134 lines
6.3 KiB
Python
134 lines
6.3 KiB
Python
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import json
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import adata
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from math import ceil
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from typing import Any, Dict
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from fastapi import Request, HTTPException, BackgroundTasks
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from tortoise.expressions import Subquery,Q
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from tortoise.contrib.pydantic import pydantic_model_creator
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from src.models.financial_reports import *
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from src.models.stock_hu_shen300 import *
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from src.models.stock_zhong_zheng_500 import *
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from src.models.stock_guo_zheng_2000 import *
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from src.models.stock_hu_shen_jing_a import *
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from src.financial_reports.schemas import *
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from src.utils.paginations import *
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from src.financial_reports.mappings import *
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# 创建一个不包含 "created_at" 字段的 Pydantic 模型用于响应
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FinancialReport_Pydantic = pydantic_model_creator(FinancialReport, exclude=("created_at",))
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async def financial_reports_query_service(
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request: FinancialReportQuery
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) -> PaginationPydantic:
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"""
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根据选择器和关键词查询财报，并进行分页
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"""
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# 构建查询集，这里假设财报数据模型为 FinancialReport
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query_set: QuerySet = FinancialReport.all()
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try:
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# 年度映射
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if request.year:
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mapped_year = year_mapping.get(request.year)
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if mapped_year is not None:
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query_set = query_set.filter(year=mapped_year)
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else:
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raise ValueError("无效的年份参数")
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# 股票池映射
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if request.stockPoolCode is None:
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# 未提供股票池代码，返回所有记录
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pass
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else:
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# 获取映射值，如果不存在则返回 None
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mapped_stock_pool = stock_pool_mapping.get(request.stockPoolCode)
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# 检查 mapped_stock_pool 是否为 None（即不在映射表中）
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if mapped_stock_pool is None:
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# 如果 stockPoolCode 不在映射表中，抛出无效股票池参数错误
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raise ValueError("无效的股票池参数")
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# 如果存在有效的映射值，执行相应的过滤
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elif mapped_stock_pool == "000300.SH":
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subquery = await StockHuShen300.filter().values_list('code', flat=True)
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query_set = query_set.filter(stock_code__in=subquery)
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elif mapped_stock_pool == "000905.SH":
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subquery = await StockZhongZheng500.filter().values_list('code', flat=True)
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query_set = query_set.filter(stock_code__in=subquery)
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elif mapped_stock_pool == "399303.SZ":
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subquery = await StockGuoZheng2000.filter().values_list('code', flat=True)
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query_set = query_set.filter(stock_code__in=subquery)
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elif mapped_stock_pool in ["large_mv", "medium_mv", "small_mv"]:
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# 先获取所有有总市值的关联的股票代码
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subquery = await StockHuShenJingA.filter(total_market_value__isnull=False).values_list('code', 'total_market_value')
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# 转换为 DataFrame 或者直接进行排序
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stock_list = sorted(subquery, key=lambda x: x[1], reverse=True) # 按 total_market_value 降序排序
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total_count = len(stock_list)
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if mapped_stock_pool == "large_mv":
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# 获取前 30% 的数据
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limit_count = ceil(total_count * 0.3)
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selected_stocks = [stock[0] for stock in stock_list[:limit_count]]
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elif mapped_stock_pool == "medium_mv":
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# 获取中间 40% 的数据
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start_offset = ceil(total_count * 0.3)
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limit_count = ceil(total_count * 0.4)
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selected_stocks = [stock[0] for stock in stock_list[start_offset:start_offset + limit_count]]
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elif mapped_stock_pool == "small_mv":
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# 获取后 30% 的数据
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start_offset = ceil(total_count * 0.7)
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selected_stocks = [stock[0] for stock in stock_list[start_offset:]]
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# 对 FinancialReport 表进行筛选
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query_set = query_set.filter(stock_code__in=selected_stocks)
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# 财报周期映射
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if request.period is not None:
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mapped_period = period_mapping.get(request.period)
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if mapped_period is not None:
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# 如果映射到有效的周期，则进行过滤
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query_set = query_set.filter(period=mapped_period)
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else:
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# 如果找不到有效的映射，抛出错误
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raise ValueError("无效的财报周期参数")
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# 检查是否所有筛选条件都为空
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if not request.revenueYoyType and not request.nIncomeYoyType and not request.revenueYoyTop10:
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# 如果全部为空，则按 date 和 created_at 降序排序
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query_set = query_set.order_by("-date", "-created_at")
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# 筛选 revenueYoyType，如果是 1 则筛选 revenue_yoy 大于 10.0 的记录
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if request.revenueYoyType == 1:
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query_set = query_set.filter(revenue_yoy__gt=10.0).order_by("-revenue_yoy")
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# 筛选 nIncomeYoyType，如果是 1 则筛选 nincome_yoy 大于 10.0 的记录
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elif request.nIncomeYoyType == 1:
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query_set = query_set.filter(nincome_yoy__gt=10.0).order_by("-nincome_yoy")
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# 如果 revenueYoyTop10 为 1，则筛选 revenue_yoy 前 10% 的记录
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elif request.revenueYoyTop10 == 1:
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# 计算前 10% 的数量
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total_count = await FinancialReport.all().count() # 获取总记录数
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limit_count = ceil(total_count * 0.1)
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# 按 revenue_yoy 降序排列，获取前 10% 记录
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query_set = query_set.order_by("-revenue_yoy").limit(limit_count).order_by("-revenue_yoy")
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# 调用分页函数进行分页处理
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params = Params(page=request.pageNo, size=request.pageSize) #parms作用是传页码和页面大小
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result = await pagination(pydantic_model=FinancialReport_Pydantic,
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query_set=query_set,
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params=params)
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return result
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except Exception as e:
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raise HTTPException(status_code=500, detail="内部服务器错误")
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