136 lines
4.9 KiB
Python
136 lines
4.9 KiB
Python
"""
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Bloomberg 数据服务
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负责处理 Bloomberg 特有的数据读取逻辑
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从 stockcard 表中提取数据并转换为统一格式
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"""
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from typing import List, Dict, Optional
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import text
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import logging
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from app.models import Company
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logger = logging.getLogger(__name__)
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async def get_bloomberg_data(
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company: Company,
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db: AsyncSession,
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frequency: str = "Annual"
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) -> List[Dict]:
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"""
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获取指定公司的 Bloomberg 财务数据
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Args:
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company: 公司对象
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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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List[Dict]: 统一格式的财务数据列表
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"""
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try:
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# Determine table name
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table_name = "stockcard"
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if frequency == "Quarterly" or frequency == "Quarter":
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table_name = "stockcard_quarter"
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elif frequency == "Semiannual" or frequency == "Semiannually":
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table_name = "stockcard_semiannual"
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# Check if table exists (to avoid UndefinedTableError on first run)
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check_table_sql = text("SELECT to_regclass(:table_name)")
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table_exists = await db.execute(check_table_sql, {"table_name": f"public.{table_name}"})
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if not table_exists.scalar():
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return []
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# 1. 查找对应的 Company_code
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# stockcard 中存储的是 "AAPL US Equity" 而 symbol 是 "AAPL"
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target_code = None
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# 优先尝试最可能的后缀
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suffixes = [" US Equity", " HK Equity", " JP Equity", " CH Equity", " VN Equity"]
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possible_codes = [f"{company.symbol}{s}" for s in suffixes]
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# 检查哪个存在
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# Use dynamic table name in SQL (safe since we control table_name variable)
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for code in possible_codes:
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check_sql = text(f"SELECT 1 FROM {table_name} WHERE Company_code = :code LIMIT 1")
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exists = await db.execute(check_sql, {"code": code})
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if exists.scalar():
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target_code = code
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break
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# 如果没找到，尝试模糊匹配 (作为兜底)
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if not target_code:
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fuzzy_sql = text(f"SELECT Company_code FROM {table_name} WHERE Company_code LIKE :symbol LIMIT 1")
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fuzzy_res = await db.execute(fuzzy_sql, {"symbol": f"%{company.symbol}%"})
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row = fuzzy_res.fetchone()
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if row:
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target_code = row[0]
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if not target_code:
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logger.warning(f"No Bloomberg data found for symbol: {company.symbol} in {table_name}")
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return []
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# 2. 获取该公司的所有数据
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# schema: indicator, value, value_date (作为报告期)
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# Added update_date
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query = text(f"""
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SELECT indicator, value, value_date, currency, update_date
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FROM {table_name}
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WHERE Company_code = :code
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""")
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result = await db.execute(query, {"code": target_code})
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# 3. 数据透视 (Pivot)
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data_by_date = {}
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for row in result:
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indicator = row.indicator
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val = row.value
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v_date = row.value_date
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curr = row.currency
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u_date = row.update_date
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if not v_date:
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continue
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date_str = v_date.isoformat() if hasattr(v_date, 'isoformat') else str(v_date)
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# Handle update_date formatting
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update_date_str = ""
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if u_date:
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update_date_str = u_date.isoformat() if hasattr(u_date, 'isoformat') else str(u_date)
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# Key by (date, currency) to support multiple currencies for the same date
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unique_key = f"{date_str}_{curr}"
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if unique_key not in data_by_date:
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data_by_date[unique_key] = {
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"end_date": date_str,
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"currency": curr,
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"update_date": ""
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}
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# Update the group's update_date if we find a newer one in this batch
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if update_date_str > data_by_date[unique_key]["update_date"]:
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data_by_date[unique_key]["update_date"] = update_date_str
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# Normalize key to match frontend expectation (mostly lowercase)
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# e.g. "ROE" -> "roe", "PE" -> "pe"
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norm_indicator = indicator.lower()
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# Special case mapping if needed, but lowercase covers most
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# frontend uses: net_income, revenue, etc.
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data_by_date[unique_key][norm_indicator] = val
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# 4. 转换为列表并排序
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full_list = list(data_by_date.values())
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full_list.sort(key=lambda x: x['end_date'], reverse=True)
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return full_list
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except Exception as e:
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logger.error(f"Error fetching Bloomberg data from {table_name}: {e}", exc_info=True)
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return []
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