104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
# 读取配置
|
|
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.grumodels import ex_GRU
|
|
import glob
|
|
import torch
|
|
torch.set_float32_matmul_precision("high")
|
|
|
|
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
|
|
)
|
|
|
|
models = [
|
|
'NHITS',
|
|
'Informer',
|
|
'LSTM',
|
|
'iTransformer',
|
|
'TSMixer',
|
|
'TSMixerx',
|
|
'PatchTST',
|
|
'RNN',
|
|
'GRU',
|
|
'TCN',
|
|
'BiTCN',
|
|
'DilatedRNN',
|
|
'MLP',
|
|
'DLinear',
|
|
'NLinear',
|
|
'TFT',
|
|
'FEDformer',
|
|
'StemGNN',
|
|
'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'
|
|
}
|
|
|
|
# df_predict = pd.read_csv('dataset/predict.csv',encoding='gbk')
|
|
# # df_predict.rename(columns={'ds':'Date'},inplace=True)
|
|
# for m in modelsindex.keys():
|
|
# list = []
|
|
# for date,value in zip(df_predict['ds'],df_predict[m]):
|
|
# list.append({'Date':date,'Value':value})
|
|
# data['DataList'] = list
|
|
# data['IndexCode'] = modelsindex[m]
|
|
# data['IndexName'] = f'价格预测{m}模型'
|
|
# data['Remark'] = m
|
|
# # print(data['DataList'])
|
|
# etadata.push_data(data)
|
|
|
|
# 删除指标
|
|
# IndexCodeList = ['SELF0000055']
|
|
# 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
|
|
}
|
|
|
|
# etadata.del_business(data) |