PriceForecast/lib/pydantic_models.py

43 lines
1.5 KiB
Python

from datetime import datetime
from decimal import Decimal
from pydantic import BaseModel
from typing import Optional
from datetime import datetime
from decimal import Decimal
class PredictionResult(BaseModel):
feature_factor_frequency: str
strategy_id: int
oil_code: Optional[str] = None
oil_name: Optional[str] = None
data_date: Optional[datetime] = None
market_price: Optional[Decimal] = None
day_price: Optional[Decimal] = None
week_price: Optional[Decimal] = None
second_week_price: Optional[Decimal] = None
next_week_price: Optional[Decimal] = None
next_month_price: Optional[Decimal] = None
next_february_price: Optional[Decimal] = None
next_march_price: Optional[Decimal] = None
next_april_price: Optional[Decimal] = None
model_evaluation_id: int
model_id: int
tenant_code: Optional[str] = None
reserved_str1: Optional[str] = None
reserved_str2: Optional[str] = None
reserved_str3: Optional[str] = None
reserved_str4: Optional[str] = None
reserved_str5: Optional[str] = None
reserved_num1: Optional[Decimal] = None
reserved_num2: Optional[Decimal] = None
reserved_num3: Optional[Decimal] = None
reserved_num4: Optional[Decimal] = None
reserved_num5: Optional[Decimal] = None
version_num: Decimal = Decimal(1)
delete_flag: str = '0'
create_user: Optional[str] = None
create_date: Optional[datetime] = None
update_user: Optional[str] = None
update_date: Optional[datetime] = None