"""
Bloomberg 数据服务

负责处理 Bloomberg 特有的数据读取逻辑
从 stockcard 表中提取数据并转换为统一格式
"""
from typing import List, Dict, Optional
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import text
import logging

from app.models import Company

logger = logging.getLogger(__name__)

async def get_bloomberg_data(
    company: Company,
    db: AsyncSession
) -> List[Dict]:
    """
    获取指定公司的 Bloomberg 财务数据
    
    Args:
        company: 公司对象
        db: 数据库会话
        
    Returns:
        List[Dict]: 统一格式的财务数据列表
    """
    try:
        # 1. 查找对应的 Company_code
        # stockcard 中存储的是 "AAPL US Equity" 而 symbol 是 "AAPL"
        target_code = None
        
        # 优先尝试最可能的后缀
        suffixes = [" US Equity", " HK Equity", " JP Equity", " CH Equity", " VN Equity"]
        possible_codes = [f"{company.symbol}{s}" for s in suffixes]
        
        # 检查哪个存在
        for code in possible_codes:
            check_sql = text("SELECT 1 FROM stockcard WHERE Company_code = :code LIMIT 1")
            exists = await db.execute(check_sql, {"code": code})
            if exists.scalar():
                target_code = code
                break
        
        # 如果没找到，尝试模糊匹配 (作为兜底)
        if not target_code:
            fuzzy_sql = text("SELECT Company_code FROM stockcard WHERE Company_code LIKE :symbol LIMIT 1")
            fuzzy_res = await db.execute(fuzzy_sql, {"symbol": f"%{company.symbol}%"})
            row = fuzzy_res.fetchone()
            if row:
                target_code = row[0]
        
        if not target_code:
            logger.warning(f"No Bloomberg data found for symbol: {company.symbol}")
            return []

        # 2. 获取该公司的所有数据
        # schema: indicator, value, value_date (作为报告期)
        # Added update_date
        query = text("""
            SELECT indicator, value, value_date, currency, update_date
            FROM stockcard 
            WHERE Company_code = :code
        """)
        result = await db.execute(query, {"code": target_code})
        
        # 3. 数据透视 (Pivot)
        data_by_date = {}
        
        for row in result:
            indicator = row.indicator
            val = row.value
            v_date = row.value_date
            curr = row.currency
            u_date = row.update_date
            
            if not v_date:
                continue
                
            date_str = v_date.isoformat() if hasattr(v_date, 'isoformat') else str(v_date)
            # Handle update_date formatting
            update_date_str = ""
            if u_date:
                update_date_str = u_date.isoformat() if hasattr(u_date, 'isoformat') else str(u_date)
            
            # Key by (date, currency) to support multiple currencies for the same date
            unique_key = f"{date_str}_{curr}"
            
            if unique_key not in data_by_date:
                data_by_date[unique_key] = {
                    "end_date": date_str,
                    "currency": curr,
                    "update_date": ""
                }
            
            # Update the group's update_date if we find a newer one in this batch
            if update_date_str > data_by_date[unique_key]["update_date"]:
                data_by_date[unique_key]["update_date"] = update_date_str
            
            # Normalize key to match frontend expectation (mostly lowercase)
            # e.g. "ROE" -> "roe", "PE" -> "pe"
            norm_indicator = indicator.lower()
            
            # Special case mapping if needed, but lowercase covers most
            # frontend uses: net_income, revenue, etc.
            
            data_by_date[unique_key][norm_indicator] = val
        
        # 4. 转换为列表并排序
        full_list = list(data_by_date.values())
        full_list.sort(key=lambda x: x['end_date'], reverse=True)
        
        return full_list
            
    except Exception as e:
        logger.error(f"Error fetching Bloomberg data from stockcard: {e}", exc_info=True)
        return []