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八大维度自有指标

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workpc 2025-04-21 16:05:55 +08:00
parent 05bfeebcb0
commit 8074647329
10 changed files with 538 additions and 401 deletions
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21
config_jingbo_yuedu.py
View File

@ -89,8 +89,7 @@ data = {
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"
# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
@ -111,7 +110,6 @@ ClassifyId = 1214
# }
# upload_data = {
# "funcModule":'研究报告信息',
# "funcOperation":'上传原油价格预测报告',
@ -119,12 +117,12 @@ ClassifyId = 1214
# "groupNo":'', # 用户组id
# "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
# "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
# "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
# }
# }
@ -151,7 +149,6 @@ ClassifyId = 1214
# }
# push_data_value_list_data = {
# "funcModule": "数据表信息列表",
# "funcOperation": "新增",
@ -186,20 +183,15 @@ ClassifyId = 1214
# }
# # 生产环境数据库
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
# port = 3306
# dbusername ='jingbo'
# dbusername ='jingbo'
# password = 'shihua@123'
# dbname = 'jingbo'
# table_name = 'v_tbl_crude_oil_warning'
# # 变量定义--测试环境
server_host = '192.168.100.53:8080' # 内网
# server_host = '183.242.74.28' # 外网
@ -307,7 +299,7 @@ table_name = 'v_tbl_crude_oil_warning'
# 开关
is_train = True # 是否训练
is_debug = False # 是否调试
is_eta = False # 是否使用eta接口
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
@ -415,4 +407,3 @@ logger.addHandler(file_handler)
logger.addHandler(console_handler)
# logger.info('当前配置：'+settings)

18
config_jingbo_zhoudu.py
View File

@ -89,8 +89,7 @@ data = {
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"
# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
@ -111,7 +110,6 @@ ClassifyId = 1214
# }
# upload_data = {
# "funcModule":'研究报告信息',
# "funcOperation":'上传原油价格预测报告',
@ -119,12 +117,12 @@ ClassifyId = 1214
# "groupNo":'', # 用户组id
# "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
# "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
# "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
# }
# }
@ -151,7 +149,6 @@ ClassifyId = 1214
# }
# push_data_value_list_data = {
# "funcModule": "数据表信息列表",
# "funcOperation": "新增",
@ -189,15 +186,12 @@ ClassifyId = 1214
# # 生产环境数据库
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
# port = 3306
# dbusername ='jingbo'
# dbusername ='jingbo'
# password = 'shihua@123'
# dbname = 'jingbo'
# table_name = 'v_tbl_crude_oil_warning'
# # 变量定义--测试环境
server_host = '192.168.100.53:8080' # 内网
# server_host = '183.242.74.28' # 外网
@ -304,7 +298,7 @@ table_name = 'v_tbl_crude_oil_warning'
# 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_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

29
lib/dataread.py
View File

@ -265,7 +265,7 @@ def upload_report_data(token, upload_data):
config.logger.info(f"token:{token}")
# 打印日志，显示要上传的报告数据
config.logger.info(f"upload_data:{upload_data}")
# config.logger.info(f"upload_data:{upload_data}")
# 发送POST请求，上传报告数据
upload_res = requests.post(
@ -275,7 +275,7 @@ def upload_report_data(token, upload_data):
upload_res = json.loads(upload_res.text)
# 打印日志，显示响应内容
config.logger.info(upload_res)
# config.logger.info(upload_res)
# 如果上传成功，返回响应对象
if upload_res:
@ -790,7 +790,7 @@ def datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time='', y
# 按时间顺序排列
df.sort_values(by='ds', inplace=True)
df['ds'] = pd.to_datetime(df['ds'])
# 获取 start_year 对应年份的第一天日期
start_date = datetime.datetime(config.start_year, 1, 1)
@ -808,7 +808,8 @@ def datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time='', y
# 判断对应的 'ds' 是否大于 start_date
if df.loc[first_valid_index, 'ds'] > start_date:
df.drop(columns=[col], inplace=True)
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"]}')
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"]}')
config.logger.info(f'删除开始时间没有数据的列后数据量：{df.shape}')
@ -1200,6 +1201,7 @@ class Config:
@property
def warning_data(self): return global_config['warning_data']
# 查询接口
@property
def query_data_list_item_nos_url(
self): return global_config['query_data_list_item_nos_url']
@ -1219,8 +1221,8 @@ class Config:
@property
def bdwd_items(self): return global_config['bdwd_items']
# 字段映射
@property
def offsite_col(self): return global_config['offsite_col']
@property
@ -1247,7 +1249,6 @@ class Config:
def is_bdwd(self): return global_config['is_bdwd']
config = Config()
@ -2303,7 +2304,6 @@ def get_baichuan_data(baichuanidnamedict):
return df1
def get_bdwd_predict_data():
# 获取认证头部信息
token = get_head_auth_report()
@ -2315,21 +2315,20 @@ def get_bdwd_predict_data():
config.logger.info("获取八大维度数据...")
# 打印日志，显示上传的URL
config.logger.info(f"query_data_list_item_nos_url:{config.query_data_list_item_nos_url}")
config.logger.info(
f"query_data_list_item_nos_url:{config.query_data_list_item_nos_url}")
# 打印日志，显示认证头部信息
config.logger.info(f"token:{token}")
# 打印日志，显示要查询的数据项
config.logger.info(f"query_data_list_item_nos_data:{query_data_list_item_nos_data}")
config.logger.info(
f"query_data_list_item_nos_data:{query_data_list_item_nos_data}")
# 发送POST请求，上传预警数据
respose = requests.post(
url=config.upload_warning_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 15))
# 如果上传成功，返回响应对象
if respose:
# 处理返回结果为df
@ -2381,11 +2380,13 @@ def get_bdwd_predict_data():
df2['date'] = df2['date'].dt.strftime('%Y-%m-%d')
# df = pd.merge(df, df2, how='left', on='date')
# 更改列名：
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)
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)
# 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)
# 更改显示顺序
# 过滤掉不存在的列
desired_columns = ['date','次日','本周','次周','隔周','次月','次二月','次三月','次四月']
desired_columns = ['date', '次日', '本周',
'次周', '隔周', '次月', '次二月', '次三月', '次四月']
existing_columns = [col for col in desired_columns if col in df2.columns]
# 更改显示顺序

76
main_juxiting.py
View File

@ -45,13 +45,13 @@ global_config.update({
# 时间参数
'start_year': start_year,
'end_time': end_time ,
'end_time': end_time,
'freq': freq, # 保持列表结构
# 接口配置
'login_pushreport_url': login_pushreport_url,
'login_data': login_data,
'upload_url': upload_url,
'upload_url': upload_url,
'upload_warning_url': upload_warning_url,
'warning_data': warning_data,
@ -59,7 +59,7 @@ global_config.update({
'query_data_list_item_nos_url': query_data_list_item_nos_url,
'query_data_list_item_nos_data': query_data_list_item_nos_data,
# 上传数据项
# 上传数据项
'push_data_value_list_url': push_data_value_list_url,
'push_data_value_list_data': push_data_value_list_data,
@ -82,9 +82,6 @@ global_config.update({
})
def push_market_value():
config.logger.info('发送预测结果到市场信息平台')
# 读取预测数据和模型评估数据
@ -121,13 +118,13 @@ def push_market_value():
predictdata = [
{
"dataItemNo": global_config['bdwd_items']['ciri'],
"dataDate": global_config['end_time'].replace('-',''),
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": first_mean
},
{
"dataItemNo": global_config['bdwd_items']['benzhou'],
"dataDate": global_config['end_time'].replace('-',''),
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": last_mean
}
@ -142,8 +139,6 @@ def push_market_value():
config.logger.error(f"推送数据失败: {e}")
def predict_main():
"""
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。
@ -198,7 +193,7 @@ def predict_main():
# 获取数据
if is_eta:
logger.info('从eta获取数据...')
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
data_set=data_set, dataset=dataset) # 原始数据，未处理
@ -385,25 +380,25 @@ def predict_main():
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
ex_Model_Juxiting(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'],
)
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')
@ -421,7 +416,7 @@ def predict_main():
reportname=reportname, sqlitedb=sqlitedb),
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()
@ -450,15 +445,14 @@ def predict_main():
if __name__ == '__main__':
# global end_time
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
for i_time in pd.date_range('2025-4-7', '2025-4-8', freq='B'):
try:
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
# for i_time in pd.date_range('2025-4-14', '2025-4-15', freq='B'):
# try:
# 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()
# push_market_value()

43
main_yuanyou_yuedu.py
View File

@ -3,7 +3,7 @@
from lib.dataread import *
from config_jingbo_yuedu import *
from lib.tools import SendMail, exception_logger
from models.nerulforcastmodels import ex_Model, model_losss,brent_export_pdf
from models.nerulforcastmodels import ex_Model, model_losss, brent_export_pdf
import datetime
import torch
torch.set_float32_matmul_precision("high")
@ -121,25 +121,25 @@ def push_market_value():
predictdata = [
{
"dataItemNo": global_config['bdwd_items']['ciyue'],
"dataDate": global_config['end_time'].replace('-',''),
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": ciyue_mean
},
{
"dataItemNo": global_config['bdwd_items']['cieryue'],
"dataDate": global_config['end_time'].replace('-',''),
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": cieryue_mean
},
{
"dataItemNo": global_config['bdwd_items']['cisanyue'],
"dataDate": global_config['end_time'].replace('-',''),
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": cisanyue_mean
},
{
"dataItemNo": global_config['bdwd_items']['cisiyue'],
"dataDate": global_config['end_time'].replace('-',''),
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": cisieryue_mean
}
@ -208,7 +208,7 @@ def predict_main():
# 获取数据
if is_eta:
logger.info('从eta获取数据...')
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
data_set=data_set, dataset=dataset) # 原始数据，未处理
@ -415,7 +415,7 @@ def predict_main():
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')
model_results3 = model_losss(sqlitedb, end_time=end_time)
@ -423,21 +423,19 @@ def predict_main():
push_market_value()
# 模型报告
logger.info('制作报告ing')
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 = reportname.replace(':', '-') # 替换冒号
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
reportname=reportname,
inputsize = global_config['horizon'],
sqlitedb=sqlitedb
),
# # 模型报告
# logger.info('制作报告ing')
# 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 = reportname.replace(':', '-') # 替换冒号
# brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
# reportname=reportname,
# inputsize = global_config['horizon'],
# 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
# 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__':
# global end_time
# 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:
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
predict_main()
@ -473,4 +471,3 @@ if __name__ == '__main__':
continue
# predict_main()

454
main_yuanyou_zhoudu.py
View File

@ -116,13 +116,13 @@ def push_market_value():
predictdata = [
{
"dataItemNo": global_config['bdwd_items']['cizhou'],
"dataDate": global_config['end_time'].replace('-',''),
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": first_mean
},
{
"dataItemNo": global_config['bdwd_items']['gezhou'],
"dataDate": global_config['end_time'].replace('-',''),
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": last_mean
}
@ -176,234 +176,234 @@ def predict_main():
返回:
None
"""
# end_time = global_config['end_time']
end_time = global_config['end_time']
# signature = BinanceAPI(APPID, SECRET)
# etadata = EtaReader(signature=signature,
# classifylisturl=global_config['classifylisturl'],
# classifyidlisturl=global_config['classifyidlisturl'],
# edbcodedataurl=global_config['edbcodedataurl'],
# edbcodelist=global_config['edbcodelist'],
# edbdatapushurl=global_config['edbdatapushurl'],
# edbdeleteurl=global_config['edbdeleteurl'],
# edbbusinessurl=global_config['edbbusinessurl'],
# classifyId=global_config['ClassifyId'],
# )
# # 获取数据
# if is_eta:
# logger.info('从eta获取数据...')
# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
# data_set=data_set, dataset=dataset) # 原始数据，未处理
signature = BinanceAPI(APPID, SECRET)
etadata = EtaReader(signature=signature,
classifylisturl=global_config['classifylisturl'],
classifyidlisturl=global_config['classifyidlisturl'],
edbcodedataurl=global_config['edbcodedataurl'],
edbcodelist=global_config['edbcodelist'],
edbdatapushurl=global_config['edbdatapushurl'],
edbdeleteurl=global_config['edbdeleteurl'],
edbbusinessurl=global_config['edbbusinessurl'],
classifyId=global_config['ClassifyId'],
)
# 获取数据
if is_eta:
logger.info('从eta获取数据...')
# if is_market:
# logger.info('从市场信息平台获取数据...')
# try:
# # 如果是测试环境，最高价最低价取excel文档
# if server_host == '192.168.100.53':
# logger.info('从excel文档获取最高价最低价')
# df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
# else:
# logger.info('从市场信息平台获取数据')
# df_zhibiaoshuju = get_market_data(
# end_time, df_zhibiaoshuju)
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
data_set=data_set, dataset=dataset) # 原始数据，未处理
# except:
# logger.info('最高最低价拼接失败')
if is_market:
logger.info('从市场信息平台获取数据...')
try:
# 如果是测试环境，最高价最低价取excel文档
if server_host == '192.168.100.53':
logger.info('从excel文档获取最高价最低价')
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
else:
logger.info('从市场信息平台获取数据')
df_zhibiaoshuju = get_market_data(
end_time, df_zhibiaoshuju)
# # 保存到xlsx文件的sheet表
# 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_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
except:
logger.info('最高最低价拼接失败')
# # 数据处理
# df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
# end_time=end_time)
# 保存到xlsx文件的sheet表
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_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
# else:
# # 读取数据
# 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,
# is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
# 数据处理
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
end_time=end_time)
# # 更改预测列名称
# df.rename(columns={y: 'y'}, inplace=True)
else:
# 读取数据
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,
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
# if is_edbnamelist:
# df = df[edbnamelist]
# 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
# # 取第一行数据存储到数据库中
# first_row = df[['ds', 'y']].tail(1)
# # 判断y的类型是否为float
# if not isinstance(first_row['y'].values[0], float):
# logger.info(f'{end_time}预测目标数据为空，跳过')
# return None
# 更改预测列名称
df.rename(columns={y: 'y'}, inplace=True)
# # 将最新真实值保存到数据库
# if not sqlitedb.check_table_exists('trueandpredict'):
# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
# else:
# for row in first_row.itertuples(index=False):
# row_dict = row._asdict()
# 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
# 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 %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())
if is_edbnamelist:
df = df[edbnamelist]
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
# 取第一行数据存储到数据库中
first_row = df[['ds', 'y']].tail(1)
# 判断y的类型是否为float
if not isinstance(first_row['y'].values[0], float):
logger.info(f'{end_time}预测目标数据为空，跳过')
return None
# # 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:
# for row in update_y.itertuples(index=False):
# try:
# row_dict = row._asdict()
# yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
# LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
# HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
# sqlitedb.update_data(
# 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
# except:
# logger.info(f'更新accuracy表的y值失败：{row_dict}')
# # except Exception as e:
# # logger.info(f'更新accuracy表的y值失败：{e}')
# 将最新真实值保存到数据库
if not sqlitedb.check_table_exists('trueandpredict'):
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
else:
for row in first_row.itertuples(index=False):
row_dict = row._asdict()
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
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 %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())
# # 判断当前日期是不是周一
# is_weekday = datetime.datetime.now().weekday() == 0
# if is_weekday:
# logger.info('今天是周一，更新预测模型')
# # 计算最近60天预测残差最低的模型名称
# model_results = sqlitedb.select_data(
# 'trueandpredict', order_by="ds DESC", limit="60")
# # 删除空值率为90%以上的列
# if len(model_results) > 10:
# model_results = model_results.dropna(
# thresh=len(model_results)*0.1, axis=1)
# # 删除空行
# model_results = model_results.dropna()
# modelnames = model_results.columns.to_list()[2:-2]
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
# model_results[col] = model_results[col].astype(np.float32)
# # 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',))
# 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:
for row in update_y.itertuples(index=False):
try:
row_dict = row._asdict()
yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
sqlitedb.update_data(
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
except:
logger.info(f'更新accuracy表的y值失败：{row_dict}')
# except Exception as e:
# logger.info(f'更新accuracy表的y值失败：{e}')
# try:
# if is_weekday:
# # if True:
# logger.info('今天是周一，发送特征预警')
# # 上传预警信息到数据库
# warning_data_df = df_zhibiaoliebiao.copy()
# warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
# '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']]
# # 重命名列名
# warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
# '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'})
# from sqlalchemy import create_engine
# import urllib
# global password
# if '@' in password:
# password = urllib.parse.quote_plus(password)
# 判断当前日期是不是周一
is_weekday = datetime.datetime.now().weekday() == 0
if is_weekday:
logger.info('今天是周一，更新预测模型')
# 计算最近60天预测残差最低的模型名称
model_results = sqlitedb.select_data(
'trueandpredict', order_by="ds DESC", limit="60")
# 删除空值率为90%以上的列
if len(model_results) > 10:
model_results = model_results.dropna(
thresh=len(model_results)*0.1, axis=1)
# 删除空行
model_results = model_results.dropna()
modelnames = model_results.columns.to_list()[2:-2]
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
model_results[col] = model_results[col].astype(np.float32)
# 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',))
# engine = create_engine(
# f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
# 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')
try:
if is_weekday:
# if True:
logger.info('今天是周一，发送特征预警')
# 上传预警信息到数据库
warning_data_df = df_zhibiaoliebiao.copy()
warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
'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']]
# 重命名列名
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
'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'})
from sqlalchemy import create_engine
import urllib
global password
if '@' in password:
password = urllib.parse.quote_plus(password)
# if is_corr:
# df = corr_feature(df=df)
engine = create_engine(
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
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')
# df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
# logger.info(f"开始训练模型...")
# row, col = df.shape
if is_corr:
df = corr_feature(df=df)
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
# 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'],
# )
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
logger.info(f"开始训练模型...")
row, col = df.shape
# logger.info('模型训练完成')
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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('训练数据绘图ing')
# model_results3 = model_losss(sqlitedb, end_time=end_time)
# logger.info('训练数据绘图end')
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end')
# # 模型报告
logger.info('制作报告ing')
@ -411,17 +411,17 @@ def predict_main():
reportname = f'Brent原油大模型周度预测--{end_time}.pdf' # 报告文件名
reportname = reportname.replace(':', '-') # 替换冒号
brent_export_pdf(dataset=dataset,
num_models=5 if is_fivemodels else 22,
time=end_time,
reportname=reportname,
inputsize = global_config['horizon'],
sqlitedb=sqlitedb
),
num_models=5 if is_fivemodels else 22,
time=end_time,
reportname=reportname,
inputsize=global_config['horizon'],
sqlitedb=sqlitedb
),
logger.info('制作报告end')
logger.info('模型训练完成')
# push_market_value()
push_market_value()
# 发送邮件
# m = SendMail(
@ -439,12 +439,12 @@ def predict_main():
if __name__ == '__main__':
# global end_time
# 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'):
# try:
# 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
for i_time in pd.date_range('2025-2-1', '2025-3-31', freq='B'):
try:
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()

10
models/implementARIMA.py
View File

@ -173,10 +173,14 @@ if __name__ == '__main__':
# cal_time_series(df, 7) # 模型调用
# 数据测试2（从excel中读取）:
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.rename(columns={f'{y}': 'deal_data'}, inplace=True)
df = df[['ds', f'{y}']]
print(df.columns)
# 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())
df.set_index(['ds'], inplace=True) # 设置索引
cal_time_series(df, 7) # 模型调用

26
models/nerulforcastmodels.py
View File

@ -1243,7 +1243,11 @@ def model_losss(sqlitedb, end_time):
df4.to_sql("accuracy_rote", con=sqlitedb.connection,
if_exists='append', index=False)
create_dates, ds_dates = get_week_date(end_time)
_get_accuracy_rate(df, create_dates, ds_dates)
try:
_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():
# 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)
doc.build(content)
# pdf 上传到数字化信息平台
# try:
# if config.is_update_report:
# with open(os.path.join(config.dataset, reportname), 'rb') as f:
# base64_data = base64.b64encode(f.read()).decode('utf-8')
# config.upload_data["data"]["fileBase64"] = base64_data
# config.upload_data["data"]["fileName"] = reportname
# token = get_head_auth_report()
# upload_report_data(token, config.upload_data)
# except TimeoutError as e:
# print(f"请求超时: {e}")
try:
if config.is_update_report:
with open(os.path.join(config.dataset, reportname), 'rb') as f:
base64_data = base64.b64encode(f.read()).decode('utf-8')
config.upload_data["data"]["fileBase64"] = base64_data
config.upload_data["data"]["fileName"] = reportname
token = get_head_auth_report()
upload_report_data(token, config.upload_data)
except TimeoutError as e:
print(f"请求超时: {e}")
@exception_logger

245
test/pushdata.py
View File

@ -3,26 +3,164 @@ from config_jingbo import *
# from config_tansuanli import *
from lib.tools import *
from lib.dataread import *
from models.nerulforcastmodels import ex_Model,model_losss,brent_export_pdf,tansuanli_export_pdf
from models.lstmmodels import ex_Lstm_M,ex_Lstm
from models.nerulforcastmodels import ex_Model, model_losss, brent_export_pdf, tansuanli_export_pdf
from models.lstmmodels import ex_Lstm_M, ex_Lstm
from models.grumodels import ex_GRU
import glob
import torch
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__':
signature = BinanceAPI(APPID, SECRET)
etadata = EtaReader(signature=signature,
classifylisturl = classifylisturl,
classifyidlisturl=classifyidlisturl,
edbcodedataurl=edbcodedataurl,
edbcodelist=edbcodelist,
edbdatapushurl = edbdatapushurl,
edbdeleteurl = edbdeleteurl,
edbbusinessurl = edbbusinessurl
)
classifylisturl=classifylisturl,
classifyidlisturl=classifyidlisturl,
edbcodedataurl=edbcodedataurl,
edbcodelist=edbcodelist,
edbdatapushurl=edbdatapushurl,
edbdeleteurl=edbdeleteurl,
edbbusinessurl=edbbusinessurl,
classifyId=ClassifyId,
)
models = [
'NHITS',
'Informer',
@ -45,31 +183,31 @@ if __name__ == '__main__':
'MLPMultivariate',
'TiDE',
'DeepNPT']
# eta自由数据指标编码
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',
'DeepNPT':'SELF0000076'
}
'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',
'DeepNPT': 'SELF0000076'
}
# df_predict = pd.read_csv('dataset/predict.csv',encoding='gbk')
# # df_predict.rename(columns={'ds':'Date'},inplace=True)
@ -82,23 +220,34 @@ if __name__ == '__main__':
# data['IndexName'] = f'价格预测{m}模型'
# data['Remark'] = m
# # 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
# IndexCodeList = ['SELF0000055']
# for i in range(1,57):
# if i < 10 : i = f'0{i}'
# IndexCodeList.append(f'SELF00000{i}')
# SELF0000098
# IndexCodeList = ['SELF0000098']
# # for i in range(1,57):
# # if i < 10 : i = f'0{i}'
# # IndexCodeList.append(f'SELF00000{i}')
# print(IndexCodeList)
# etadata.del_zhibiao(IndexCodeList)
# 删除特定日期的值
indexcodelist = modelsindex.values()
for indexcode in indexcodelist:
data = {
"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
"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
}
# indexcodelist = modelsindex.values()
# for indexcode in indexcodelist:
# data = {
# "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
# "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)

17
原油八大维度预测任务.py
View File

@ -9,14 +9,15 @@ import time
def run_predictions(target_date):
"""执行三个预测脚本"""
scripts = [
"main_yuanyou.py",
# "main_yuanyou.py",
"main_yuanyou_zhoudu.py",
"main_yuanyou_yuedu.py"
]
# 依次执行每个脚本
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)
@ -26,10 +27,10 @@ def is_weekday(date):
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
start_date = datetime.date.today()
# end_date = datetime.date(2100, 12, 31)
# start_date = datetime.date.today()
end_date = datetime.date(2025, 3, 31)
current_date = start_date
# while current_date <= end_date:
@ -46,5 +47,7 @@ if __name__ == "__main__":
# current_date += datetime.timedelta(days=1)
print(f"开始执行 {current_date} 的预测任务")
run_predictions(current_date)
while current_date <= end_date:
print(f"开始执行 {current_date} 的预测任务")
run_predictions(current_date)
current_date += datetime.timedelta(days=1)