartDate": "2020-04-20", # 指标需要删除的开始日期（>=），如果开始日期和结束日期相等，那么就是删除该日期
|
||||||
"EndDate": "2024-05-28" #指标需要删除的结束日期（<=），如果开始日期和结束日期相等，那么就是删除该日期
|
# "EndDate": "2024-05-28" # 指标需要删除的结束日期（<=），如果开始日期和结束日期相等，那么就是删除该日期
|
||||||
}
|
# }
|
||||||
|
|
||||||
# etadata.del_business(data)
|
# etadata.del_business(data)
|
||||||
@ -9,14 +9,15 @@ import time
|
|||||||
def run_predictions(target_date):
|
def run_predictions(target_date):
|
||||||
"""执行三个预测脚本"""
|
"""执行三个预测脚本"""
|
||||||
scripts = [
|
scripts = [
|
||||||
"main_yuanyou.py",
|
# "main_yuanyou.py",
|
||||||
"main_yuanyou_zhoudu.py",
|
"main_yuanyou_zhoudu.py",
|
||||||
"main_yuanyou_yuedu.py"
|
"main_yuanyou_yuedu.py"
|
||||||
]
|
]
|
||||||
|
|
||||||
# 依次执行每个脚本
|
# 依次执行每个脚本
|
||||||
for script in scripts:
|
for script in scripts:
|
||||||
command = [r"C:\Users\Hello\.conda\envs\predict\python", script]
|
# command = [r"C:\Users\Hello\.conda\envs\predict\python", script]
|
||||||
|
command = [r"C:\Users\EDY\.conda\envs\predict\python", script]
|
||||||
subprocess.run(command, check=True)
|
subprocess.run(command, check=True)
|
||||||
|
|
||||||
|
|
||||||
@ -26,10 +27,10 @@ def is_weekday(date):
|
|||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
# start_date = datetime.date(2025, 3, 13)
|
start_date = datetime.date(2025, 2, 1)
|
||||||
# 开始时间取当前时间
|
# 开始时间取当前时间
|
||||||
start_date = datetime.date.today()
|
# start_date = datetime.date.today()
|
||||||
# end_date = datetime.date(2100, 12, 31)
|
end_date = datetime.date(2025, 3, 31)
|
||||||
|
|
||||||
current_date = start_date
|
current_date = start_date
|
||||||
# while current_date <= end_date:
|
# while current_date <= end_date:
|
||||||
@ -46,5 +47,7 @@ if __name__ == "__main__":
|
|||||||
|
|
||||||
# current_date += datetime.timedelta(days=1)
|
# current_date += datetime.timedelta(days=1)
|
||||||
|
|
||||||
|
while current_date <= end_date:
|
||||||
print(f"开始执行 {current_date} 的预测任务")
|
print(f"开始执行 {current_date} 的预测任务")
|
||||||
run_predictions(current_date)
|
run_predictions(current_date)
|
||||||
|
current_date += datetime.timedelta(days=1)
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user