592 lines
20 KiB
Python
592 lines
20 KiB
Python
"""
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数据获取服务层
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负责从不同数据源获取财务数据并存储到数据库
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"""
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import os
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from datetime import datetime
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from typing import Dict, List, Optional
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import pandas as pd
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, and_, text
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from sqlalchemy.orm import selectinload
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from app.models import Company, DataUpdate, DataSourceAvailability
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from app.schemas import DataCheckResponse, FetchDataRequest
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from app.fetchers.factory import FetcherFactory
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from app.services.bloomberg_service import get_bloomberg_data
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import logging
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logger = logging.getLogger(__name__)
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def update_progress_sync(update_id: int, message: str, percentage: int):
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"""同步更新数据获取进度 - 使用psycopg2直连避免event loop冲突"""
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import psycopg2
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import os
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logger.info(f"🔄 [进度更新] ID={update_id}, Msg={message}, Progress={percentage}%")
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try:
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conn = psycopg2.connect(
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host=os.getenv('DB_HOST', '192.168.3.195'),
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user=os.getenv('DB_USER', 'value'),
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password=os.getenv('DB_PASSWORD', 'Value609!'),
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dbname=os.getenv('DB_NAME', 'fa3'),
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port=os.getenv('DB_PORT', '5432')
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)
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cur = conn.cursor()
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cur.execute(
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"""
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UPDATE data_updates
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SET progress_message = %s, progress_percentage = %s
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WHERE id = %s
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""",
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(message, percentage, update_id)
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)
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if cur.rowcount == 0:
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logger.warning(f"⚠️ [进度更新] ID={update_id} 未找到记录!")
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conn.commit()
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cur.close()
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conn.close()
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except Exception as e:
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logger.error(f"❌ [进度更新失败] ID={update_id}: {e}", exc_info=True)
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async def check_data_status(
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market: str,
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symbol: str,
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data_source: str,
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db: AsyncSession
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) -> DataCheckResponse:
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"""
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检查指定公司和数据源的数据状态
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Returns:
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DataCheckResponse: 包含数据状态信息
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"""
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# 1. 查询或创建公司记录
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result = await db.execute(
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select(Company).where(
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and_(Company.market == market, Company.symbol == symbol)
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)
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)
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company = result.scalar_one_or_none()
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if not company:
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# 没有找到公司记录
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return DataCheckResponse(
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has_data=False,
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company_id=None,
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data_source=data_source,
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message=f"该数据源暂无该公司数据"
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)
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# 2. 查询最近一次成功的数据更新
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result = await db.execute(
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select(DataUpdate)
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.where(
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and_(
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DataUpdate.company_id == company.id,
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DataUpdate.data_source == data_source,
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DataUpdate.status == 'completed'
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)
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)
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.order_by(DataUpdate.completed_at.desc())
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.limit(1)
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)
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latest_update = result.scalar_one_or_none()
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if not latest_update:
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return DataCheckResponse(
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has_data=False,
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company_id=company.id,
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data_source=data_source,
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message="该数据源暂无该公司数据"
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)
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# 3. 构建响应
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return DataCheckResponse(
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has_data=True,
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company_id=company.id,
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data_source=data_source,
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last_update={
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"date": latest_update.completed_at.isoformat(),
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"data_start_date": latest_update.data_start_date,
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"data_end_date": latest_update.data_end_date,
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"table_counts": latest_update.row_counts or {}
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}
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)
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async def create_or_get_company(
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market: str,
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symbol: str,
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company_name: str,
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db: AsyncSession
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) -> Company:
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"""创建或获取公司记录"""
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result = await db.execute(
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select(Company).where(
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and_(Company.market == market, Company.symbol == symbol)
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)
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)
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company = result.scalar_one_or_none()
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if not company:
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company = Company(
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market=market,
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symbol=symbol,
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company_name=company_name
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)
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db.add(company)
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await db.commit()
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await db.refresh(company)
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return company
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async def create_data_update_record(
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company_id: int,
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data_source: str,
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update_type: str,
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db: AsyncSession
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) -> DataUpdate:
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"""创建数据更新记录"""
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data_update = DataUpdate(
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company_id=company_id,
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data_source=data_source,
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update_type=update_type,
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status='in_progress'
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)
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db.add(data_update)
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await db.commit()
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await db.refresh(data_update)
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return data_update
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async def update_data_update_record(
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update_id: int,
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status: str,
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db: AsyncSession,
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completed_at: Optional[datetime] = None,
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error_message: Optional[str] = None,
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data_start_date: Optional[str] = None,
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data_end_date: Optional[str] = None,
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fetched_tables: Optional[List[str]] = None,
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row_counts: Optional[Dict[str, int]] = None
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):
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"""更新数据更新记录"""
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result = await db.execute(
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select(DataUpdate).where(DataUpdate.id == update_id)
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)
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data_update = result.scalar_one()
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data_update.status = status
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if completed_at:
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data_update.completed_at = completed_at
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if error_message:
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data_update.error_message = error_message
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if data_start_date:
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data_update.data_start_date = data_start_date
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if data_end_date:
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data_update.data_end_date = data_end_date
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if fetched_tables:
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data_update.fetched_tables = fetched_tables
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if row_counts:
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data_update.row_counts = row_counts
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await db.commit()
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await db.refresh(data_update)
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return data_update
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def fetch_financial_data_sync(
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company_id: int,
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market: str,
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symbol: str,
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data_source: str,
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update_id: int,
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currency: Optional[str] = None,
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frequency: Optional[str] = "Annual"
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):
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"""
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同步方式获取财务数据（在后台任务中调用）
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此函数实际执行数据获取，使用同步的 Fetcher
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"""
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try:
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# 0. 初始化
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display_freq = "季度" if frequency == "Quarterly" or frequency == "Quarter" else "年度"
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update_progress_sync(update_id, f"正在初始化数据获取 ({display_freq})...", 0)
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# 格式化股票代码 - CN市场需要添加.SH或.SZ后缀
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formatted_symbol = symbol
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if market.upper() == 'CN' and data_source == 'Tushare':
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if '.' not in symbol:
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# 判断是上海还是深圳
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if symbol.startswith('6'):
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formatted_symbol = f"{symbol}.SH"
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elif symbol.startswith(('0', '3', '2')):
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formatted_symbol = f"{symbol}.SZ"
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else:
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formatted_symbol = f"{symbol}.SH" # 默认上海
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import logging
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logger = logging.getLogger(__name__)
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logger.info(f"📝 [数据获取] 股票代码格式化: {symbol} -> {formatted_symbol}")
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# 1. 获取对应的 Fetcher
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# 1. 获取对应的 Fetcher
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fetcher = FetcherFactory.get_fetcher(market, data_source)
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update_progress_sync(update_id, "数据源连接成功", 10)
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# 2. 触发数据同步
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# 对于 Bloomberg，我们只需要触发一次全量同步
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if data_source == 'Bloomberg':
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update_progress_sync(update_id, "正在连接 Bloomberg 终端...", 20)
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# 定义进度回调
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def progress_callback(msg, pct):
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# 映射内部进度 (0-100) 到总体进度 (20-90)
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mapped_pct = 20 + int(pct * 0.7)
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update_progress_sync(update_id, msg, mapped_pct)
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# 使用新的 sync_all_data 方法（将在 fetcher 中实现）
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if hasattr(fetcher, 'sync_all_data'):
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# 检查 sync_all_data 是否接受 progress_callback 参数
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import inspect
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sig = inspect.signature(fetcher.sync_all_data)
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kwargs = {}
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if 'progress_callback' in sig.parameters:
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kwargs['progress_callback'] = progress_callback
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if 'force_currency' in sig.parameters:
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kwargs['force_currency'] = currency
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if 'frequency' in sig.parameters:
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kwargs['frequency'] = frequency
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fetcher.sync_all_data(formatted_symbol, **kwargs)
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else:
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# 兼容旧代码，虽然有了 sync_all_data 后这部分应该不需要了
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fetcher.get_income_statement(formatted_symbol)
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update_progress_sync(update_id, "Bloomberg 数据同步完成", 100)
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else:
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# 对于其他数据源，保持原有逻辑但简化日志
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update_progress_sync(update_id, "正在获取财务报表数据...", 30)
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fetcher.get_income_statement(formatted_symbol)
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fetcher.get_balance_sheet(formatted_symbol)
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fetcher.get_cash_flow(formatted_symbol)
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fetcher.get_market_metrics(formatted_symbol)
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# 尝试获取辅助数据
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try:
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fetcher.get_dividends(symbol)
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fetcher.get_repurchases(symbol)
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fetcher.get_employee_count(symbol)
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except Exception:
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pass # 忽略辅助数据获取错误
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update_progress_sync(update_id, "数据同步完成", 100)
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result_data = {
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'status': 'completed',
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'completed_at': datetime.now(),
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'fetched_tables': ['unified_data'] if data_source == 'Bloomberg' else ['income_statement', 'balance_sheet', 'cash_flow', 'daily_basic'],
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# 不再统计行数
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'row_counts': {}
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}
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return result_data
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except Exception as e:
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update_progress_sync(update_id, f"数据获取失败: {str(e)}", 0)
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return {
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'status': 'failed',
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'error_message': str(e)
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}
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async def get_financial_data_from_db(
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company_id: int,
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data_source: str,
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db: AsyncSession,
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frequency: str = "Annual"
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) -> Dict:
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"""
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从数据库读取财务数据
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Args:
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company_id: 公司ID
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data_source: 数据源 (iFinD, Bloomberg, Tushare)
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db: 数据库会话
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frequency: 'Annual' or 'Quarterly'
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Returns:
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包含所有财务数据的字典
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"""
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# 获取公司信息
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result = await db.execute(
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select(Company).where(Company.id == company_id)
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)
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company = result.scalar_one()
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# 准备返回数据结构
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response_data = {
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"company": {
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"id": company.id,
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"market": company.market,
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"symbol": company.symbol,
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"company_name": company.company_name,
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"created_at": company.created_at,
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"updated_at": company.updated_at
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},
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"data_source": data_source,
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"income_statement": [],
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"balance_sheet": [],
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"cash_flow": [],
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"daily_basic": [],
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"dividend": [],
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"repurchase": [],
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"employee": [],
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"unified_data": []
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}
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# 根据数据源读取数据
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if data_source == 'Tushare' and company.market == 'CN':
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# 定义表映射
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tables = {
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"income_statement": "tushare_income_statement",
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"balance_sheet": "tushare_balance_sheet",
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"cash_flow": "tushare_cash_flow",
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"daily_basic": "tushare_daily_basic",
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"dividend": "tushare_dividend",
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"repurchase": "tushare_repurchase",
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"employee": "tushare_stock_company" # Tushare 员工信息通常在公司信息表中
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}
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# 辅助函数：读取表数据
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# from sqlalchemy import text (moved to top)
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async def fetch_table_data(table_name, symbol_code, order_by=None):
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try:
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# 检查表是否存在
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check_sql = text("SELECT to_regclass(:table_name)")
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exists = await db.execute(check_sql, {"table_name": f"public.{table_name}"})
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if not exists.scalar():
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return []
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# 查询数据
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query = f"SELECT * FROM {table_name} WHERE ts_code = :ts_code"
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if order_by:
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query += f" ORDER BY {order_by} DESC"
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query += " LIMIT 100" # 限制返回数量
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result = await db.execute(text(query), {"ts_code": symbol_code})
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# 转换为字典列表，处理日期格式
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data = []
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for row in result.mappings():
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item = dict(row)
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# 处理日期对象为字符串
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for k, v in item.items():
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if isinstance(v, datetime):
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item[k] = v.isoformat()
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data.append(item)
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return data
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except Exception as e:
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print(f"Error fetching from {table_name}: {e}")
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return []
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# Tushare 使用 ts_code (symbol)
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ts_code = company.symbol # 假设 symbol 已经是 ts_code 格式 (e.g. 300750.SZ)
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# 如果 symbol 只有数字，可能需要补充后缀，但这里先假设存储时已处理
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# 实际上 TushareClient._get_ts_code 会处理，这里我们最好模糊匹配或确保一致性
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# 在前端传递 symbol 时通常是 6 位数字，后端 TushareClient 用它去查询
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# 这里尝试直接用 symbol 查询，如果为空，尝试添加后缀匹配（但这比较复杂）
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# 简单起见，我们先用 symbol (假设数据库中存储的 ts_code 包含后缀，我们需要模糊查询或者假设 symbol 是一致的)
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# 修正：Tushare 存储时用的是 ts_code (如 000001.SZ)。
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# 但我们这里的 company.symbol 可能是没有后缀的 (000001)。
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# 我们应该用 LIKE 查询或者获取存储时的 ts_code。
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# 为简单起见，先尝试模糊匹配
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async def fetch_table_data_fuzzy(table_name, symbol_code, order_by=None):
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try:
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check_sql = text("SELECT to_regclass(:table_name)")
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exists = await db.execute(check_sql, {"table_name": f"public.{table_name}"})
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if not exists.scalar(): return []
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query = f"SELECT * FROM {table_name} WHERE ts_code LIKE :ts_code"
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if order_by: query += f" ORDER BY {order_by} DESC"
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query += " LIMIT 50"
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# 尝试匹配 symbol% (如 300750%)
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result = await db.execute(text(query), {"ts_code": f"{symbol_code}%"})
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data = []
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for row in result.mappings():
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item = dict(row)
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for k, v in item.items():
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if hasattr(v, 'isoformat'): item[k] = v.isoformat()
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data.append(item)
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return data
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except Exception as e:
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print(f"Error fetching from {table_name}: {e}")
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return []
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# 并行或串行获取数据
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response_data["income_statement"] = await fetch_table_data_fuzzy(tables["income_statement"], company.symbol, "end_date")
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response_data["balance_sheet"] = await fetch_table_data_fuzzy(tables["balance_sheet"], company.symbol, "end_date")
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response_data["cash_flow"] = await fetch_table_data_fuzzy(tables["cash_flow"], company.symbol, "end_date")
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# 日频数据
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response_data["daily_basic"] = await fetch_table_data_fuzzy(tables["daily_basic"], company.symbol, "trade_date")
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# 其他数据
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response_data["dividend"] = await fetch_table_data_fuzzy(tables["dividend"], company.symbol, "end_date")
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response_data["repurchase"] = await fetch_table_data_fuzzy(tables["repurchase"], company.symbol, "ann_date")
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elif data_source == 'Bloomberg':
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try:
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# 使用独立的 Bloomberg 服务读取数据
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unified_data = await get_bloomberg_data(company, db, frequency=frequency)
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response_data["unified_data"] = unified_data
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response_data["income_statement"] = []
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except Exception as e:
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logger.error(f"Error fetching Bloomberg data from stockcard: {e}", exc_info=True)
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return response_data
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def get_available_data_sources(market: str) -> List[Dict]:
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"""
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获取指定市场的可用数据源
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Returns:
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数据源列表，每个包含 source, available, description, supported_markets
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"""
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all_sources = {
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'iFinD': {
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'description': 'iFinD (同花顺) 数据源',
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'supported_markets': ['HK', 'JP', 'US', 'VN']
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},
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'Tushare': {
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'description': 'Tushare 数据源',
|
||
'supported_markets': ['CN']
|
||
},
|
||
'Bloomberg': {
|
||
'description': 'Bloomberg 数据源',
|
||
'supported_markets': ['CN', 'HK', 'JP', 'US', 'VN']
|
||
}
|
||
}
|
||
|
||
sources = []
|
||
for source_name, info in all_sources.items():
|
||
sources.append({
|
||
'source': source_name,
|
||
'available': market in info['supported_markets'],
|
||
'description': info['description'],
|
||
'supported_markets': info['supported_markets']
|
||
})
|
||
|
||
return sources
|
||
|
||
async def get_recent_companies(
|
||
data_source: Optional[str],
|
||
db: AsyncSession,
|
||
limit: int = 20
|
||
) -> List[Dict]:
|
||
"""获取最近更新的公司列表
|
||
|
||
Args:
|
||
data_source: 可选的数据源过滤。如果为None，则返回所有数据源的最近更新。
|
||
db: 数据库会话
|
||
limit: 返回数量限制
|
||
"""
|
||
query = (
|
||
select(Company, DataUpdate.completed_at, DataUpdate.data_source)
|
||
.join(DataUpdate, Company.id == DataUpdate.company_id)
|
||
.where(DataUpdate.status == 'completed')
|
||
.order_by(DataUpdate.completed_at.desc())
|
||
.limit(limit * 5) # Fetch more to handle duplicates
|
||
)
|
||
|
||
if data_source:
|
||
query = query.where(DataUpdate.data_source == data_source)
|
||
|
||
result = await db.execute(query)
|
||
rows = result.all()
|
||
|
||
seen_ids = set()
|
||
companies = []
|
||
|
||
for row in rows:
|
||
company, completed_at, source = row
|
||
if company.id not in seen_ids:
|
||
seen_ids.add(company.id)
|
||
companies.append({
|
||
"market": company.market,
|
||
"symbol": company.symbol,
|
||
"company_name": company.company_name,
|
||
"data_source": source,
|
||
"last_update": completed_at.strftime('%Y-%m-%d %H:%M') if completed_at else ""
|
||
})
|
||
if len(companies) >= limit:
|
||
break
|
||
|
||
|
||
return companies
|
||
|
||
async def search_companies(
|
||
query: str,
|
||
db: AsyncSession,
|
||
limit: int = 10
|
||
) -> List[Dict]:
|
||
"""
|
||
搜索本地公司库
|
||
|
||
Args:
|
||
query: 搜索关键词 (代码或名称)
|
||
db: 数据库会话
|
||
limit: 返回数量
|
||
|
||
Returns:
|
||
公司列表
|
||
"""
|
||
if not query:
|
||
return []
|
||
|
||
# 构建模糊查询
|
||
search_term = f"%{query}%"
|
||
|
||
# 查找 symbol 或 company_name 匹配的公司
|
||
stmt = (
|
||
select(Company)
|
||
.where(
|
||
(Company.symbol.ilike(search_term)) |
|
||
(Company.company_name.ilike(search_term))
|
||
)
|
||
.limit(limit)
|
||
)
|
||
|
||
result = await db.execute(stmt)
|
||
companies = result.scalars().all()
|
||
|
||
return [
|
||
{
|
||
"market": c.market,
|
||
"symbol": c.symbol,
|
||
"company_name": c.company_name,
|
||
"id": c.id
|
||
}
|
||
for c in companies
|
||
]
|