大改版，默认bloomberg做数据源

.env

@@ -12,4 +12,4 @@ DB_HOST=192.168.3.195
 DB_PORT=5432
 DB_USER=value
 DB_PASSWORD=Value609!
-DB_NAME=FA
+DB_NAME=fa3

QUICKSTART.md

@@ -0,0 +1,168 @@
+# FA3 新架构快速开始指南
+
+## 启动应用
+
+### 1. 启动后端
+```bash
+cd /Users/xucheng/git.qubit.ltd/FA3/backend
+uvicorn app.main:app --reload --port 8000
+```
+
+### 2. 启动前端
+```bash
+cd /Users/xucheng/git.qubit.ltd/FA3/frontend
+npm run dev
+```
+
+### 3. 访问应用
+打开浏览器访问: http://localhost:3000
+
+---
+
+## 使用新架构
+
+### 完整流程示例（HK市场）
+
+1. **搜索公司**
+   - 在搜索框输入 "腾讯"
+   - 点击搜索结果中的 "选择" 按钮
+
+2. **选择数据源**
+   - 系统自动显示 HK 市场可用的数据源（iFinD）
+   - 确认选择或切换数据源
+
+3. **获取数据**
+   - 点击 "获取数据" 按钮
+   - 等待数据获取完成（约30秒-2分钟）
+   - 系统会自动轮询状态并更新
+
+4. **查看财务数据**
+   - 数据获取完成后，自动显示财务表格
+   - 切换 Tab 查看不同报表：
+     - 利润表
+     - 资产负债表
+     - 现金流量表
+     - 估值数据
+
+5. **启动AI分析**
+   - 点击 "开始 AI 分析" 按钮
+   - 等待分析完成（约1-2分钟）
+
+6. **查看分析报告**
+   - 分析完成后，自动显示报告
+   - 切换 Tab 查看不同部分：
+     - 公司简介
+     - 基本面分析
+     - 内部人士分析
+     - 看涨分析
+     - 看跌分析
+
+---
+
+## API 测试
+
+### 使用 curl 测试新API
+
+```bash
+# 1. 检查数据状态
+curl "http://localhost:8000/api/data/check?market=HK&symbol=00700&data_source=iFinD"
+
+# 2. 获取可用数据源
+curl "http://localhost:8000/api/data/sources?market=HK"
+
+# 3. 触发数据获取
+curl -X POST "http://localhost:8000/api/data/fetch" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "market": "HK",
+    "symbol": "00700",
+    "company_name": "腾讯控股",
+    "data_source": "iFinD"
+  }'
+
+# 4. 查询获取状态（替换 {update_id}）
+curl "http://localhost:8000/api/data/status/1"
+
+# 5. 读取财务数据（替换 {company_id}）
+curl "http://localhost:8000/api/data/financial?company_id=1&data_source=iFinD"
+
+# 6. 启动AI分析
+curl -X POST "http://localhost:8000/api/analysis/start" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "company_id": 1,
+    "data_source": "iFinD",
+    "model": "gemini-2.0-flash"
+  }'
+
+# 7. 查询分析状态（替换 {analysis_id}）
+curl "http://localhost:8000/api/analysis/status/1"
+
+# 8. 获取分析结果
+curl "http://localhost:8000/api/analysis/result/1"
+```
+
+---
+
+## 常见问题
+
+### Q: 数据获取失败怎么办？
+A: 检查以下几点：
+1. 确认 API Token 配置正确（.env 文件）
+2. 检查数据库连接是否正常
+3. 查看后端日志中的错误信息
+
+### Q: 前端显示类型错误？
+A: 运行以下命令重新编译：
+```bash
+cd frontend
+npm run build
+```
+
+### Q: Bloomberg 数据源不可用？
+A: Bloomberg Fetcher 需要 Bloomberg API License，目前为存根实现。
+
+### Q: 如何查看数据库中的数据？
+A: 使用 psql 或 pgAdmin：
+```bash
+psql -h 192.168.3.195 -U value -d fa3
+SELECT * FROM companies;
+SELECT * FROM data_updates;
+```
+
+---
+
+## 数据库表结构
+
+### 核心表
+- `companies` - 公司信息
+- `data_updates` - 数据更新记录
+- `ai_analyses` - AI分析报告
+- `data_source_availability` - 数据源可用性
+- `settings` - 系统配置
+
+### 数据源表
+- `ifind_hk_*` - iFinD 香港数据
+- `ifind_int_*` - iFinD 国际数据（JP, US, VN）
+- `bloomberg_*` - Bloomberg 数据
+- `tushare_cn_*` - Tushare 中国数据
+
+---
+
+## 下一步优化
+
+### 待实现功能
+1. `get_financial_data_from_db()` - 从数据库读取财务数据
+2. Bloomberg Fetcher 完整实现
+3. WebSocket 实时更新（替代轮询）
+4. 图表可视化
+5. 数据导出功能
+
+### 已知问题
+- 部分财务指标的中文名称映射需要完善
+- 大数据量表格可能需要虚拟滚动优化
+- 错误提示可以更详细
+
+---
+
+**快速开始完成！** 祝使用愉快 🎉

backend/app/api/analysis_routes.py

@@ -0,0 +1,182 @@
+"""
+AI 分析 API 路由
+处理 LLM 分析的启动、状态查询和结果获取
+"""
+from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, and_
+from typing import List
+
+from app.database import get_db
+from app.schemas import (
+    AnalysisStartRequest,
+    AnalysisStartResponse,
+    AnalysisStatusResponse,
+    AnalysisResultResponse
+)
+from app.models import AIAnalysis, Company
+
+router = APIRouter(prefix="/analysis", tags=["analysis"])
+
+
+@router.post("/start", response_model=AnalysisStartResponse)
+async def start_analysis(
+    request: AnalysisStartRequest,
+    background_tasks: BackgroundTasks,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    启动 AI 分析
+    
+    基于指定公司和数据源的财务数据启动 LLM 分析
+    """
+    # 1. 创建分析记录
+    analysis = AIAnalysis(
+        company_id=request.company_id,
+        data_source=request.data_source,
+        ai_model=request.model,
+        status='pending'
+    )
+    db.add(analysis)
+    await db.commit()
+    await db.refresh(analysis)
+    
+    # 2. 启动后台分析任务
+    background_tasks.add_task(
+        run_analysis_background,
+        analysis_id=analysis.id,
+        company_id=request.company_id,
+        data_source=request.data_source,
+        model=request.model
+    )
+    
+    return AnalysisStartResponse(
+        analysis_id=analysis.id,
+        company_id=request.company_id,
+        data_source=request.data_source,
+        status='pending',
+        message='AI 分析已加入队列'
+    )
+
+
+async def run_analysis_background(
+    analysis_id: int,
+    company_id: int,
+    data_source: str,
+    model: str
+):
+    """后台 AI 分析任务"""
+    from app.database import SessionLocal
+    from app.services.analysis_service import run_llm_analysis
+    from datetime import datetime
+    
+    async with SessionLocal() as db:
+        try:
+            # 更新状态为进行中
+            result = await db.execute(
+                select(AIAnalysis).where(AIAnalysis.id == analysis_id)
+            )
+            analysis = result.scalar_one()
+            analysis.status = 'in_progress'
+            await db.commit()
+            
+            # 执行 LLM 分析
+            analysis_result = await run_llm_analysis(
+                company_id=company_id,
+                data_source=data_source,
+                model=model,
+                db=db
+            )
+            
+            # 更新分析结果
+            analysis.status = 'completed'
+            analysis.company_profile = analysis_result.get('company_profile')
+            analysis.fundamental_analysis = analysis_result.get('fundamental_analysis')
+            analysis.insider_analysis = analysis_result.get('insider_analysis')
+            analysis.bullish_analysis = analysis_result.get('bullish_analysis')
+            analysis.bearish_analysis = analysis_result.get('bearish_analysis')
+            analysis.total_tokens = analysis_result.get('total_tokens', 0)
+            analysis.tokens_by_section = analysis_result.get('tokens_by_section')
+            analysis.completed_at = datetime.now()
+            
+            await db.commit()
+            
+        except Exception as e:
+            # 更新为失败状态
+            result = await db.execute(
+                select(AIAnalysis).where(AIAnalysis.id == analysis_id)
+            )
+            analysis = result.scalar_one()
+            analysis.status = 'failed'
+            analysis.error_message = str(e)
+            await db.commit()
+
+
+@router.get("/status/{analysis_id}", response_model=AnalysisStatusResponse)
+async def get_analysis_status(
+    analysis_id: int,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    查询分析状态
+    
+    通过 analysis_id 查询 AI 分析任务的当前状态
+    """
+    result = await db.execute(
+        select(AIAnalysis).where(AIAnalysis.id == analysis_id)
+    )
+    analysis = result.scalar_one_or_none()
+    
+    if not analysis:
+        raise HTTPException(status_code=404, detail="分析记录不存在")
+    
+    return analysis
+
+
+@router.get("/result/{analysis_id}", response_model=AnalysisResultResponse)
+async def get_analysis_result(
+    analysis_id: int,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    获取分析结果
+    
+    获取已完成的 AI 分析的完整报告内容
+    """
+    result = await db.execute(
+        select(AIAnalysis).where(AIAnalysis.id == analysis_id)
+    )
+    analysis = result.scalar_one_or_none()
+    
+    if not analysis:
+        raise HTTPException(status_code=404, detail="分析记录不存在")
+    
+    if analysis.status != 'completed':
+        raise HTTPException(status_code=400, detail="分析尚未完成")
+    
+    return analysis
+
+
+@router.get("/history", response_model=List[AnalysisStatusResponse])
+async def get_analysis_history(
+    company_id: int,
+    data_source: str = None,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    历史分析列表
+    
+    获取指定公司的所有历史分析记录
+    可选择按数据源筛选
+    """
+    query = select(AIAnalysis).where(AIAnalysis.company_id == company_id)
+    
+    if data_source:
+        query = query.where(AIAnalysis.data_source == data_source)
+    
+    query = query.order_by(AIAnalysis.created_at.desc())
+    
+    result = await db.execute(query)
+    analyses = result.scalars().all()
+    
+    return analyses

backend/app/api/data_routes.py

@@ -0,0 +1,317 @@
+"""
+数据管理 API 路由
+处理财务数据的检查、获取和读取
+"""
+from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks
+from sqlalchemy.ext.asyncio import AsyncSession
+from typing import List
+
+from app.database import get_db
+from app.schemas import (
+    DataCheckRequest,
+    DataCheckResponse,
+    FetchDataRequest,
+    FetchDataResponse,
+    DataUpdateResponse,
+    FinancialDataResponse,
+    DataSourceListResponse,
+    DataSourceInfo
+)
+from app.services import data_fetcher_service
+from app.models import DataUpdate
+from sqlalchemy import select
+
+router = APIRouter(prefix="/data", tags=["data"])
+
+
+@router.get("/check", response_model=DataCheckResponse)
+async def check_data_status(
+    market: str,
+    symbol: str,
+    data_source: str,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    检查公司数据状态
+    
+    查询数据库中是否存在指定公司和数据源的数据，
+    如果存在则返回最近一次更新的时间和数据范围
+    """
+    return await data_fetcher_service.check_data_status(
+        market=market,
+        symbol=symbol,
+        data_source=data_source,
+        db=db
+    )
+
+
+@router.post("/fetch", response_model=FetchDataResponse)
+async def fetch_data(
+    request: FetchDataRequest,
+    background_tasks: BackgroundTasks,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    获取/更新财务数据
+    
+    从指定数据源获取公司的财务数据并存储到数据库
+    此操作在后台异步执行
+    """
+    # 1. 创建或获取公司记录
+    company = await data_fetcher_service.create_or_get_company(
+        market=request.market,
+        symbol=request.symbol,
+        company_name=request.company_name,
+        db=db
+    )
+    
+    # 2. 创建数据更新记录
+    data_update = await data_fetcher_service.create_data_update_record(
+        company_id=company.id,
+        data_source=request.data_source,
+        update_type='full',
+        db=db
+    )
+    
+    # 3. 启动后台任务
+    background_tasks.add_task(
+        fetch_data_background,
+        company_id=company.id,
+        market=request.market,
+        symbol=request.symbol,
+        data_source=request.data_source,
+        update_id=data_update.id
+    )
+    
+    return FetchDataResponse(
+        update_id=data_update.id,
+        data_source=request.data_source,
+        status='in_progress',
+        message=f'正在从 {request.data_source} 获取数据，请稍候...'
+    )
+
+
+def fetch_data_background(
+    company_id: int,
+    market: str,
+    symbol: str,
+    data_source: str,
+    update_id: int
+):
+    """后台数据获取任务 - 完全同步执行,避免event loop冲突"""
+    import sys
+    import os
+    from pathlib import Path
+    import psycopg2
+    from datetime import datetime
+    
+    # 确保项目根目录在 Python 路径中
+    project_root = Path(__file__).parent.parent.parent.parent
+    if str(project_root) not in sys.path:
+        sys.path.insert(0, str(project_root))
+    
+    try:
+        # 执行数据获取（同步）
+        result = data_fetcher_service.fetch_financial_data_sync(
+            company_id=company_id,
+            market=market,
+            symbol=symbol,
+            data_source=data_source,
+            update_id=update_id
+        )
+        
+        # 更新数据更新记录 - 使用psycopg2同步连接
+        conn = psycopg2.connect(
+            host=os.getenv('DB_HOST', '192.168.3.195'),
+            user=os.getenv('DB_USER', 'value'),
+            password=os.getenv('DB_PASSWORD', 'Value609!'),
+            dbname=os.getenv('DB_NAME', 'fa3'),
+            port=os.getenv('DB_PORT', '5432')
+        )
+        
+        cur = conn.cursor()
+        
+        # 更新记录
+        update_sql = """
+            UPDATE data_updates 
+            SET status = %s, 
+                completed_at = %s,
+                error_message = %s,
+                data_start_date = %s,
+                data_end_date = %s,
+                fetched_tables = %s,
+                row_counts = %s
+            WHERE id = %s
+        """
+        
+        import json
+        cur.execute(
+            update_sql,
+            (
+                result['status'],
+                datetime.now() if result['status'] == 'completed' else None,
+                result.get('error_message'),
+                result.get('data_start_date'),
+                result.get('data_end_date'),
+                json.dumps(result.get('fetched_tables')),
+                json.dumps(result.get('row_counts')),
+                update_id
+            )
+        )
+        conn.commit()
+        cur.close()
+        conn.close()
+        
+    except Exception as e:
+        print(f"❌ 后台任务执行错误: {e}")
+        import traceback
+        traceback.print_exc()
+        
+        # 尝试更新错误状态
+        try:
+            conn = psycopg2.connect(
+                host=os.getenv('DB_HOST', '192.168.3.195'),
+                user=os.getenv('DB_USER', 'value'),
+                password=os.getenv('DB_PASSWORD', 'Value609!'),
+                dbname=os.getenv('DB_NAME', 'fa3'),
+                port=os.getenv('DB_PORT', '5432')
+            )
+            cur = conn.cursor()
+            cur.execute(
+                "UPDATE data_updates SET status = %s, error_message = %s, completed_at = %s WHERE id = %s",
+                ('failed', str(e), datetime.now(), update_id)
+            )
+            conn.commit()
+            cur.close()
+            conn.close()
+        except:
+            pass
+
+
+
+@router.get("/status/{update_id}", response_model=DataUpdateResponse)
+async def get_fetch_status(
+    update_id: int,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    查询数据获取状态
+    
+    通过 update_id 查询数据获取任务的当前状态
+    """
+    result = await db.execute(
+        select(DataUpdate).where(DataUpdate.id == update_id)
+    )
+    data_update = result.scalar_one_or_none()
+    
+    if not data_update:
+        raise HTTPException(status_code=404, detail="数据更新记录不存在")
+    
+    # ⚠️ 特殊处理：由于 fetched_tables 和 row_counts 现在是 TEXT 类型存储 JSON 字符串
+    # 我们需要手动解析它们，否则 Pydantic 会校验失败
+    import json
+    
+    fetched_tables = []
+    if data_update.fetched_tables:
+        if isinstance(data_update.fetched_tables, str):
+            try:
+                fetched_tables = json.loads(data_update.fetched_tables)
+            except:
+                fetched_tables = []
+        else:
+            fetched_tables = data_update.fetched_tables
+
+    row_counts = {}
+    if data_update.row_counts:
+        if isinstance(data_update.row_counts, str):
+            try:
+                row_counts = json.loads(data_update.row_counts)
+            except:
+                row_counts = {}
+        else:
+            row_counts = data_update.row_counts
+            
+    # 手动构建响应对象
+    return DataUpdateResponse(
+        id=data_update.id,
+        company_id=data_update.company_id,
+        data_source=data_update.data_source,
+        update_type=data_update.update_type,
+        status=data_update.status,
+        progress_message=data_update.progress_message,
+        progress_percentage=data_update.progress_percentage,
+        started_at=data_update.started_at,
+        completed_at=data_update.completed_at,
+        error_message=data_update.error_message,
+        data_start_date=data_update.data_start_date,
+        data_end_date=data_update.data_end_date,
+        fetched_tables=fetched_tables, # 传入解析后的 List
+        row_counts=row_counts          # 传入解析后的 Dict
+    )
+
+
+@router.get("/financial", response_model=FinancialDataResponse)
+async def get_financial_data(
+    company_id: int,
+    data_source: str,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    读取财务数据
+    
+    从数据库中读取指定公司和数据源的所有财务数据
+    """
+    try:
+        data = await data_fetcher_service.get_financial_data_from_db(
+            company_id=company_id,
+            data_source=data_source,
+            db=db
+        )
+        
+        # 递归清理 NaN/Infinity 值，这会导致 JSON 序列化失败
+        import math
+        def clean_nan(obj):
+            if isinstance(obj, float):
+                if math.isnan(obj) or math.isinf(obj):
+                    return None
+                return obj
+            elif isinstance(obj, dict):
+                return {k: clean_nan(v) for k, v in obj.items()}
+            elif isinstance(obj, list):
+                return [clean_nan(v) for v in obj]
+            return obj
+            
+        return clean_nan(data)
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=str(e))
+    
+    return data
+
+
+@router.get("/sources", response_model=DataSourceListResponse)
+async def get_available_sources(market: str):
+    """
+    获取可用数据源列表
+    
+    返回指定市场支持的所有数据源
+    """
+    sources = data_fetcher_service.get_available_data_sources(market)
+    
+    return DataSourceListResponse(
+        market=market,
+        sources=[DataSourceInfo(**s) for s in sources]
+    )
+
+@router.get("/recent", response_model=List[dict])
+async def get_recent_companies(
+    data_source: str = "Bloomberg",
+    db: AsyncSession = Depends(get_db)
+):
+    """获取最近更新的公司列表"""
+    return await data_fetcher_service.get_recent_companies(
+        data_source=data_source,
+        db=db,
+        limit=20
+    )

backend/app/api/routes.py

@@ -3,9 +3,9 @@ from fastapi.responses import HTMLResponse, FileResponse
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select
 from sqlalchemy.orm import selectinload
-from app.database import get_db
+from app.legacy.database_old import get_db
-from app.schemas import StockSearchRequest, StockSearchResponse, AnalysisRequest, ReportResponse, AnalysisStatus, ConfigUpdateRequest
+from app.legacy.schemas import StockSearchRequest, StockSearchResponse, AnalysisRequest, ReportResponse, AnalysisStatus, ConfigUpdateRequest
-from app.models import Report, Setting
+from app.legacy.models_old import Report, Setting
 from app.services import analysis_service
 import os
 import markdown
@@ -89,6 +89,23 @@ async def get_reports(db: AsyncSession = Depends(get_db)):
     result = await db.execute(select(Report).options(selectinload(Report.sections)).order_by(Report.created_at.desc()))
     return result.scalars().all()
 
+@router.get("/reports")
+async def get_all_reports(db: AsyncSession = Depends(get_db)):
+    """获取所有报告列表"""
+    result = await db.execute(
+        select(Report).order_by(Report.created_at.desc()).limit(100)
+    )
+    reports = result.scalars().all()
+    return [{
+        "id": r.id,
+        "market": r.market,
+        "symbol": r.symbol,
+        "company_name": r.company_name,
+        "status": r.status,
+        "ai_model": r.ai_model,
+        "created_at": r.created_at.isoformat() if r.created_at else None
+    } for r in reports]
+
 @router.get("/reports/{report_id}", response_model=ReportResponse)
 async def get_report(report_id: int, db: AsyncSession = Depends(get_db)):
     result = await db.execute(select(Report).options(selectinload(Report.sections)).where(Report.id == report_id))

backend/app/clients/__init__.py

@@ -0,0 +1,3 @@
+"""
+Client package initialization
+"""

backend/app/clients/bloomberg_client.py

@@ -0,0 +1,775 @@
+
+import os
+import sys
+import json
+import uuid
+import time
+import requests
+import websocket
+import psycopg2
+import pandas as pd
+from psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT
+from datetime import datetime
+from dotenv import load_dotenv
+from pathlib import Path
+import logging
+
+# Configure logger
+logger = logging.getLogger(__name__)
+
+# Explicitly load .env
+ROOT_DIR = Path(__file__).resolve().parent.parent.parent.parent
+load_dotenv(ROOT_DIR / ".env")
+
+# --- Configurations ---
+
+CURRENCY_CONFIG = {
+    "Revenue": "SALES_REV_TURN",
+    "Net_Income": "EARN_FOR_COMMON",
+    "Cash_From_Operating": "CF_CASH_FROM_OPER",
+    "Capital_Expenditure": "CAPITAL_EXPEND",
+    "Free_Cash_Flow": "CF_FREE_CASH_FLOW",
+    "Dividends_Paid": "CF_DVD_PAID",
+    "Total_Assets": "BS_TOT_ASSET",
+    "Equity": "BS_TOT_EQY",
+    "Goodwill": "BS_GOODWILL",
+    "SG&A": "IS_SG_AND_A_EXPENSE",
+    "Selling&Marketing": "IS_SELLING_EXPENSES",
+    "General&Admin": "IS_GENERAL_AND_ADMIN_GAAP",
+    "R&D": "IS_RD_EXPEND",
+    "Depreciation": "IS_DEPR_EXP",
+    "Cash": "BS_CASH_NEAR_CASH_ITEM",
+    "Inventory": "BS_INVENTORIES",
+    "Accounts&Notes_Receivable": "BS_ACCT_NOTE_RCV",
+    "Prepaid": "BS_PREPAY",
+    "Property_Plant&Equipment": "BS_NET_FIX_ASSET",
+    "LT_Investment": "BS_LT_INVEST",
+    "Accounts_Payable": "BS_ACCT_PAYABLE",
+    "ST_Debt": "BS_ST_BORROW",
+    "LT_Debt": "BS_LT_BORROW",
+    "ST_Defer_Rev":"ST_DEFERRED_REVENUE",
+    "repurchase":"cf_proceeds_repurchase_equity"
+}
+
+NON_CURRENCY_CONFIG = {
+    "ROE": "RETURN_COM_EQY",
+    "ROA": "RETURN_ON_ASSET",
+    "ROCE": "RETURN_ON_CAPITAL_EMPLOYED",
+    "Gross_Margin": "GROSS_MARGIN",
+    "EBITDA_margin": "EBITDA_TO_REVENUE",
+    "Net_Profit_Margin": "PROF_MARGIN",
+    "Tax_Rate": "IS_STATUTORY_TAX_RATE",
+    "Inventory_Days": "INVENT_DAYS",
+    "Days_Sales_Outstanding": "ANNUALIZED_DAYS_SALES_OUTSTDG",
+    "Payables_Days": "ACCOUNTS_PAYABLE_TURNOVER_DAYS",
+    "Employee": "NUM_OF_EMPLOYEES",
+    "PE": "PE_RATIO",
+    "PB": "PX_TO_BOOK_RATIO",
+    "Shareholders": "BS_NUM_OF_SHAREHOLDERS",
+    "Dividend_Payout_Ratio":"DVD_PAYOUT_RATIO",
+    "Total_Debt_Ratio":"TOT_DEBT_TO_TOT_ASSET",
+    "Net_Fixed_Asset_Turnover":"NET_FIX_ASSET_TURN",
+    "Asset_Turnover":"ASSET_TURNOVER",
+    "NetIncome_Growth":"earn_for_com_growth",
+    "Revenue_Growth":"sales_growth",
+}
+
+PRICE_CONFIG = {
+    "Last_Price": "PX_LAST",
+    "Market_Cap":"cur_mkt_cap",
+    "Dividend_Yield": "DIVIDEND_12_MONTH_YIELD"
+}
+
+STOCKCARD_CONFIG = {
+    "period": 15,
+    "unit": 100000000
+}
+
+
+class BloombergClient:
+    """
+    Backend Bloomberg Client.
+    Connects to remote Jupyter Kernel via WebSocket to execute 'blp' code.
+    Persists data to 'stockcard' table (EAV pattern) using direct DB connection.
+    """
+    def __init__(self, jupyter_url=None, password=None):
+        self.jupyter_url = jupyter_url or os.getenv("JUPYTER_URL", "http://192.168.3.161:8888")
+        self.jupyter_url = self.jupyter_url.rstrip("/")
+        self.password = password or os.getenv("JUPYTER_PASSWORD", "Value609!")
+        self.session = requests.Session()
+        self.kernel_id = None
+        self.ws = None
+        
+        # Authenticate and setup connection
+        try:
+            self._authenticate()
+            self._setup_kernel()
+            self._connect_websocket()
+        except Exception as e:
+            logger.error(f"Failed to initialize Bloomberg Client connection: {e}")
+            # Don't raise here, allow instantiation but methods might fail
+            # or caller handles initialization failure
+
+    def _authenticate(self):
+        """Authenticate with JupyterHub/Lab using password to get session cookies."""
+        logger.info(f"Connecting to Jupyter at {self.jupyter_url}...")
+        try:
+            # 1. Get Login Page to fetch _xsrf (if needed)
+            r = self.session.get(f"{self.jupyter_url}/login")
+            if r.status_code != 200:
+                logger.warning(f"Failed to access login page: {r.status_code}")
+            
+            xsrf = self.session.cookies.get("_xsrf", "")
+            
+            # 2. Post Password
+            data = {"password": self.password}
+            if xsrf:
+                data["_xsrf"] = xsrf
+                
+            login_resp = self.session.post(f"{self.jupyter_url}/login", data=data)
+            
+            # 3. Verify Auth
+            api_resp = self.session.get(f"{self.jupyter_url}/api/status")
+            if api_resp.status_code == 200:
+                logger.info("✅ Authentication successful.")
+            else:
+                raise Exception(f"Authentication failed. Status: {api_resp.status_code}")
+                
+        except Exception as e:
+            logger.error(f"❌ Error during authentication: {e}")
+            raise
+
+    def _setup_kernel(self):
+        """Find an existing kernel or create a new one."""
+        try:
+            # List kernels
+            resp = self.session.get(f"{self.jupyter_url}/api/kernels")
+            kernels = resp.json()
+            
+            # Try to find 'remote_env' or just pick the first available
+            target_kernel = None
+            for k in kernels:
+                if k.get('name') == 'remote_env': # Ideal match
+                    target_kernel = k
+                    break
+            
+            if not target_kernel and kernels:
+                target_kernel = kernels[0] # Fallback
+                
+            if target_kernel:
+                self.kernel_id = target_kernel['id']
+                logger.info(f"✅ Found existing kernel: {self.kernel_id} ({target_kernel.get('name')})")
+            else:
+                logger.info("No active kernels found. Starting a new one...")
+                # Start new kernel (assuming python3)
+                start_resp = self.session.post(f"{self.jupyter_url}/api/kernels", json={"name": "python3"})
+                if start_resp.status_code == 201:
+                    self.kernel_id = start_resp.json()['id']
+                    logger.info(f"✅ Started new kernel: {self.kernel_id}")
+                else:
+                    raise Exception(f"Failed to start kernel: {start_resp.text}")
+
+        except Exception as e:
+            logger.error(f"❌ Error setting up kernel: {e}")
+            raise
+
+    def _connect_websocket(self):
+        """Connect to the Kernel's WebSocket channel."""
+        base_ws_url = self.jupyter_url.replace("http", "ws")
+        ws_url = f"{base_ws_url}/api/kernels/{self.kernel_id}/channels"
+        
+        # Extract cookies for WS connection
+        cookies = self.session.cookies.get_dict()
+        cookie_str = "; ".join([f"{k}={v}" for k, v in cookies.items()])
+        
+        try:
+            self.ws = websocket.create_connection(
+                ws_url, 
+                header={"Cookie": cookie_str},
+                timeout=60 # Connection timeout
+            )
+            logger.info("✅ WebSocket connected.")
+        except Exception as e:
+            logger.error(f"❌ WebSocket connection failed: {e}")
+            raise
+
+    def execute_remote_code(self, code_str):
+        """
+        Send code to remote kernel via WebSocket and wait for result.
+        Returns list of output strings (stdout).
+        """
+        if not self.ws:
+            raise Exception("WebSocket not connected")
+
+        msg_id = str(uuid.uuid4())
+        msg = {
+            "header": {
+                "msg_id": msg_id,
+                "username": "client",
+                "session": str(uuid.uuid4()),
+                "msg_type": "execute_request",
+                "version": "5.3"
+            },
+            "parent_header": {},
+            "metadata": {},
+            "content": {
+                "code": code_str,
+                "silent": False,
+                "store_history": True,
+                "user_expressions": {},
+                "allow_stdin": False
+            }
+        }
+        
+        self.ws.send(json.dumps(msg))
+        
+        outputs = []
+        error = None
+        
+        # Loop to receive messages until 'idle'
+        while True:
+            try:
+                resp = json.loads(self.ws.recv())
+                msg_type = resp['msg_type']
+                
+                # Check for idle status (command finished)
+                if msg_type == 'status':
+                    if resp['content']['execution_state'] == 'idle':
+                        # Make sure it's the response to OUR request (simple check)
+                        if resp['parent_header'].get('msg_id') == msg_id:
+                            break
+                            
+                elif msg_type == 'stream':
+                    # Stdout/Stderr
+                    outputs.append(resp['content']['text'])
+                    
+                elif msg_type == 'error':
+                    error = resp['content']['evalue']
+                    logger.error(f"❌ Remote Execution Error: {error}")
+                    for line in resp['content']['traceback']:
+                        logger.error(line)
+                        
+            except Exception as e:
+                logger.error(f"Error receiving WS message: {e}")
+                break
+                
+        if error:
+            raise Exception(f"Remote Code Execution Failed: {error}")
+            
+        return "".join(outputs)
+
+    # --- Database Operations ---
+    
+    def _get_db_connection(self):
+        """Create a database connection."""
+        db_host = os.getenv("DB_HOST", "192.168.3.195")
+        db_user = os.getenv("DB_USER", "value")
+        db_pass = os.getenv("DB_PASSWORD", "Value609!")
+        db_name = os.getenv("DB_NAME", "fa3") # Default to fa3 now? fa legacy used 'FA'
+        db_port = os.getenv("DB_PORT", "5432")
+
+        try:
+            conn = psycopg2.connect(
+                host=db_host, user=db_user, password=db_pass, dbname=db_name, port=db_port
+            )
+            return conn
+        except psycopg2.OperationalError as e:
+            logger.error(f"DB Connection Error: {e}")
+            return None
+
+    def _ensure_schema(self, conn):
+        """Ensure the necessary tables exist."""
+        with conn.cursor() as cur:
+            # Create table if not exists (Inferred schema from legacy use)
+            cur.execute("""
+                CREATE TABLE IF NOT EXISTS stockcard (
+                    id SERIAL PRIMARY KEY,
+                    Company_code TEXT,
+                    update_date DATE,
+                    currency TEXT,
+                    indicator TEXT,
+                    value TEXT,
+                    value_date DATE,
+                    source TEXT
+                );
+            """)
+            
+            # Ensure column exists if table was already created
+            try:
+                cur.execute("ALTER TABLE stockcard ADD COLUMN IF NOT EXISTS source TEXT;")
+            except Exception:
+                conn.rollback() # Should not happen with IF NOT EXISTS but good practice
+            
+            conn.commit()
+
+    def save_data(self, data_list):
+        """Insert a list of data dictionaries into the database."""
+        if not data_list:
+            return
+            
+        conn = self._get_db_connection()
+        if not conn: return
+
+        self._ensure_schema(conn)
+        
+        try:
+            with conn.cursor() as cur:
+                args_list = []
+                for d in data_list:
+                    # Format: (Company_code, update_date, currency, indicator, value, value_date, source)
+                    args_list.append((
+                        d['Company_code'],
+                        d['update_date'],
+                        d['currency'],
+                        d['indicator'],
+                        d['value'],
+                        d['value_date'],
+                        'bloomberg'
+                    ))
+                
+                query = """
+                    INSERT INTO stockcard (Company_code, update_date, currency, indicator, value, value_date, source)
+                    VALUES (%s, %s, %s, %s, %s, %s, %s)
+                """
+                cur.executemany(query, args_list)
+                conn.commit()
+                logger.info(f"✅ Saved {len(data_list)} records to database.")
+        except Exception as e:
+            logger.error(f"Error saving to stockcard: {e}")
+            conn.rollback()
+        finally:
+            conn.close()
+
+    def run_cleanup(self):
+        """Run deduplication and view refresh logic."""
+        conn = self._get_db_connection()
+        if not conn: return
+        
+        try:
+            with conn.cursor() as cur:
+                # 1. Deduplication
+                cur.execute('''
+                WITH DuplicateRows AS (
+                    SELECT
+                        id,
+                        ROW_NUMBER() OVER(
+                            PARTITION BY company_code, currency, indicator, value_date
+                            ORDER BY update_date DESC
+                        ) as rn
+                    FROM
+                        stockcard
+                )
+                DELETE FROM stockcard
+                WHERE id IN (
+                    SELECT id
+                    FROM DuplicateRows
+                    WHERE rn > 1
+                );
+                ''')
+                
+                # 2. Materialized View Refresh
+                cur.execute('''
+                    CREATE MATERIALIZED VIEW IF NOT EXISTS public.unique_company_codes AS
+                    SELECT
+                        s.Company_code,
+                        (SELECT cn_sub.value
+                        FROM public.stockcard AS cn_sub
+                        WHERE cn_sub.Company_code = s.Company_code
+                        AND cn_sub.indicator = 'company_name'
+                        ORDER BY cn_sub.value ASC
+                        LIMIT 1) AS company_name
+                    FROM
+                        (SELECT DISTINCT Company_code FROM public.stockcard WHERE Company_code IS NOT NULL) s
+                    ORDER BY
+                        s.Company_code;
+                ''')
+                # Try refresh
+                try:
+                    cur.execute("REFRESH MATERIALIZED VIEW public.unique_company_codes;")
+                except:
+                    pass
+                
+                conn.commit()
+                logger.info("✅ Cleanup and View Refresh completed.")
+        except Exception as e:
+            logger.error(f"❌ Cleanup failed: {e}")
+            conn.rollback()
+        finally:
+            conn.close()
+
+    # --- Core Fetching Logic ---
+
+    def fetch_company(self, market, symbol, progress_callback=None):
+        """
+        Main entry point to fetch data for a single company.
+        """
+        # Determine Bloomberg Ticker format
+        # If symbol already has Equity, use it. Else append.
+        if "Equity" in symbol:
+            company_code = symbol
+        else:
+            # Special case for China market: use 'CH Equity' instead of 'CN Equity'
+            mapped_market = "CH" if market == "CN" else market
+            company_code = f"{symbol} {mapped_market} Equity"
+            
+        today_str = datetime.now().strftime('%Y-%m-%d')
+        
+        logger.info(f"🚀 Starting fetch for: {company_code}")
+        if progress_callback: progress_callback("Starting Bloomberg session...", 0)
+        
+        # 0. Prep Remote Environment
+        init_code = """
+import json
+import pandas as pd
+from datetime import datetime
+try:
+    from blp import blp
+except ImportError:
+    print("Error: 'blp' module not found.")
+
+# Ensure bquery is started
+if 'bquery' not in globals():
+    try:
+        bquery = blp.BlpQuery().start()
+        print("BlpQuery started.")
+    except Exception as e:
+        print(f"Error starting BlpQuery: {e}")
+"""
+        try:
+            self.execute_remote_code(init_code)
+
+            # 1. Fetch Basic Info
+            logger.info("Fetching Basic Data...")
+            if progress_callback: progress_callback("Fetching Company Basic Info...", 10)
+            basic_data = self._fetch_basic_remote(company_code, "USD") 
+            self.save_data(basic_data)
+            
+            # Determine currency
+            currency = "USD"
+            if "JP" in market.upper(): currency = "JPY"
+            elif "VN" in market.upper(): currency = "VND"
+            elif "CN" in market.upper(): currency = "CNY"
+            elif "HK" in market.upper(): currency = "HKD"
+            
+            logger.info(f"Using currency: {currency}")
+
+            # 2. Fetch Currency Data
+            logger.info("Fetching Currency Data...")
+            if progress_callback: progress_callback(f"正在获取货币指标 ({currency})...", 30)
+            curr_data = self._fetch_series_remote(company_code, currency, CURRENCY_CONFIG, "currency")
+            self.save_data(curr_data)
+
+            # 3. Fetch Non-Currency Data
+            logger.info("Fetching Non-Currency Data...")
+            if progress_callback: progress_callback("正在获取非货币指标...", 50)
+            non_curr_data = self._fetch_series_remote(company_code, currency, NON_CURRENCY_CONFIG, "non_currency")
+            self.save_data(non_curr_data)
+            
+            # 4. Fetch Price Data (Aligned with Revenue Dates)
+            logger.info("Fetching Price Data (Aligned)...")
+            if progress_callback: progress_callback("正在获取价格指标...", 70)
+            
+            # Extract Revenue dates
+            revenue_dates = []
+            rev_key = CURRENCY_CONFIG.get("Revenue", "SALES_REV_TURN")
+            # The saved data uses indicator name from config keys (e.g. "Revenue")
+            # So looking for "Revenue" in saved entries
+            
+            for item in curr_data:
+                # Check for "Revenue" (case insensitive match if needed)
+                if item['indicator'].lower() == 'revenue':
+                    if item['value_date']:
+                         # Ensure YYYY-MM-DD
+                        revenue_dates.append(item['value_date'])
+            
+            # Remove specs, duplicates, sort
+            revenue_dates = sorted(list(set(revenue_dates)), reverse=True)
+            
+            if revenue_dates:
+                logger.info(f"Found {len(revenue_dates)} revenue reporting dates. Fetching aligned price data...")
+                price_data = self._fetch_price_by_dates_remote(company_code, currency, revenue_dates)
+                self.save_data(price_data)
+            else:
+                logger.warning("No revenue dates found. Falling back to yearly price fetch.")
+                price_data = self._fetch_series_remote(company_code, currency, PRICE_CONFIG, "price")
+                self.save_data(price_data)
+
+            # 5. Cleanup
+            if progress_callback: progress_callback("Finalizing data...", 90)
+            self.run_cleanup()
+            logger.info(f"✅ Completed processing for {company_code}")
+            if progress_callback: progress_callback("Bloomberg data sync complete", 100)
+            
+        except Exception as e:
+            logger.error(f"Fetch failed for {company_code}: {e}")
+            if progress_callback: progress_callback(f"Error: {e}", 0)
+            raise e
+
+    def _fetch_basic_remote(self, company_code, currency):
+        """Generates code to fetch basic data"""
+        code = f"""
+def get_basic():
+    company = "{company_code}"
+    curr = "{currency}"
+    res_list = []
+    
+    # 1. BQL query
+    q = f"for(['{{company}}']) get(name,listing_date,pe_ratio,px_to_book_ratio,cur_mkt_cap(currency={{curr}}),PCT_REVENUE_FROM_FOREIGN_SOURCES)"
+    try:
+        df = bquery.bql(q)
+        if not df.empty:
+            def get_val(df, field_name):
+                if field_name == 'name':
+                    rows = df[df['field'] == 'name']
+                elif 'cur_mkt_cap' in field_name:
+                    rows = df[df['field'].str.contains('cur_mkt_cap')]
+                elif 'FOREIGN' in field_name:
+                    rows = df[df['field'].str.contains('FOREIGN')]
+                else:
+                    rows = df[df['field'] == field_name]
+                
+                if not rows.empty:
+                    val = rows['value'].iloc[0]
+                    # Handle Timestamp object from remote pandas
+                    if hasattr(val, 'strftime'):
+                        return val.strftime('%Y-%m-%d')
+                    return str(val) if val is not None else None
+                return None
+
+            res_list.append({{"indicator": "company_name", "value": get_val(df, 'name')}})
+            res_list.append({{"indicator": "pe_ratio", "value": get_val(df, 'pe_ratio')}})
+            res_list.append({{"indicator": "pb_ratio", "value": get_val(df, 'pb_ratio')}})
+            res_list.append({{"indicator": "market_cap", "value": get_val(df, 'cur_mkt_cap')}})
+            res_list.append({{"indicator": "Rev_Abroad", "value": get_val(df, 'FOREIGN')}})
+    except Exception as e:
+        print(f"Basic BQL Error: {{e}}")
+
+    # 2. BDP for IPO and Dividend
+    try:
+        did = bquery.bdp([company], ["DIVIDEND_12_MONTH_YIELD"])
+        if not did.empty and 'DIVIDEND_12_MONTH_YIELD' in did.columns:
+            res_list.append({{"indicator": "dividend_yield", "value": str(did['DIVIDEND_12_MONTH_YIELD'][0])}})
+        
+        ipo = bquery.bdp([company], ["EQY_INIT_PO_DT"])
+        if not ipo.empty and 'EQY_INIT_PO_DT' in ipo.columns:
+            val = ipo['EQY_INIT_PO_DT'][0]
+            val_str = str(val)
+            if hasattr(val, 'strftime'):
+                val_str = val.strftime('%Y-%m-%d')
+            res_list.append({{"indicator": "IPO_date", "value": val_str}})
+            
+    except Exception as e:
+        print(f"Basic BDP Error: {{e}}")
+
+    # Format result
+    final_res = []
+    today = datetime.now().strftime('%Y-%m-%d')
+    for item in res_list:
+        if item['value']:
+            final_res.append({{
+                "Company_code": company,
+                "update_date": today,
+                "currency": curr,
+                "indicator": item['indicator'],
+                "value": item['value'],
+                "value_date": today
+            }})
+    
+    print("JSON_START")
+    print(json.dumps(final_res))
+    print("JSON_END")
+
+get_basic()
+"""
+        return self._execute_and_parse(code)
+
+    def _fetch_series_remote(self, company_code, currency, config_dict, result_type):
+        """Generates code to fetch series data using BDH"""
+        
+        config_json = json.dumps(config_dict)
+        period_years = STOCKCARD_CONFIG['period']
+        
+        start_year = datetime.now().year - period_years
+        start_date = f"{start_year}0101"
+        end_date = datetime.now().strftime('%Y%m%d')
+
+        bdh_options = {
+            'periodicitySelection': 'YEARLY',
+            'currency': currency,
+            'nonTradingDayFillOption': 'ALL_CALENDAR_DAYS',
+            'nonTradingDayFillMethod': 'PREVIOUS_VALUE'
+        }
+        bdh_opts_json = json.dumps(bdh_options)
+        
+        code = f"""
+def get_series():
+    company = "{company_code}"
+    curr = "{currency}"
+    config = {config_json}
+    
+    mnemonic_map = {{v.upper(): k for k, v in config.items()}}
+    fields = list(mnemonic_map.keys())
+    
+    res_list = []
+    
+    try:
+        df = bquery.bdh(
+            [company], 
+            fields, 
+            start_date='{start_date}', 
+            end_date='{end_date}', 
+            options={bdh_opts_json}
+        )
+        
+        if not df.empty:
+            for _, row in df.iterrows():
+                date_val = None
+                if 'date' in df.columns: date_val = row['date']
+                elif 'DATE' in df.columns: date_val = row['DATE']
+                
+                if not date_val: continue
+                
+                date_str = str(date_val)[:10]
+                
+                for mnemonic, indicator_name in mnemonic_map.items():
+                    col_name = mnemonic
+                    # Handle multi-column result from bdh if any
+                    # Usually bdh returns column names as requested fields
+                    
+                    val = None
+                    # Flexible column matching
+                    if col_name in row:
+                        val = row[col_name]
+                    elif col_name in df.columns:
+                        val = row[col_name]
+                    else:
+                        # Try case insensitive
+                        for c in df.columns:
+                            if c.upper() == col_name:
+                                val = row[c]
+                                break
+                    
+                    if pd.isna(val) or val is None:
+                        continue
+                        
+                    val_str = str(val)
+                    
+                    res_list.append({{
+                        "Company_code": company,
+                        "update_date": "{datetime.now().strftime('%Y-%m-%d')}",
+                        "currency": curr,
+                        "indicator": indicator_name,
+                        "value": val_str,
+                        "value_date": date_str
+                    }})
+                            
+    except Exception as e:
+        print(f"BDH Error: {{e}}")
+
+    print("JSON_START")
+    print(json.dumps(res_list))
+    print("JSON_END")
+
+get_series()
+"""
+        return self._execute_and_parse(code)
+
+    def _fetch_price_by_dates_remote(self, company_code, currency, dates):
+        """Generates code to fetch price/mkt_cap for specific dates"""
+        if not dates:
+            return []
+            
+        dates_json = json.dumps(dates)
+        config_json = json.dumps(PRICE_CONFIG)
+        
+        bdh_options = {
+            'currency': currency,
+            'nonTradingDayFillOption': 'ALL_CALENDAR_DAYS',
+            'nonTradingDayFillMethod': 'PREVIOUS_VALUE'
+        }
+        bdh_opts_json = json.dumps(bdh_options)
+        
+        code = f"""
+def get_price_by_dates():
+    company = "{company_code}"
+    curr = "{currency}"
+    dates = {dates_json}
+    config = {config_json}
+    
+    mnemonic_map = {{v.upper(): k for k, v in config.items()}}
+    fields = list(mnemonic_map.keys())
+    
+    res_list = []
+    
+    for d_str in dates:
+        # d_str is 'YYYY-MM-DD', bdh needs 'YYYYMMDD'
+        d_param = d_str.replace('-', '')
+        
+        try:
+            df = bquery.bdh(
+                [company], 
+                fields, 
+                start_date=d_param, 
+                end_date=d_param, 
+                options={bdh_opts_json}
+            )
+            
+            if not df.empty:
+                for _, row in df.iterrows():
+                    # value_date is d_str
+                    
+                    for mnemonic, indicator_name in mnemonic_map.items():
+                        col_name = mnemonic
+                        # bdh columns might be tuple or just string depending on request
+                        # usually 'PX_LAST'
+                        
+                        val = None
+                        if col_name in df.columns:
+                            val = row[col_name]
+                        
+                        if pd.isna(val) or val is None:
+                            continue
+                            
+                        val_str = str(val)
+                        
+                        res_list.append({{
+                            "Company_code": company,
+                            "update_date": "{datetime.now().strftime('%Y-%m-%d')}",
+                            "currency": curr,
+                            "indicator": indicator_name,
+                            "value": val_str,
+                            "value_date": d_str 
+                        }})
+        except Exception as e:
+            print(f"Error fetching price for {{d_str}}: {{e}}")
+            
+    print("JSON_START")
+    print(json.dumps(res_list))
+    print("JSON_END")
+
+get_price_by_dates()
+"""
+        return self._execute_and_parse(code)
+
+    def _execute_and_parse(self, code):
+        """Execute code and parse [JSON_START]...[JSON_END]"""
+        raw_output_list = self.execute_remote_code(code)
+        raw_output = "".join(raw_output_list) if isinstance(raw_output_list, list) else str(raw_output_list)
+        # logger.debug(f"Remote execution returned {len(raw_output)} chars.") # Optional debug
+        
+        # Simple parser
+        try:
+            start_idx = raw_output.find("JSON_START")
+            end_idx = raw_output.find("JSON_END")
+            
+            if start_idx != -1 and end_idx != -1:
+                json_str = raw_output[start_idx + len("JSON_START"):end_idx].strip()
+                data = json.loads(json_str)
+                logger.info(f"✅ Parsed {len(data) if isinstance(data, list) else 1} items from remote.")
+                return data
+            else:
+                logger.warning(f"⚠️ No JSON output found in remote response.")
+                return []
+        except Exception as e:
+            logger.error(f"❌ Error parsing JSON from remote: {e}")
+            return []

backend/app/clients/ifind_client.py

@@ -0,0 +1,86 @@
+
+import requests
+import json
+import time
+import logging
+import sys
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+
+# Configure logger
+logger = logging.getLogger(__name__)
+
+class IFindClient:
+    """
+    iFinD HTTP API Client Base Class.
+    Handles authentication and token management.
+    """
+    def __init__(self, refresh_token: str):
+        self.refresh_token = refresh_token
+        self.access_token = None
+        self.token_expiry = 0
+        self.base_url = "https://quantapi.51ifind.com/api/v1"
+        
+        # Initialize Session with Retry logic
+        self.session = requests.Session()
+        retries = Retry(
+            total=5,
+            backoff_factor=1,
+            status_forcelist=[500, 502, 503, 504],
+            allowed_methods=["POST", "GET"]
+        )
+        self.session.mount('https://', HTTPAdapter(max_retries=retries))
+
+    def _get_access_token(self):
+        """Get or refresh access_token"""
+        if self.access_token and time.time() < self.token_expiry:
+            return self.access_token
+
+        url = f"{self.base_url}/get_access_token"
+        headers = {
+            "Content-Type": "application/json",
+            "refresh_token": self.refresh_token
+        }
+        
+        try:
+            response = self.session.post(url, headers=headers)
+            data = response.json()
+            if data.get("errorcode") == 0:
+                self.access_token = data["data"]["access_token"]
+                # Expires in 7 days, refresh every 6 days
+                self.token_expiry = time.time() + (6 * 24 * 3600)
+                return self.access_token
+            else:
+                error_msg = f"iFinD access token error: {data.get('errmsg')} (Code: {data.get('errorcode')})"
+                if data.get("errorcode") == -1301:
+                    logger.critical(f"CRITICAL ERROR: {error_msg}")
+                    logger.critical("Please check your IFIND_REFRESH_TOKEN in .env file.")
+                    # In backend service, we shouldn't sys.exit() as it kills the server
+                    # Just raise exception
+                    raise Exception(error_msg)
+                raise Exception(error_msg)
+        except Exception as e:
+            logger.error(f"Error refreshing iFinD access token: {e}")
+            return None
+
+    def post(self, endpoint: str, params: dict):
+        """Send POST request to API endpoint"""
+        token = self._get_access_token()
+        if not token:
+            return None
+        
+        url = f"{self.base_url}/{endpoint}"
+        headers = {
+            "Content-Type": "application/json",
+            "access_token": token
+        }
+        
+        try:
+            response = self.session.post(url, headers=headers, json=params, timeout=30)
+            if response.status_code != 200:
+                logger.error(f"iFinD API HTTP Error: {response.status_code} - {response.text}")
+                return None
+            return response.json()
+        except Exception as e:
+            logger.error(f"iFinD API Request Error on {endpoint}: {e}")
+            return None

backend/app/clients/ifind_hk_client.py

@@ -0,0 +1,758 @@
+
+import pandas as pd
+import time
+import os
+import psycopg2
+import numpy as np
+from typing import List, Union
+from dotenv import load_dotenv
+from pathlib import Path
+import logging
+from .ifind_client import IFindClient
+
+# Get logger
+logger = logging.getLogger(__name__)
+
+# Explicitly load .env from project root
+ROOT_DIR = Path(__file__).resolve().parent.parent.parent.parent
+load_dotenv(ROOT_DIR / ".env")
+
+class IFindHKClient:
+    """
+    iFinD Client specifically for Hong Kong Market (Backend Version).
+    Uses 'THS' indicators and Chinese accounting standard mappings.
+    Implements direct Postgres persistence (Side-Channel) for background task compatibility.
+    """
+    def __init__(self, api_key: str):
+        self.cli = IFindClient(refresh_token=api_key)
+        self.market = 'HK'
+        self._basic_info_cache = {}
+        self._active_years_cache = {}
+
+    def _get_db_connection(self):
+        """Create a database connection."""
+        db_host = os.getenv("DB_HOST", "192.168.3.195")
+        db_user = os.getenv("DB_USER", "value")
+        db_pass = os.getenv("DB_PASSWORD", "Value609!")
+        db_name = os.getenv("DB_NAME", "fa3")
+        db_port = os.getenv("DB_PORT", "5432")
+
+        try:
+            conn = psycopg2.connect(
+                host=db_host, user=db_user, password=db_pass, dbname=db_name, port=db_port
+            )
+            conn.set_client_encoding('UTF8')
+            return conn
+        except Exception as e:
+            logger.error(f"DB Connection Error: {e}")
+            return None
+
+    def _map_dtype_to_sql(self, dtype):
+        """Map pandas dtype to PostgreSQL type."""
+        if pd.api.types.is_integer_dtype(dtype):
+            return "BIGINT"
+        elif pd.api.types.is_float_dtype(dtype):
+            return "NUMERIC"
+        elif pd.api.types.is_bool_dtype(dtype):
+            return "BOOLEAN"
+        elif pd.api.types.is_datetime64_any_dtype(dtype):
+            return "TIMESTAMP"
+        else:
+            return "TEXT"
+
+    def _save_df_to_wide_table(self, table_name: str, df: pd.DataFrame, pk_cols: List[str]):
+        """
+        Save DataFrame to a specific wide table using direct psycopg2 connection.
+        Creates table if not exists using DF columns.
+        Performs incremental UPSERT.
+        """
+        if df is None or df.empty:
+            return
+
+        conn = self._get_db_connection()
+        if not conn: return
+
+        try:
+            # 1. Clean Data
+            df_clean = df.replace({np.nan: None})
+            
+            # Convert date columns to YYYY-MM-DD format if needed
+            for col in df_clean.columns:
+                if 'date' in col.lower() and df_clean[col].dtype == 'object':
+                    try:
+                        sample = df_clean[col].dropna().astype(str).iloc[0] if not df_clean[col].dropna().empty else ""
+                        if len(sample) == 8 and sample.isdigit():
+                             df_clean[col] = df_clean[col].astype(str).apply(
+                                 lambda x: f"{x[:4]}-{x[4:6]}-{x[6:]}" if x and len(str(x))==8 else x
+                             )
+                    except:
+                        pass
+
+            columns = list(df_clean.columns)
+            
+            with conn.cursor() as cur:
+                # 2. Check/Create Table
+                cur.execute("SELECT to_regclass(%s)", (f"public.{table_name}",))
+                table_exists = cur.fetchone()[0] is not None
+                
+                # Filter out valid columns for SQL
+                # Just use dataframe columns for initial creation
+                
+                if not table_exists:
+                    logger.info(f"🆕 [Database] Creating table {table_name}...")
+                    col_defs = ['"id" SERIAL PRIMARY KEY']
+                    for col in columns:
+                        sql_type = self._map_dtype_to_sql(df_clean[col].dtype)
+                        col_defs.append(f'"{col}" {sql_type}')
+                    
+                    col_defs.append('"update_date" TIMESTAMP DEFAULT CURRENT_TIMESTAMP')
+                    pk_str = ", ".join([f'"{c}"' for c in pk_cols])
+                    constraint_name = f"uq_{table_name}"
+                    
+                    create_sql = f"""
+                        CREATE TABLE IF NOT EXISTS {table_name} (
+                            {', '.join(col_defs)},
+                            CONSTRAINT {constraint_name} UNIQUE ({pk_str})
+                        );
+                    """
+                    cur.execute(create_sql)
+                    conn.commit()
+
+                # 3. Get existing columns to avoid insertion errors
+                cur.execute("""
+                    SELECT column_name 
+                    FROM information_schema.columns 
+                    WHERE table_name = %s
+                """, (table_name,))
+                db_cols = {row[0] for row in cur.fetchall()}
+                
+                # Check if 'id' column exists, if not add it (JIT schema evolution)
+                if 'id' not in db_cols:
+                    try:
+                        cur.execute(f'ALTER TABLE "{table_name}" ADD COLUMN "id" SERIAL')
+                        conn.commit()
+                        db_cols.add('id')
+                    except Exception as e:
+                        logger.error(f"Error adding id column to {table_name}: {e}")
+                        conn.rollback()
+                
+                valid_cols = [c for c in columns if c in db_cols]
+                if not valid_cols: return
+
+                # 4. Upsert
+                cols_str = ", ".join([f'"{c}"' for c in valid_cols])
+                vals_str = ", ".join(["%s"] * len(valid_cols))
+                updates = [f'"{c}" = EXCLUDED."{c}"' for c in valid_cols if c not in pk_cols]
+                if 'update_date' in db_cols:
+                     updates.append('"update_date" = CURRENT_TIMESTAMP')
+                update_clause = f"DO UPDATE SET {', '.join(updates)}" if updates else "DO NOTHING"
+                pk_conflict = ", ".join([f'"{c}"' for c in pk_cols])
+                
+                insert_sql = f"""
+                    INSERT INTO {table_name} ({cols_str})
+                    VALUES ({vals_str})
+                    ON CONFLICT ({pk_conflict})
+                    {update_clause}
+                """
+
+                data_tuples = [tuple(x) for x in df_clean[valid_cols].to_numpy()]
+                cur.executemany(insert_sql, data_tuples)
+                conn.commit()
+                # logger.info(f"✅ Saved {len(data_tuples)} rows to {table_name}")
+
+        except Exception as e:
+            logger.error(f"Error saving to {table_name}: {e}", exc_info=True)
+            conn.rollback()
+        finally:
+            conn.close()
+
+    def _parse_ifind_tables(self, res: dict, params: dict = None) -> pd.DataFrame:
+        if not res:
+            return pd.DataFrame()
+        
+        error_code = res.get("errorcode", 0)
+        if error_code != 0:
+            logger.error(f"iFinD API Error: {res.get('errmsg')} (code: {error_code})")
+            if params:
+                logger.error(f"Failed Params: {params}")
+            return pd.DataFrame()
+        
+        tables = res.get("tables", [])
+        if not tables:
+            return pd.DataFrame()
+        
+        table_info = tables[0]
+        table_data = table_info.get("table", {})
+        times = table_info.get("time", []) 
+        
+        if not table_data:
+            return pd.DataFrame()
+            
+        processed_table_data = {}
+        for k, v in table_data.items():
+            if not isinstance(v, list):
+                processed_table_data[k] = [v]
+            else:
+                processed_table_data[k] = v
+
+        df = pd.DataFrame(processed_table_data)
+        if times and len(times) == len(df):
+            df['end_date'] = [str(t).replace('-', '').replace('/', '').split(' ')[0] for t in times]
+        elif times and len(df) == 1:
+            df['end_date'] = str(times[0]).replace('-', '').replace('/', '').split(' ')[0]
+        
+        if 'end_date' not in df.columns:
+            for col in ['time', 'date', 'trade_date', 'REPORT_DATE']:
+                if col in df.columns:
+                    df['end_date'] = df[col].astype(str).str.replace('-', '').str.replace('/', '').str.split(' ').str[0]
+                    break
+        
+        return df
+
+    def _filter_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        if df.empty or 'end_date' not in df.columns:
+            return df
+        
+        df = df.sort_values(by='end_date', ascending=False)
+        df = df.drop_duplicates(subset=['end_date'], keep='first')
+        
+        if df.empty:
+            return df
+
+        latest_record = df.iloc[[0]]
+        try:
+            latest_date_str = str(latest_record['end_date'].values[0])
+            last_year_date_str = str(int(latest_date_str) - 10000)
+            comparable_record = df[df['end_date'].astype(str) == last_year_date_str]
+        except:
+            comparable_record = pd.DataFrame()
+
+        if comparable_record.empty:
+            dfs_to_concat = [latest_record, df]
+        else:
+            dfs_to_concat = [latest_record, comparable_record, df]
+
+        combined = pd.concat(dfs_to_concat)
+        combined = combined.drop_duplicates(subset=['end_date'])
+        combined = combined.sort_values(by='end_date', ascending=False)
+        return combined
+
+    def _fetch_basic_info(self, symbol: str, code: str) -> dict:
+        if code in self._basic_info_cache:
+            return self._basic_info_cache[code]
+
+        params = {
+            "codes": code,
+            "indipara": [
+                {"indicator": "corp_cn_name", "indiparams": []},
+                {"indicator": "accounting_date", "indiparams": []},
+                {"indicator": "ipo_date", "indiparams": []}
+            ]
+        }
+        res = self.cli.post("basic_data_service", params)
+        df = self._parse_ifind_tables(res, params)
+        if not df.empty:
+            df['code'] = code # Inject ID
+            # In backend, we skip file saving and rely on DB
+            # self._save_raw_data(df, symbol, "basic_info_raw") 
+            self._save_df_to_wide_table("ifind_hk_stock_basic", df, ["code"])
+        
+        info = {
+            "name": "",
+            "accounting_date": "1231",
+            "ipo_date": ""
+        }
+        
+        if not df.empty:
+            row = df.iloc[0]
+            info["name"] = str(row.get("corp_cn_name", ""))
+            info["acc_date"] = str(row.get("accounting_date", "1231"))
+            info["accounting_date"] = "1231"
+            info["ipo_date"] = str(row.get("ipo_date", "")).replace("-", "").replace("/", "")
+
+        self._basic_info_cache[code] = info
+        return info
+
+    def _fetch_financial_data_annual(self, symbol: str, code: str, indicator_configs: list) -> pd.DataFrame:
+        current_year = int(time.strftime("%Y"))
+        
+        last_valid_year = None
+        for offset in range(3):
+            test_year = current_year - offset
+            test_date = f"{test_year}1231"
+            first_indicator = indicator_configs[0]
+            params = {
+                "codes": code,
+                "indipara": [
+                    {"indicator": first_indicator["indicator"], "indiparams": [test_date, first_indicator.get("type", "1"), "CNY"]}
+                ]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty:
+                valid_val = df.iloc[0, 0] if not df.empty and df.shape[1] > 0 else None
+                if pd.notna(valid_val) and valid_val != 0:
+                    last_valid_year = test_year
+                    break
+        
+        if last_valid_year is None:
+            last_valid_year = current_year
+
+        all_dfs = []
+        for i in range(5):
+            target_year = last_valid_year - i
+            target_date = f"{target_year}1231"
+            
+            params = {
+                "codes": code,
+                "indipara": [
+                    {"indicator": item["indicator"], "indiparams": [target_date, item.get("type", "1"), "CNY"]}
+                    for item in indicator_configs
+                ]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res, params)
+            
+            if not df.empty:
+                valid_cols = [c for c in df.columns if c not in ['end_date', 'date']]
+                if not df[valid_cols].isnull().all().all():
+                    df['end_date'] = target_date
+                    all_dfs.append(df)
+        
+        if not all_dfs:
+            return pd.DataFrame()
+        
+        # Cache the actual years found in financial statements
+        try:
+            years = sorted([int(str(d)[:4]) for d in pd.concat(all_dfs)['end_date'].unique() if str(d)[:4].isdigit()])
+            if years:
+                self._active_years_cache[code] = years
+        except:
+            pass
+            
+        return pd.concat(all_dfs, ignore_index=True)
+
+    def get_income_statement(self, symbol: str, code: str) -> pd.DataFrame:
+        indicators = [
+            {"indicator": "total_oi"},
+            {"indicator": "prime_oi"},
+            {"indicator": "other_oi"},
+            {"indicator": "operating_cost"},
+            {"indicator": "operating_expense"},
+            {"indicator": "operating_fee"},
+            {"indicator": "p_depreciation_and_amortization"},
+            {"indicator": "gross_profit"},
+            {"indicator": "sales_ad_and_ga"},
+            {"indicator": "rad_cost"},
+            {"indicator": "sales_fee"},
+            {"indicator": "financial_expense"},
+            {"indicator": "sales_income"},
+            {"indicator": "sales_cost"},
+            {"indicator": "other_income"},
+            {"indicator": "manage_fee"},
+            {"indicator": "deprec_and_amorti"},
+            {"indicator": "total_other_opearting_expense"},
+            {"indicator": "p_total_cost"},
+            {"indicator": "operating_profit"},
+            {"indicator": "total_gal"},
+            {"indicator": "interest_income"},
+            {"indicator": "interest_net_pay"},
+            {"indicator": "interest_expense"},
+            {"indicator": "income_from_asso_and_joint"},
+            {"indicator": "other_gal_effct_profit_pre_tax"},
+            {"indicator": "conti_op_before_tax"},
+            {"indicator": "profit_before_noncurrent_items"},
+            {"indicator": "profit_and_loss_of_noncurrent_items"},
+            {"indicator": "profit_before_tax"},
+            {"indicator": "income_tax"},
+            {"indicator": "profit_after_tax"},
+            {"indicator": "minoritygal"},
+            {"indicator": "continue_operate_net_profit"},
+            {"indicator": "noncontinue_operate_net_profit"},
+            {"indicator": "other_special_items"},
+            {"indicator": "ni_attr_to_cs"},
+            {"indicator": "np_atms"},
+            {"indicator": "preferred_divid_and_other_adjust"},
+            {"indicator": "oci"},
+            {"indicator": "total_oci"},
+            {"indicator": "oci_from_parent"},
+            {"indicator": "oci_from_minority"},
+            {"indicator": "invest_property_fv_chg"},
+            {"indicator": "operating_amt"},
+            {"indicator": "oi_si"},
+            {"indicator": "operating_premium_profit_si"},
+            {"indicator": "to_toallied_corp_perf"},
+            {"indicator": "to_joint_control_entity_perf"},
+            {"indicator": "pre_tax_profit_si"},
+            {"indicator": "after_tax_profit_si"},
+            {"indicator": "profit_attrbt_to_nonholders"},
+            {"indicator": "total_income_atncs"}
+        ]
+        
+        df = self._fetch_financial_data_annual(symbol, code, indicators)
+        if df.empty: return df
+        # Backend assumption: no manual raw file saving.
+        
+        # Save Wide Table
+        df['code'] = code # Ensure Primary Key part 1
+        df['symbol'] = symbol 
+        # Primary Key: code + end_date
+        self._save_df_to_wide_table("ifind_hk_income_statement", df, ["code", "end_date"])
+
+        rename_map = {
+            'total_oi': 'revenue',
+            'operating_amt': 'turnover', 
+            'gross_profit': 'gross_profit',
+            'sales_ad_and_ga': 'sga_exp',
+            'sales_fee': 'selling_marketing_exp',
+            'manage_fee': 'ga_exp',
+            'rad_cost': 'rd_exp',
+            'income_tax': 'income_tax',
+            'ni_attr_to_cs': 'net_income',
+            'operating_profit': 'operating_profit',
+            'depreciation': 'depreciation',
+            'deprec_and_amorti': 'depreciation',
+            'p_depreciation_and_amortization': 'depreciation'
+        }
+        
+        df_filtered = df.rename(columns=rename_map)
+        df_filtered = df_filtered.loc[:, ~df_filtered.columns.duplicated()]
+        
+        if 'ebit' not in df_filtered.columns and 'operating_profit' in df_filtered.columns:
+            df_filtered['ebit'] = df_filtered['operating_profit']
+
+        for col in df_filtered.columns:
+            if col not in ['date', 'end_date', 'code', 'symbol']:
+                df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
+        
+        return self._filter_data(df_filtered)
+
+    def get_balance_sheet(self, symbol: str, code: str) -> pd.DataFrame:
+        indicators = [
+            {"indicator": "cce"},
+            {"indicator": "st_investment"},
+            {"indicator": "total_cash"},
+            {"indicator": "account_receivable"},
+            {"indicator": "tradable_fnncl_asset"},
+            {"indicator": "derivative_fnncl_assets"},
+            {"indicator": "restriv_fund"},
+            {"indicator": "other_short_term_investment"},
+            {"indicator": "ar_nr"},
+            {"indicator": "total_ar"},
+            {"indicator": "or"},
+            {"indicator": "inventory"},
+            {"indicator": "flow_assets_dit"},
+            {"indicator": "pre_payment"},
+            {"indicator": "other_cunrrent_assets_si"},
+            {"indicator": "other_ca"},
+            {"indicator": "total_ca"},
+            {"indicator": "receivables_from_allied_corp"},
+            {"indicator": "current_assets_si"},
+            {"indicator": "prepay_deposits_etc"},
+            {"indicator": "receivables_from_jce"},
+            {"indicator": "receivables_from_ac"},
+            {"indicator": "recoverable_tax"},
+            {"indicator": "total_fixed_assets"},
+            {"indicator": "depreciation"},
+            {"indicator": "equity_and_lt_invest"},
+            {"indicator": "net_fixed_assets"},
+            {"indicator": "invest_property"},
+            {"indicator": "equity_investment"},
+            {"indicator": "investment_in_associate"},
+            {"indicator": "investment_in_joints"},
+            {"indicator": "held_to_maturity_invest"},
+            {"indicator": "goodwill_and_intangible_asset"},
+            {"indicator": "intangible_assets"},
+            {"indicator": "accum_amortized"},
+            {"indicator": "noncurrent_assets_dit"},
+            {"indicator": "other_noncurrent_assets_si"},
+            {"indicator": "dt_assets"},
+            {"indicator": "total_noncurrent_assets"},
+            {"indicator": "total_assets"},
+            {"indicator": "ac_equity"},
+            {"indicator": "lease_prepay"},
+            {"indicator": "noncurrent_assets_si"},
+            {"indicator": "st_lt_current_loan"},
+            {"indicator": "trade_financial_lia"},
+            {"indicator": "derivative_financial_lia"},
+            {"indicator": "ap_np"},
+            {"indicator": "accounts_payable"},
+            {"indicator": "advance_payment"},
+            {"indicator": "st_debt"},
+            {"indicator": "contra_liab"},
+            {"indicator": "tax_payable"},
+            {"indicator": "accrued_liab"},
+            {"indicator": "flow_debt_deferred_income"},
+            {"indicator": "other_cl"},
+            {"indicator": "other_cunrrent_liab_si"},
+            {"indicator": "total_cl"},
+            {"indicator": "accrued_expenses_etc"},
+            {"indicator": "money_payable_toac"},
+            {"indicator": "joint_control_entity_payable"},
+            {"indicator": "payable_to_associated_corp"},
+            {"indicator": "lt_debt"},
+            {"indicator": "long_term_loan"},
+            {"indicator": "other_noncurrent_liabi"},
+            {"indicator": "deferred_tax_liability"},
+            {"indicator": "ncl_deferred_income"},
+            {"indicator": "other_noncurrent_liab_si"},
+            {"indicator": "noncurrent_liab_si"},
+            {"indicator": "total_noncurrent_liab"},
+            {"indicator": "total_liab"},
+            {"indicator": "common_shares"},
+            {"indicator": "capital_reserve"},
+            {"indicator": "equity_premium"},
+            {"indicator": "treasury_stock"},
+            {"indicator": "accumgal"},
+            {"indicator": "equity_atsopc_sbi"},
+            {"indicator": "preferred_stock"},
+            {"indicator": "perpetual_debt"},
+            {"indicator": "reserve"},
+            {"indicator": "other_reserves"},
+            {"indicator": "retained_earnings"},
+            {"indicator": "oci_bs"},
+            {"indicator": "total_common_equity"},
+            {"indicator": "equity_belong_to_parent"},
+            {"indicator": "minority_interests"},
+            {"indicator": "other_equity_si"},
+            {"indicator": "total_equity"},
+            {"indicator": "total_lib_and_equity"},
+            {"indicator": "equity_si"},
+            {"indicator": "equity_atncs"}
+        ]
+        
+        df = self._fetch_financial_data_annual(symbol, code, indicators)
+        if df.empty: return df
+
+        df['code'] = code 
+        df['symbol'] = symbol
+        self._save_df_to_wide_table("ifind_hk_balance_sheet", df, ["code", "end_date"])
+
+        rename_map = {
+            'cce': 'cash',
+            'ar_nr': 'receivables',
+            'inventory': 'inventory',
+            'net_fixed_assets': 'fixed_assets',
+            'equity_and_lt_invest': 'long_term_investments',
+            'goodwill_and_intangible_asset': 'goodwill',
+            'st_debt': 'short_term_debt',
+            'st_lt_current_loan': 'short_term_borrowings',
+            'ap_np': 'accounts_payable',
+            'contra_liab': 'contract_liabilities',
+            'advance_payment': 'advances_from_customers',
+            'flow_debt_deferred_income': 'deferred_revenue',
+            'lt_debt': 'long_term_debt',
+            'long_term_loan': 'long_term_borrowings',
+            'total_assets': 'total_assets',
+            'equity_belong_to_parent': 'total_equity',
+            'pre_payment': 'prepayment'
+        }
+        
+        df_filtered = df.rename(columns=rename_map)
+        df_filtered = df_filtered.loc[:, ~df_filtered.columns.duplicated()]
+
+        if 'total_liabilities' not in df_filtered.columns or df_filtered['total_liabilities'].isnull().all():
+            if 'total_liab' in df_filtered.columns:
+                 df_filtered['total_liabilities'] = df_filtered['total_liab']
+            elif 'total_assets' in df_filtered.columns and 'total_equity' in df_filtered.columns:
+                df_filtered['total_liabilities'] = df_filtered['total_assets'] - df_filtered['total_equity']
+                
+        df_filtered = df_filtered.loc[:, ~df_filtered.columns.duplicated()]
+
+        for col in df_filtered.columns:
+            if col not in ['date', 'end_date', 'code', 'symbol']:
+                df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
+                
+        return self._filter_data(df_filtered)
+
+    def get_cash_flow(self, symbol: str, code: str) -> pd.DataFrame:
+        indicators = [
+            {"indicator": "ni"},
+            {"indicator": "depreciation_and_amortization"},
+            {"indicator": "operating_capital_change"},
+            {"indicator": "ncf_from_oa"},
+            {"indicator": "capital_cost"},
+            {"indicator": "invest_buy"},
+            {"indicator": "ncf_from_ia"},
+            {"indicator": "increase_in_share_capital"},
+            {"indicator": "decrease_in_share_capital"},
+            {"indicator": "total_dividends_paid"},
+            {"indicator": "ncf_from_fa"}
+        ]
+        
+        df = self._fetch_financial_data_annual(symbol, code, indicators)
+        if df.empty: return df
+
+        df['code'] = code 
+        df['symbol'] = symbol
+        self._save_df_to_wide_table("ifind_hk_cash_flow", df, ["code", "end_date"])
+
+        rename_map = {
+            'ncf_from_oa': 'ocf',
+            'capital_cost': 'capex',
+            'total_dividends_paid': 'dividends'
+        }
+        
+        df_filtered = df.rename(columns=rename_map)
+        df_filtered = df_filtered.loc[:, ~df_filtered.columns.duplicated()]
+        for col in df_filtered.columns:
+            if col not in ['date', 'end_date', 'code', 'symbol']:
+                df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
+        
+        if 'capex' in df_filtered.columns:
+            df_filtered['capex'] = df_filtered['capex'].abs()
+            
+        return self._filter_data(df_filtered)
+
+    def get_market_metrics(self, symbol: str, code: str) -> dict:
+        basic_info = self._fetch_basic_info(symbol, code)
+        
+        metrics = {
+            "name": basic_info.get("name", ""),
+            "list_date": basic_info.get("ipo_date", ""),
+            "accounting_date": basic_info.get("accounting_date", ""),
+            "price": 0,
+            "market_cap": 0,
+            "pe": 0,
+            "pb": 0,
+            "dividend_yield": 0
+        }
+        
+        # In backend logic we cannot easily read file.
+        # But we can query DB for income statement dates!
+        # Or just fallback to 'active_years' logic.
+        # For simplicity in migration, let's use the DB query if possible, or just the fallback logic.
+        
+        report_dates = []
+        # Try to get dates from active_years_cache first (populated by get_income_statement call in flow)
+        if code in self._active_years_cache:
+            years = self._active_years_cache[code]
+            report_dates = [f"{y}1231" for y in years]
+        else:
+             # Fallback
+            current_year = int(time.strftime("%Y"))
+            report_dates = [f"{current_year - i}1231" for i in range(5)]
+        
+        # Fetch daily_basic data for each report date
+        all_daily_basic = []
+        for date_str in report_dates:
+            if len(str(date_str)) == 8:
+                formatted_date = f"{str(date_str)[:4]}-{str(date_str)[4:6]}-{str(date_str)[6:]}"
+            else:
+                formatted_date = str(date_str)
+            
+            params = {
+                "codes": code,
+                "functionpara": {"date_sequence": formatted_date},
+                "startdate": "",
+                "enddate": "",
+                "indipara": [
+                    {"indicator": "close_price", "indiparams": ["", "0", "BB"]},
+                    {"indicator": "market_value", "indiparams": ["", "BB"]},
+                    {"indicator": "pb_latest", "indiparams": ["", "100"]},
+                    {"indicator": "dividend_yield_ttm_ex_sd", "indiparams": [""]},
+                    {"indicator": "pe_ttm", "indiparams": ["", "100"]}
+                ]
+            }
+            res = self.cli.post("date_sequence", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty:
+                df['code'] = code
+                df['end_date'] = str(date_str).replace('-', '')
+                all_daily_basic.append(df)
+        
+        # Save to DB
+        if all_daily_basic:
+            combined_df = pd.concat(all_daily_basic, ignore_index=True)
+            self._save_df_to_wide_table("ifind_hk_daily_basic", combined_df, ["code", "end_date"])
+            
+            # Use the first (most recent) data for metrics dict
+            latest_df = all_daily_basic[0]
+            if not latest_df.empty:
+                row = latest_df.iloc[0]
+                metrics["price"] = float(row.get("close_price") or 0)
+                metrics["market_cap"] = float(row.get("market_value") or 0)
+                metrics["pe"] = float(row.get("pe_ttm") or 0)
+                metrics["pb"] = float(row.get("pb_latest") or 0)
+                metrics["dividend_yield"] = float(row.get("dividend_yield_ttm_ex_sd") or 0)
+            
+        return metrics
+
+    def get_dividends(self, symbol: str, code: str) -> pd.DataFrame:
+        if code in self._active_years_cache:
+            years_to_fetch = sorted(self._active_years_cache[code], reverse=True)
+        else:
+            current_year = int(time.strftime("%Y"))
+            years_to_fetch = [current_year - i for i in range(5)]
+        
+        results = []
+
+        for year_int in years_to_fetch:
+            year_str = str(year_int)
+            params = {
+                "codes": code,
+                "indipara": [
+                    {"indicator": "annual_cum_dividend", "indiparams": [year_str, "CNY"]}
+                ]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty and 'annual_cum_dividend' in df.columns:
+                df['code'] = code
+                end_date = f"{year_str}1231"
+                df['end_date'] = end_date
+                self._save_df_to_wide_table("ifind_hk_dividend", df, ["code", "end_date"])
+
+                val = df['annual_cum_dividend'].iloc[0]
+                if pd.notna(val) and val != 0:
+                    results.append({
+                        'date_str': end_date,
+                        'dividends': float(val)
+                    })
+        
+        if not results:
+            return pd.DataFrame()
+            
+        df_div = pd.DataFrame(results)
+        return df_div
+
+    def get_repurchases(self, symbol: str, code: str) -> pd.DataFrame:
+        if code in self._active_years_cache:
+            years_to_fetch = sorted(self._active_years_cache[code], reverse=True)
+        else:
+            current_year = int(time.strftime("%Y"))
+            years_to_fetch = [current_year - i for i in range(5)]
+        
+        results = []
+
+        for target_year in years_to_fetch:
+            start_date = f"{target_year - 1}-12-31"
+            end_date = f"{target_year}-12-31"
+            
+            params = {
+                "codes": code,
+                "indipara": [
+                    {"indicator": "repurchase_volume_interval", "indiparams": [start_date, end_date]},
+                    {"indicator": "repurchase_amount_interval", "indiparams": [start_date, end_date]},
+                    {"indicator": "repurchase_low_price", "indiparams": [start_date, end_date]},
+                    {"indicator": "repurchase_high_price", "indiparams": [start_date, end_date]}
+                ]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty:
+                 valid_row = False
+                 for col in df.columns:
+                     val = df[col].iloc[0]
+                     if pd.notna(val) and val != 0:
+                         valid_row = True
+                         break
+                 
+                 if valid_row:
+                    df['code'] = code
+                    df['end_date'] = end_date.replace('-', '')
+                    self._save_df_to_wide_table("ifind_hk_repurchase", df, ["code", "end_date"])

backend/app/clients/ifind_int_client.py

@@ -0,0 +1,755 @@
+
+import pandas as pd
+import time
+import os
+import psycopg2
+import numpy as np
+from typing import List, Union
+from dotenv import load_dotenv
+from pathlib import Path
+import logging
+from .ifind_client import IFindClient
+
+# Get logger
+logger = logging.getLogger(__name__)
+
+# Explicitly load .env from project root
+ROOT_DIR = Path(__file__).resolve().parent.parent.parent.parent
+load_dotenv(ROOT_DIR / ".env")
+
+class IFindIntClient:
+    """
+    Generic iFinD Client for International Markets (JP, VN, US) - Backend Version.
+    Uses 'OAS' (Original Accounting Standards) standardized indicators.
+    Implements generic wide-table persistence (Side-Channel) for background task compatibility.
+    """
+    def __init__(self, api_key: str, market: str):
+        self.cli = IFindClient(refresh_token=api_key)
+        self.market = market
+        self._basic_info_cache = {}
+        self._active_years_cache = {} 
+
+    def _get_db_connection(self):
+        """Create a database connection."""
+        db_host = os.getenv("DB_HOST", "192.168.3.195")
+        db_user = os.getenv("DB_USER", "value")
+        db_pass = os.getenv("DB_PASSWORD", "Value609!")
+        db_name = os.getenv("DB_NAME", "fa3")
+        db_port = os.getenv("DB_PORT", "5432")
+
+        try:
+            conn = psycopg2.connect(
+                host=db_host, user=db_user, password=db_pass, dbname=db_name, port=db_port
+            )
+            conn.set_client_encoding('UTF8')
+            return conn
+        except Exception as e:
+            logger.error(f"DB Connection Error: {e}")
+            return None
+
+    def _map_dtype_to_sql(self, dtype):
+        """Map pandas dtype to PostgreSQL type."""
+        if pd.api.types.is_integer_dtype(dtype):
+            return "BIGINT"
+        elif pd.api.types.is_float_dtype(dtype):
+            return "NUMERIC"
+        elif pd.api.types.is_bool_dtype(dtype):
+            return "BOOLEAN"
+        elif pd.api.types.is_datetime64_any_dtype(dtype):
+            return "TIMESTAMP"
+        else:
+            return "TEXT"
+
+    def _save_df_to_wide_table(self, table_name: str, df: pd.DataFrame, pk_cols: List[str]):
+        """
+        Save DataFrame to a specific wide table using direct psycopg2 connection.
+        Creates table if not exists using DF columns.
+        Performs incremental UPSERT.
+        """
+        if df is None or df.empty:
+            return
+
+        conn = self._get_db_connection()
+        if not conn: return
+
+        try:
+            # 1. Clean Data
+            df_clean = df.replace({np.nan: None})
+            
+            # Convert date columns
+            for col in df_clean.columns:
+                if 'date' in col.lower() and df_clean[col].dtype == 'object':
+                    try:
+                        sample = df_clean[col].dropna().astype(str).iloc[0] if not df_clean[col].dropna().empty else ""
+                        if len(sample) == 8 and sample.isdigit():
+                             df_clean[col] = df_clean[col].astype(str).apply(
+                                 lambda x: f"{x[:4]}-{x[4:6]}-{x[6:]}" if x and len(str(x))==8 else x
+                             )
+                    except:
+                        pass
+
+            columns = list(df_clean.columns)
+            
+            with conn.cursor() as cur:
+                # 2. Check/Create Table
+                cur.execute("SELECT to_regclass(%s)", (f"public.{table_name}",))
+                table_exists = cur.fetchone()[0] is not None
+
+                if not table_exists:
+                    logger.info(f"🆕 [Database] Creating table {table_name}...")
+                    col_defs = ['"id" SERIAL PRIMARY KEY']
+                    for col in columns:
+                        sql_type = self._map_dtype_to_sql(df_clean[col].dtype)
+                        col_defs.append(f'"{col}" {sql_type}')
+                    
+                    col_defs.append('"update_date" TIMESTAMP DEFAULT CURRENT_TIMESTAMP')
+                    pk_str = ", ".join([f'"{c}"' for c in pk_cols])
+                    constraint_name = f"uq_{table_name}"
+                    
+                    create_sql = f"""
+                        CREATE TABLE IF NOT EXISTS {table_name} (
+                            {', '.join(col_defs)},
+                            CONSTRAINT {constraint_name} UNIQUE ({pk_str})
+                        );
+                    """
+                    cur.execute(create_sql)
+                    conn.commit()
+
+                # 3. Get existing columns definition
+                cur.execute("""
+                    SELECT column_name 
+                    FROM information_schema.columns 
+                    WHERE table_name = %s
+                """, (table_name,))
+                db_cols = {row[0] for row in cur.fetchall()}
+
+                # Check if 'id' column exists, if not add it
+                if 'id' not in db_cols:
+                    try:
+                        cur.execute(f'ALTER TABLE "{table_name}" ADD COLUMN "id" SERIAL')
+                        conn.commit()
+                        db_cols.add('id')
+                    except Exception as e:
+                        logger.error(f"Error adding id column to {table_name}: {e}")
+                        conn.rollback()
+                
+                valid_cols = [c for c in columns if c in db_cols]
+                if not valid_cols: return
+
+                # 4. Upsert
+                cols_str = ", ".join([f'"{c}"' for c in valid_cols])
+                vals_str = ", ".join(["%s"] * len(valid_cols))
+                updates = [f'"{c}" = EXCLUDED."{c}"' for c in valid_cols if c not in pk_cols]
+                if 'update_date' in db_cols:
+                     updates.append('"update_date" = CURRENT_TIMESTAMP')
+                update_clause = f"DO UPDATE SET {', '.join(updates)}" if updates else "DO NOTHING"
+                pk_conflict = ", ".join([f'"{c}"' for c in pk_cols])
+                
+                insert_sql = f"""
+                    INSERT INTO {table_name} ({cols_str})
+                    VALUES ({vals_str})
+                    ON CONFLICT ({pk_conflict})
+                    {update_clause}
+                """
+
+                data_tuples = [tuple(x) for x in df_clean[valid_cols].to_numpy()]
+                cur.executemany(insert_sql, data_tuples)
+                conn.commit()
+
+        except Exception as e:
+            logger.error(f"Error saving to {table_name}: {e}", exc_info=True)
+            conn.rollback()
+        finally:
+            conn.close()
+
+    def _parse_ifind_tables(self, res: dict) -> pd.DataFrame:
+        if not res:
+            return pd.DataFrame()
+        
+        if res.get("errorcode") != 0:
+            logger.error(f"iFinD API Error: {res.get('errmsg')} (code: {res.get('errorcode')})")
+            return pd.DataFrame()
+        
+        tables = res.get("tables", [])
+        if not tables:
+            return pd.DataFrame()
+        
+        table_info = tables[0]
+        table_data = table_info.get("table", {})
+        times = table_info.get("time", []) 
+        
+        if not table_data:
+            return pd.DataFrame()
+            
+        processed_table_data = {}
+        for k, v in table_data.items():
+            if not isinstance(v, list):
+                processed_table_data[k] = [v]
+            else:
+                processed_table_data[k] = v
+
+        df = pd.DataFrame(processed_table_data)
+        if times and len(times) == len(df):
+            df['end_date'] = [str(t).replace('-', '').replace('/', '').split(' ')[0] for t in times]
+        elif times and len(df) == 1:
+            df['end_date'] = str(times[0]).replace('-', '').replace('/', '').split(' ')[0]
+        
+        if 'end_date' not in df.columns:
+            for col in ['time', 'date', 'trade_date', 'REPORT_DATE']:
+                if col in df.columns:
+                    df['end_date'] = df[col].astype(str).str.replace('-', '').str.replace('/', '').str.split(' ').str[0]
+                    break
+        
+        return df
+
+    def _filter_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        if df.empty or 'end_date' not in df.columns:
+            return df
+        
+        df = df.sort_values(by='end_date', ascending=False)
+        df = df.drop_duplicates(subset=['end_date'], keep='first')
+        
+        if df.empty:
+            return df
+
+        latest_record = df.iloc[[0]]
+        try:
+            latest_date_str = str(latest_record['end_date'].values[0])
+            last_year_date_str = str(int(latest_date_str) - 10000)
+            comparable_record = df[df['end_date'].astype(str) == last_year_date_str]
+        except:
+            comparable_record = pd.DataFrame()
+
+        is_annual = df['end_date'].astype(str).str.endswith('1231') | df['end_date'].astype(str).str.endswith('0331')
+        annual_records = df[is_annual]
+        
+        combined = pd.concat([latest_record, comparable_record, annual_records])
+        combined = combined.drop_duplicates(subset=['end_date'])
+        combined = combined.sort_values(by='end_date', ascending=False)
+        return combined
+
+    def _fetch_basic_info(self, symbol: str, code: str) -> dict:
+        if code in self._basic_info_cache:
+            return self._basic_info_cache[code]
+
+        params = {
+            "codes": code,
+            "indipara": [
+                {"indicator": "corp_cn_name", "indiparams": []},
+                {"indicator": "accounting_date", "indiparams": []},
+                {"indicator": "ipo_date", "indiparams": []}
+            ]
+        }
+        res = self.cli.post("basic_data_service", params)
+        df = self._parse_ifind_tables(res)
+        if not df.empty:
+            df['code'] = code # Inject ID
+            # self._save_raw_data(df, symbol, "basic_info_raw")
+            # Usually generic international doesn't save stock_basic table, but we can if needed.
+            # Keeping consistent with original logic which only saved raw file.
+            pass
+        
+        info = {
+            "name": "",
+            "accounting_date": "1231", 
+            "ipo_date": ""
+        }
+        
+        if not df.empty:
+            row = df.iloc[0]
+            info["name"] = str(row.get("corp_cn_name", ""))
+            acc_date = str(row.get("accounting_date", "1231")).replace("-", "").replace("/", "")
+            if acc_date:
+                 info["accounting_date"] = acc_date
+            info["ipo_date"] = str(row.get("ipo_date", "")).replace("-", "").replace("/", "")
+
+        self._basic_info_cache[code] = info
+        return info
+
+    def _fetch_financial_data_annual(self, symbol: str, code: str, indicator_configs: list) -> pd.DataFrame:
+        basic_info = self._fetch_basic_info(symbol, code)
+        acc_date = basic_info.get("accounting_date", "1231")
+        
+        current_year = int(time.strftime("%Y"))
+        last_valid_year = None
+        
+        # 1. Determine most recent valid year
+        for offset in range(3):
+            test_year = current_year - offset
+            test_date = f"{test_year}{acc_date}"
+            first_indicator = indicator_configs[0]
+            params = {
+                "codes": code,
+                "indipara": [
+                    {"indicator": first_indicator["indicator"], "indiparams": [test_date, first_indicator.get("type", "1"), "CNY"]}
+                ]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty:
+                valid_val = df.iloc[0, 0] if not df.empty and df.shape[1] > 0 else None
+                if pd.notna(valid_val) and valid_val != 0:
+                    last_valid_year = test_year
+                    break
+        
+        if last_valid_year is None:
+            last_valid_year = current_year
+
+        all_dfs = []
+        for i in range(5):
+            target_year = last_valid_year - i
+            target_date = f"{target_year}{acc_date}"
+            
+            params = {
+                "codes": code,
+                "indipara": [
+                    {"indicator": item["indicator"], "indiparams": [target_date, item.get("type", "1"), "CNY"]}
+                    for item in indicator_configs
+                ]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty:
+                df['end_date'] = target_date
+                all_dfs.append(df)
+        
+        # Filter and concat
+        all_dfs = [d for d in all_dfs if not d.empty and not d.isna().all().all()]
+        
+        if not all_dfs:
+            return pd.DataFrame()
+        
+        # Cache the actual years found in financial statements
+        try:
+            years = sorted([int(str(d)[:4]) for d in pd.concat(all_dfs)['end_date'].unique() if str(d)[:4].isdigit()])
+            if years:
+                self._active_years_cache[code] = years
+        except Exception as e:
+            logger.error(f"Error caching years: {e}")
+
+        return pd.concat(all_dfs, ignore_index=True)
+
+    def get_income_statement(self, symbol: str, code: str) -> pd.DataFrame:
+        indicators = [
+            {"indicator": "revenue_oas"},
+            {"indicator": "gross_profit_oas"},
+            {"indicator": "sga_expenses_oas"},
+            {"indicator": "selling_marketing_expenses_oas"},
+            {"indicator": "ga_expenses_oas"},
+            {"indicator": "rd_expenses_oas"},
+            {"indicator": "income_tax_expense_oas"},
+            {"indicator": "net_income_attri_to_common_sh_oas"},
+            {"indicator": "operating_income_oas"}
+        ]
+        
+        df = self._fetch_financial_data_annual(symbol, code, indicators)
+        if df.empty: return df
+        # Backend assumption: no file save
+        
+        # DB Save
+        df['code'] = code
+        df['market'] = self.market # Inject Market
+        df['symbol'] = symbol
+        self._save_df_to_wide_table("ifind_int_income_statement", df, ["code", "market", "end_date"])
+
+        rename_map = {
+            'revenue_oas': 'revenue',
+            'gross_profit_oas': 'gross_profit',
+            'sga_expenses_oas': 'sga_exp',
+            'selling_marketing_expenses_oas': 'selling_marketing_exp',
+            'ga_expenses_oas': 'ga_exp',
+            'rd_expenses_oas': 'rd_exp',
+            'income_tax_expense_oas': 'income_tax',
+            'net_income_attri_to_common_sh_oas': 'net_income',
+            'operating_income_oas': 'operating_profit'
+        }
+        
+        df_filtered = df.rename(columns=rename_map)
+        df_filtered = df_filtered.loc[:, ~df_filtered.columns.duplicated()]
+        
+        for col in df_filtered.columns:
+            if col not in ['date', 'end_date', 'code', 'symbol', 'market']:
+                df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
+        
+        return self._filter_data(df_filtered)
+
+    def get_balance_sheet(self, symbol: str, code: str) -> pd.DataFrame:
+        indicators = [
+            {"indicator": "cash_equi_short_term_inve_oas"},
+            {"indicator": "accou_and_notes_recei_oas"},
+            {"indicator": "inventories_oas"},
+            {"indicator": "ppe_net_oas"},
+            {"indicator": "long_term_inv_and_receiv_oas"},
+            {"indicator": "goodwill_and_intasset_oas"},
+            {"indicator": "short_term_debt_oas"},
+            {"indicator": "short_term_borrowings_oas"},
+            {"indicator": "account_and_note_payable_oas"},
+            {"indicator": "contra_liabilities_current_oas"},
+            {"indicator": "advance_from_cust_current_oas"},
+            {"indicator": "defer_revenue_current_oas"},
+            {"indicator": "long_term_debt_oas"},
+            {"indicator": "long_term_borrowings_oas"},
+            {"indicator": "total_assets_oas"},
+            {"indicator": "equity_attri_to_companyowner_oas"},
+            {"indicator": "prepaid_expenses_current_oas"}
+        ]
+        
+        df = self._fetch_financial_data_annual(symbol, code, indicators)
+        if df.empty: return df
+
+        df['code'] = code
+        df['market'] = self.market
+        df['symbol'] = symbol
+        self._save_df_to_wide_table("ifind_int_balance_sheet", df, ["code", "market", "end_date"])
+
+        rename_map = {
+            'cash_equi_short_term_inve_oas': 'cash',
+            'accou_and_notes_recei_oas': 'receivables',
+            'inventories_oas': 'inventory',
+            'ppe_net_oas': 'fixed_assets',
+            'long_term_inv_and_receiv_oas': 'long_term_investments',
+            'goodwill_and_intasset_oas': 'goodwill',
+            'short_term_debt_oas': 'short_term_debt',
+            'short_term_borrowings_oas': 'short_term_borrowings',
+            'account_and_note_payable_oas': 'accounts_payable',
+            'contra_liabilities_current_oas': 'contract_liabilities',
+            'advance_from_cust_current_oas': 'advances_from_customers',
+            'defer_revenue_current_oas': 'deferred_revenue',
+            'long_term_debt_oas': 'long_term_debt',
+            'long_term_borrowings_oas': 'long_term_borrowings',
+            'total_assets_oas': 'total_assets',
+            'equity_attri_to_companyowner_oas': 'total_equity',
+            'prepaid_expenses_current_oas': 'prepayment'
+        }
+        
+        df_filtered = df.rename(columns=rename_map)
+        df_filtered = df_filtered.loc[:, ~df_filtered.columns.duplicated()]
+        
+        if 'total_liabilities' not in df_filtered.columns or df_filtered['total_liabilities'].isnull().all():
+            if 'total_assets' in df_filtered.columns and 'total_equity' in df_filtered.columns:
+                df_filtered['total_liabilities'] = df_filtered['total_assets'] - df_filtered['total_equity']
+ 
+        for col in df_filtered.columns:
+            if col not in ['date', 'end_date', 'code', 'symbol', 'market']:
+                df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
+                
+        return self._filter_data(df_filtered)
+
+    def get_cash_flow(self, symbol: str, code: str) -> pd.DataFrame:
+        indicators = [
+            {"indicator": "net_cash_flows_from_oa_oas"},
+            {"indicator": "purchase_of_ppe_and_ia_oas"},
+            {"indicator": "dividends_paid_oas"}
+        ]
+        
+        df = self._fetch_financial_data_annual(symbol, code, indicators)
+        if df.empty: return df
+
+        df['code'] = code
+        df['market'] = self.market
+        df['symbol'] = symbol
+        self._save_df_to_wide_table("ifind_int_cash_flow", df, ["code", "market", "end_date"])
+
+        rename_map = {
+            'net_cash_flows_from_oa_oas': 'ocf',
+            'purchase_of_ppe_and_ia_oas': 'capex',
+            'dividends_paid_oas': 'dividends'
+        }
+        
+        df_filtered = df.rename(columns=rename_map)
+        df_filtered = df_filtered.loc[:, ~df_filtered.columns.duplicated()]
+        
+        for col in df_filtered.columns:
+            if col not in ['date', 'end_date', 'code', 'symbol', 'market']:
+                df_filtered[col] = pd.to_numeric(df_filtered[col], errors='coerce')
+        
+        if 'capex' in df_filtered.columns:
+            df_filtered['capex'] = df_filtered['capex'].abs()
+            
+        return self._filter_data(df_filtered)
+
+    def get_market_metrics(self, symbol: str, code: str) -> dict:
+        basic_info = self._fetch_basic_info(symbol, code)
+        metrics = {
+            "name": basic_info.get("name", ""),
+            "list_date": basic_info.get("ipo_date", ""),
+            "price": 0,
+            "market_cap": 0,
+            "pe": 0,
+            "pb": 0,
+            "dividend_yield": 0
+        }
+        
+        # In backend, rely on DB query or fallbacks
+        report_dates = []
+        if code in self._active_years_cache:
+            years = self._active_years_cache[code]
+            report_dates = [f"{y}1231" for y in years]
+        else:
+            current_year = int(time.strftime("%Y"))
+            report_dates = [f"{current_year - i}1231" for i in range(5)]
+        
+        # Fetch daily_basic data for each report date
+        all_daily_basic = []
+        for date_str in report_dates:
+            if len(str(date_str)) == 8:
+                formatted_date = f"{str(date_str)[:4]}-{str(date_str)[4:6]}-{str(date_str)[6:]}"
+            else:
+                formatted_date = str(date_str)
+            
+            params = {
+                "codes": code,
+                "functionpara": {"date_sequence": formatted_date},
+                "startdate": "",
+                "enddate": "",
+                "indipara": [
+                    {"indicator": "close_price", "indiparams": ["", "0", "BB"]},
+                    {"indicator": "market_value", "indiparams": ["", "BB"]},
+                    {"indicator": "pb_latest", "indiparams": ["", "100"]},
+                    {"indicator": "dividend_yield_ttm_ex_sd", "indiparams": [""]},
+                    {"indicator": "pe_ttm", "indiparams": ["", "100"]}
+                ]
+            }
+            res = self.cli.post("date_sequence", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty:
+                df['code'] = code
+                df['market'] = self.market
+                df['end_date'] = str(date_str).replace('-', '')
+                all_daily_basic.append(df)
+        
+        # Save to DB
+        if all_daily_basic:
+            combined_df = pd.concat(all_daily_basic, ignore_index=True)
+            self._save_df_to_wide_table("ifind_int_daily_basic", combined_df, ["code", "market", "end_date"])
+            
+            # Use the first (most recent) data for metrics dict
+            latest_df = all_daily_basic[0]
+            if not latest_df.empty:
+                row = latest_df.iloc[0]
+                metrics["price"] = float(row.get("close_price") or 0)
+                metrics["market_cap"] = float(row.get("market_value") or 0)
+                metrics["pe"] = float(row.get("pe_ttm") or 0)
+                metrics["pb"] = float(row.get("pb_latest") or 0)
+                metrics["dividend_yield"] = float(row.get("dividend_yield_ttm_ex_sd") or 0)
+        
+        return metrics
+
+    def get_dividends(self, symbol: str, code: str) -> pd.DataFrame:
+        basic_info = self._fetch_basic_info(symbol, code)
+        acc_date = basic_info.get("accounting_date", "1231")
+        
+        if code in self._active_years_cache:
+            years_to_fetch = sorted(self._active_years_cache[code], reverse=True)
+        else:
+            current_year = int(time.strftime("%Y"))
+            years_to_fetch = [current_year - i for i in range(5)]
+        
+        results = []
+
+        for year in years_to_fetch:
+            year_str = str(year)
+            params = {
+                "codes": code,
+                "indipara": [
+                    {"indicator": "annual_cum_dividend", "indiparams": [year_str, "CNY"]}
+                ]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty and 'annual_cum_dividend' in df.columns:
+                end_date = f"{year_str}{acc_date}"
+                df['code'] = code
+                df['market'] = self.market
+                df['end_date'] = end_date
+                self._save_df_to_wide_table("ifind_int_dividend", df, ["code", "market", "end_date"])
+
+                val = df['annual_cum_dividend'].iloc[0]
+                if pd.notna(val) and val != 0:
+                    results.append({
+                        'date_str': end_date,
+                        'dividends': float(val)
+                    })
+        
+        if not results:
+            return pd.DataFrame()
+            
+        df_div = pd.DataFrame(results)
+        return df_div
+
+    def get_repurchases(self, symbol: str, code: str) -> pd.DataFrame:
+        basic_info = self._fetch_basic_info(symbol, code)
+        acc_date = basic_info.get("accounting_date", "1231")
+        
+        mm = acc_date[:2]
+        dd = acc_date[2:]
+        fmt_mm_dd = f"{mm}-{dd}"
+        
+        if code in self._active_years_cache:
+            years_to_fetch = sorted(self._active_years_cache[code], reverse=True)
+        else:
+            current_year = int(time.strftime("%Y"))
+            years_to_fetch = [current_year - i for i in range(5)]
+        
+        results = []
+
+        for target_year in years_to_fetch:
+            start_date = f"{target_year - 1}-{fmt_mm_dd}"
+            end_date = f"{target_year}-{fmt_mm_dd}"
+            
+            params = {
+                "codes": code,
+                "indipara": [
+                    {"indicator": "repur_num_new", "indiparams": [start_date, end_date, "1"]}
+                ]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty and 'repur_num_new' in df.columns:
+                target_date = f"{target_year}{acc_date}"
+                df['code'] = code
+                df['market'] = self.market
+                df['end_date'] = target_date
+                self._save_df_to_wide_table("ifind_int_repurchase", df, ["code", "market", "end_date"])
+
+                val = df['repur_num_new'].iloc[0]
+                if pd.notna(val) and val != 0:
+                    results.append({
+                        'date_str': target_date,
+                        'repurchases': float(val)
+                    })
+        
+        if not results:
+            return pd.DataFrame()
+            
+        df_repur = pd.DataFrame(results)
+        return df_repur
+
+    def get_employee_count(self, symbol: str, code: str) -> pd.DataFrame:
+        basic_info = self._fetch_basic_info(symbol, code)
+        acc_date = basic_info.get("accounting_date", "1231")
+        mm = acc_date[:2]
+        dd = acc_date[2:]
+        
+        if code in self._active_years_cache:
+            years_to_fetch = sorted(self._active_years_cache[code], reverse=True)
+        else:
+            current_year = int(time.strftime("%Y"))
+            years_to_fetch = [current_year - i for i in range(5)]
+        
+        results = []
+
+        for target_year in years_to_fetch:
+            target_date = f"{target_year}-{mm}-{dd}"
+            
+            params = {
+                "codes": code,
+                "indipara": [
+                    {"indicator": "staff_num", "indiparams": [target_date]}
+                ]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty and 'staff_num' in df.columns:
+                df['code'] = code
+                df['market'] = self.market
+                df['end_date'] = f"{target_year}{acc_date}"
+                self._save_df_to_wide_table("ifind_int_employee", df, ["code", "market", "end_date"])
+
+                val = df['staff_num'].iloc[0]
+                if pd.notna(val) and val != 0:
+                    results.append({
+                        'date_str': f"{target_year}{acc_date}",
+                        'employee_count': float(val)
+                    })
+        
+        if not results:
+            return pd.DataFrame()
+            
+        df_emp = pd.DataFrame(results)
+        return df_emp
+    
+    def get_financial_ratios(self, symbol: str, code: str) -> pd.DataFrame:
+        current_year = int(time.strftime("%Y"))
+        basic_info = self._fetch_basic_info(symbol, code)
+        acc_date = basic_info.get("accounting_date", "1231")
+
+        last_valid_year = None
+        for offset in range(3):
+            test_year = current_year - offset
+            test_date = f"{test_year}{acc_date}"
+            params = {
+                "codes": code,
+                "indipara": [{"indicator": "roe", "indiparams": [test_date]}]
+            }
+            res = self.cli.post("basic_data_service", params)
+            df = self._parse_ifind_tables(res)
+            if not df.empty:
+                val = df.iloc[0, 0] if not df.empty and df.shape[1] > 0 else None
+                if pd.notna(val) and val != 0:
+                    last_valid_year = test_year
+                    break
+        
+        if last_valid_year is None:
+            last_valid_year = current_year
+
+        all_dfs = []
+        
+        # Use cached years from financial statements
+        if code in self._active_years_cache:
+            years_to_fetch = sorted(self._active_years_cache[code], reverse=True)
+        else:
+            # Fallback to last_valid_year approach
+            years_to_fetch = [last_valid_year - i for i in range(5)]
+
+        for target_year in years_to_fetch:
+            date_str = f"{target_year}{acc_date}"
+            year_str = str(target_year)
+            
+            indipara = []
+            for key in ["salary_pp", "revenue_pp", "profit_pp"]:
+                indipara.append({"indicator": key, "indiparams": [year_str, "100"]})
+                
+            ratio_keys = [
+                "roe", "roa", "roic", 
+                "sales_fee_to_or", "manage_fee_to_revenue", "rad_expense_to_total_income",
+                "operating_revenue_yoy", "np_atsopc_yoy", 
+                "ibdebt_ratio_asset_base", 
+                "inventory_turnover_days", "receivable_turnover_days", "accounts_payable_turnover_days",
+                "fixed_asset_turnover_ratio", "total_capital_turnover"
+            ]
+            for key in ratio_keys:
+                indipara.append({"indicator": key, "indiparams": [date_str]})
+                
+            params = {
+                "codes": code,
+                "indipara": indipara
+            }
+            
+            res = self.cli.post("basic_data_service", params)
+            # Use 'params' to debug if needed, but logging is inside _parse in client base
+            df = self._parse_ifind_tables(res)
+            
+            if not df.empty:
+                if 'end_date' not in df.columns:
+                    df['end_date'] = date_str
+                df = df.dropna(axis=1, how='all')
+                all_dfs.append(df)
+                    
+        if not all_dfs:
+            return pd.DataFrame()
+            
+        combined = pd.concat(all_dfs, ignore_index=True)
+        # Ratio table save
+        combined['code'] = code
+        combined['market'] = self.market
+        self._save_df_to_wide_table("ifind_int_financial_ratios", combined, ["code", "market", "end_date"])
+        
+        return combined
+

backend/app/clients/tushare_cn_client.py

@@ -0,0 +1,468 @@
+"""
+Tushare CN Client for Backend API
+Direct port from src/fetchers/tushare_cn_client.py
+Removed storage.file_io dependency, only uses PostgreSQL
+"""
+import tushare as ts
+import pandas as pd
+import os
+import psycopg2
+from datetime import datetime
+from dotenv import load_dotenv
+from typing import Union, List, Dict, Any
+import numpy as np
+from pathlib import Path
+import logging
+import time
+
+# 获取logger (不再配置basicConfig,由main.py统一配置)
+logger = logging.getLogger(__name__)
+
+# Explicitly load .env from project root
+ROOT_DIR = Path(__file__).resolve().parent.parent.parent.parent
+load_dotenv(ROOT_DIR / ".env")
+
+class TushareCnClient:
+    def __init__(self, api_key: str):
+        ts.set_token(api_key)
+        self.pro = ts.pro_api()
+        self.api_key = api_key
+
+    def _get_db_connection(self):
+        """Create a database connection."""
+        db_host = os.getenv("DB_HOST", "192.168.3.195")
+        db_user = os.getenv("DB_USER", "value")
+        db_pass = os.getenv("DB_PASSWORD", "Value609!")
+        db_name = os.getenv("DB_NAME", "fa3")
+        db_port = os.getenv("DB_PORT", "5432")
+
+        try:
+            conn = psycopg2.connect(
+                host=db_host, user=db_user, password=db_pass, dbname=db_name, port=db_port
+            )
+            return conn
+        except Exception as e:
+            print(f"DB Connection Error: {e}")
+            return None
+
+    def _map_dtype_to_sql(self, dtype):
+        """Map pandas dtype to PostgreSQL type."""
+        if pd.api.types.is_integer_dtype(dtype):
+            return "BIGINT"
+        elif pd.api.types.is_float_dtype(dtype):
+            return "NUMERIC"
+        elif pd.api.types.is_bool_dtype(dtype):
+            return "BOOLEAN"
+        elif pd.api.types.is_datetime64_any_dtype(dtype):
+            return "TIMESTAMP"
+        else:
+            return "TEXT"
+
+    def _save_df_to_wide_table(self, table_name: str, df: pd.DataFrame, pk_cols: List[str]):
+        """
+        Save DataFrame to a specific wide table.
+        Creates table if not exists using DF columns.
+        Performs incremental save: checks existing records and only inserts new ones.
+        """
+        if df is None or df.empty:
+            return
+        
+        start_time = time.time()
+        # 1. Clean Data
+        df_clean = df.replace({np.nan: None})
+        
+        # Convert date columns to YYYY-MM-DD format
+        for col in df_clean.columns:
+            if 'date' in col.lower() and df_clean[col].dtype == 'object':
+                try:
+                    sample = df_clean[col].dropna().astype(str).iloc[0] if not df_clean[col].dropna().empty else ""
+                    if len(sample) == 8 and sample.isdigit():
+                            df_clean[col] = df_clean[col].astype(str).apply(
+                                lambda x: f"{x[:4]}-{x[4:6]}-{x[6:]}" if x and len(str(x))==8 else x
+                            )
+                except:
+                    pass
+
+        conn = self._get_db_connection()
+        if not conn: return
+
+        try:
+            with conn.cursor() as cur:
+                # 2. Check if table exists
+                cur.execute("SELECT to_regclass(%s)", (f"public.{table_name}",))
+                table_exists = cur.fetchone()[0] is not None
+                
+                columns = list(df_clean.columns)
+
+                if not table_exists:
+                    # Create table if not exists
+                    logger.info(f"🆕 [数据库] 表 {table_name} 不存在，正在创建...")
+                    col_defs = ['"id" SERIAL PRIMARY KEY']
+                    for col in columns:
+                        sql_type = self._map_dtype_to_sql(df_clean[col].dtype)
+                        col_defs.append(f'"{col}" {sql_type}')
+                    
+                    col_defs.append('"update_date" TIMESTAMP DEFAULT CURRENT_TIMESTAMP')
+                    
+                    pk_str = ", ".join([f'"{c}"' for c in pk_cols])
+                    constraint_name = f"uq_{table_name}"
+                    create_sql = f"""
+                        CREATE TABLE IF NOT EXISTS {table_name} (
+                            {', '.join(col_defs)},
+                            CONSTRAINT {constraint_name} UNIQUE ({pk_str})
+                        );
+                    """
+                    cur.execute(create_sql)
+                    conn.commit()
+                    # No existing data, insert all
+                    df_to_insert = df_clean
+                else:
+                    # 3. Incremental Logic: Filter out existing records
+                    # We assume 'ts_code' is always in pk_cols and present in df
+                    if 'ts_code' in df_clean.columns and 'ts_code' in pk_cols:
+                        ts_code_val = df_clean['ts_code'].iloc[0]
+                        
+                        # Build query to fetch existing PKs for this stock
+                        # We only select the PK columns to minimize data transfer
+                        pk_select = ", ".join([f'"{c}"' for c in pk_cols])
+                        
+                        header_sql = f"SELECT {pk_select} FROM {table_name} WHERE \"ts_code\" = %s"
+                        cur.execute(header_sql, (ts_code_val,))
+                        existing_rows = cur.fetchall()
+                        
+                        if existing_rows:
+                            # Create a set of existing PK tuples for fast lookup
+                            # Ensure types match (convert to string if necessary/consistent with df)
+                            existing_keys = set(existing_rows)
+                            
+                            # Filter df
+                            # We need to construct a tuple from df rows corresponding to pk_cols
+                            # Note: DB returns tuples in order of pk_colsQuery
+                            
+                            def row_to_key(row):
+                                return tuple(row[col] for col in pk_cols)
+
+                            # Identify rows to keep
+                            # This is a bit slow for very large DFs, but usually we have < 1000 rows
+                            is_new_list = []
+                            for _, row in df_clean.iterrows():
+                                key = row_to_key(row)
+                                # DB driver might return dates as datetime.date, df has strings or timestamps
+                                # Let's try simple comparison first, if issues arise we might need normalization
+                                # For now, assuming string matching for dates if they were converted above
+                                is_new = key not in existing_keys
+                                is_new_list.append(is_new)
+                            
+                            df_to_insert = df_clean[is_new_list]
+                            skipped_count = len(df_clean) - len(df_to_insert)
+                            if skipped_count > 0:
+                                logger.info(f"⏭️ [数据库] 表 {table_name}: 跳过 {skipped_count} 条已存在的数据, 准备插入 {len(df_to_insert)} 条")
+                        else:
+                            df_to_insert = df_clean
+                    else:
+                        # Fallback for tables without ts_code (rare in this context)
+                        # Identify existing PKs? Or just try insert all with ON CONFLICT DO NOTHING
+                        # User specifically asked for 'code and end date' logic
+                        df_to_insert = df_clean
+
+                if df_to_insert.empty:
+                    logger.info(f"✅ [数据库] 表 {table_name}: 所有数据已存在，无需更新。")
+                    return
+
+                # 4. Get existing columns from DB to ensure schema match
+                cur.execute("""
+                    SELECT column_name 
+                    FROM information_schema.columns 
+                    WHERE table_name = %s
+                """, (table_name,))
+                db_cols = {row[0] for row in cur.fetchall()}
+                
+                # Add auto-columns (id) if missing
+                if 'id' not in db_cols:
+                    try:
+                        cur.execute(f'ALTER TABLE "{table_name}" ADD COLUMN "id" SERIAL')
+                        conn.commit()
+                        db_cols.add('id')
+                    except Exception as e:
+                        print(f"Error adding id column to {table_name}: {e}")
+                        conn.rollback()
+                
+                valid_cols = [c for c in columns if c in db_cols]
+                
+                # 5. Construct INSERT statement
+                cols_str = ", ".join([f'"{c}"' for c in valid_cols])
+                vals_str = ", ".join(["%s"] * len(valid_cols))
+                
+                # Since we filtered, we can use INSERT directly, 
+                # but ON CONFLICT DO NOTHING is safer for race conditions
+                # The user wanted to avoid updating if exists.
+                
+                insert_sql = f"""
+                    INSERT INTO {table_name} ({cols_str})
+                    VALUES ({vals_str})
+                    ON CONFLICT ({", ".join([f'"{c}"' for c in pk_cols])}) 
+                    DO NOTHING
+                """
+
+                # 6. Execute Batch Insert
+                logger.info(f"📥 [数据库] 正在保存 {len(df_to_insert)} 条新数据到 {table_name}...")
+                data_tuples = [tuple(x) for x in df_to_insert[valid_cols].to_numpy()]
+                cur.executemany(insert_sql, data_tuples)
+                conn.commit()
+                
+                elapsed = time.time() - start_time
+                logger.info(f"✅ [数据库] {table_name}: 成功插入 {len(data_tuples)} 条数据, 耗时: {elapsed:.2f}秒")
+
+        except Exception as e:
+            logger.error(f"❌ [数据库] 保存 {table_name} 失败: {e}")
+            conn.rollback()
+        finally:
+            conn.close()
+
+    def _get_ts_code(self, symbol: str) -> str:
+        return symbol
+
+    def _filter_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        if df.empty or 'end_date' not in df.columns:
+            return df
+        df = df.sort_values(by='end_date', ascending=False)
+        df = df.drop_duplicates(subset=['end_date'], keep='first')
+        return df
+
+    def get_income_statement(self, symbol: str) -> pd.DataFrame:
+        logger.info(f"🐛 [DEBUG] get_income_statement called with symbol: '{symbol}'")
+        ts_code = self._get_ts_code(symbol)
+        logger.info(f"🔍 [Tushare API] 调用 income() 获取利润表, symbol={ts_code}")
+        start_time = time.time()
+        df = self.pro.income(ts_code=ts_code)
+        elapsed = time.time() - start_time
+        logger.info(f"✅ [Tushare API] income() 完成, 返回 {len(df)} 行, 耗时: {elapsed:.2f}秒")
+        
+        # Save to DB (Wide Table)
+        if not df.empty and {'ts_code', 'end_date', 'report_type'}.issubset(df.columns):
+             self._save_df_to_wide_table('tushare_income_statement', df, ['ts_code', 'end_date', 'report_type'])
+
+        # Legacy Rename for Frontend compatibility
+        rename_map = {
+            'end_date': 'date',
+            'revenue': 'revenue',
+            'n_income_attr_p': 'net_income'
+        }
+        df_processed = self._filter_data(df)
+        df_processed = df_processed.rename(columns=rename_map)
+        
+        return df_processed
+
+    def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
+        ts_code = self._get_ts_code(symbol)
+        logger.info(f"🔍 [Tushare API] 调用 balancesheet() 获取资产负债表, symbol={ts_code}")
+        start_time = time.time()
+        df = self.pro.balancesheet(ts_code=ts_code)
+        elapsed = time.time() - start_time
+        logger.info(f"✅ [Tushare API] balancesheet() 完成, 返回 {len(df)} 行, 耗时: {elapsed:.2f}秒")
+        
+        # Save to DB
+        if not df.empty and {'ts_code', 'end_date', 'report_type'}.issubset(df.columns):
+            self._save_df_to_wide_table('tushare_balance_sheet', df, ['ts_code', 'end_date', 'report_type'])
+
+        rename_map = {
+            'end_date': 'date',
+            'total_hldr_eqy_exc_min_int': 'total_equity',
+            'total_liab': 'total_liabilities',
+            'total_cur_assets': 'current_assets',
+            'total_cur_liab': 'current_liabilities'
+        }
+        df_processed = self._filter_data(df)
+        df_processed = df_processed.rename(columns=rename_map)
+        
+        return df_processed
+
+    def get_cash_flow(self, symbol: str) -> pd.DataFrame:
+        ts_code = self._get_ts_code(symbol)
+        logger.info(f"🔍 [Tushare API] 调用 cashflow() 获取现金流量表, symbol={ts_code}")
+        start_time = time.time()
+        df = self.pro.cashflow(ts_code=ts_code)
+        elapsed = time.time() - start_time
+        logger.info(f"✅ [Tushare API] cashflow() 完成, 返回 {len(df)} 行, 耗时: {elapsed:.2f}秒")
+        
+        # Save to DB
+        if not df.empty and {'ts_code', 'end_date', 'report_type'}.issubset(df.columns):
+            self._save_df_to_wide_table('tushare_cash_flow', df, ['ts_code', 'end_date', 'report_type'])
+
+        df_processed = self._filter_data(df)
+        df_processed = df_processed.rename(columns={
+            'end_date': 'date', 
+            'n_cashflow_act': 'net_cash_flow',
+            'depr_fa_coga_dpba': 'depreciation'
+        })
+        
+        return df_processed
+
+    def get_market_metrics(self, symbol: str) -> dict:
+        ts_code = self._get_ts_code(symbol)
+        metrics = {
+            "price": 0.0,
+            "market_cap": 0.0,
+            "pe": 0.0,
+            "pb": 0.0,
+            "total_share_holders": 0,
+            "employee_count": 0
+        }
+        
+        try:
+            # 1. Daily Basic
+            df_daily = self.pro.daily_basic(ts_code=ts_code, limit=1)
+            if not df_daily.empty:
+                 row = df_daily.iloc[0]
+                 metrics["price"] = row.get('close', 0.0)
+                 metrics["pe"] = row.get('pe', 0.0)
+                 metrics["pb"] = row.get('pb', 0.0)
+                 metrics["market_cap"] = row.get('total_mv', 0.0) * 10000 
+                 metrics["dividend_yield"] = row.get('dv_ttm', 0.0)
+                 # Save to DB
+                 if 'trade_date' in df_daily.columns:
+                    self._save_df_to_wide_table('tushare_daily_basic', df_daily, ['ts_code', 'trade_date'])
+
+            # 2. Stock Basic
+            df_basic = self.pro.stock_basic(ts_code=ts_code, fields='ts_code,symbol,name,area,industry,list_date')
+            if not df_basic.empty:
+                metrics['name'] = df_basic.iloc[0]['name']
+                metrics['list_date'] = df_basic.iloc[0]['list_date'] 
+                # Save to DB
+                self._save_df_to_wide_table('tushare_stock_basic', df_basic, ['ts_code'])
+                
+            # 3. Company Info
+            df_comp = self.pro.stock_company(ts_code=ts_code)
+            if not df_comp.empty:
+                metrics["employee_count"] = int(df_comp.iloc[0].get('employees', 0) or 0)
+                
+            # 4. Shareholder Number
+            df_holder = self.pro.stk_holdernumber(ts_code=ts_code, limit=1)
+            if not df_holder.empty:
+                 metrics["total_share_holders"] = int(df_holder.iloc[0].get('holder_num', 0) or 0)
+                 # Save to DB.
+                 if 'end_date' in df_holder.columns:
+                     self._save_df_to_wide_table('tushare_stk_holdernumber', df_holder, ['ts_code', 'end_date'])
+                 
+        except Exception as e:
+            print(f"Error fetching market metrics for {symbol}: {e}")
+            import traceback
+            traceback.print_exc()
+            
+        return metrics
+
+    def get_historical_metrics(self, symbol: str, dates: list) -> pd.DataFrame:
+        ts_code = self._get_ts_code(symbol)
+        results = []
+        
+        if not dates:
+            return pd.DataFrame()
+
+        unique_dates = sorted(list(set([str(d).replace('-', '') for d in dates])), reverse=True)
+        
+        try:
+            min_date = min(unique_dates)
+            max_date = max(unique_dates)
+            
+            # Fetch and Save Daily Basic
+            df_daily = self.pro.daily_basic(ts_code=ts_code, start_date=min_date, end_date=max_date)
+            if not df_daily.empty:
+                self._save_df_to_wide_table('tushare_daily_basic', df_daily, ['ts_code', 'trade_date'])
+                df_daily = df_daily.sort_values('trade_date', ascending=False)
+
+            # Fetch and Save Shareholder Number
+            df_holder = self.pro.stk_holdernumber(ts_code=ts_code, start_date=min_date, end_date=max_date)
+            if not df_holder.empty:
+                self._save_df_to_wide_table('tushare_stk_holdernumber', df_holder, ['ts_code', 'end_date'])
+                df_holder = df_holder.sort_values('end_date', ascending=False)
+            
+            # Build legacy results DataFrame for internal return
+            for date_str in unique_dates:
+                metrics = {'date': date_str} 
+                
+                if not df_daily.empty:
+                    closest_daily = df_daily[df_daily['trade_date'] <= date_str]
+                    if not closest_daily.empty:
+                        row = closest_daily.iloc[0]
+                        metrics['Price'] = row.get('close')
+                        metrics['PE'] = row.get('pe')
+                        metrics['PB'] = row.get('pb')
+                        metrics['MarketCap'] = row.get('total_mv', 0) * 10000
+                
+                if not df_holder.empty:
+                    closest_holder = df_holder[df_holder['end_date'] <= date_str]
+                    if not closest_holder.empty:
+                        metrics['Shareholders'] = closest_holder.iloc[0].get('holder_num')
+                
+                results.append(metrics)
+
+        except Exception as e:
+            print(f"Error fetching historical metrics for {symbol}: {e}")
+            
+        return pd.DataFrame(results)
+
+    def get_dividends(self, symbol: str) -> pd.DataFrame:
+        import time
+        ts_code = self._get_ts_code(symbol)
+        df_div = self.pro.dividend(ts_code=ts_code, fields='ts_code,end_date,ann_date,div_proc,stk_div,cash_div_tax,cash_div,record_date,ex_date,pay_date,div_listdate,imp_ann_date')
+
+        if not df_div.empty:
+             # Save to DB
+             self._save_df_to_wide_table('tushare_dividend', df_div, ['ts_code', 'end_date', 'ann_date'])
+
+        if df_div.empty:
+            return pd.DataFrame()
+
+        # Legacy processing for return
+        df_div = df_div[(df_div['div_proc'] == '实施') & (df_div['cash_div'] > 0)]
+        if df_div.empty:
+            return pd.DataFrame()
+
+        df_div['total_cash_div'] = 0.0
+        
+        calced_divs = []
+        for index, row in df_div.iterrows():
+            ex_date = row['ex_date']
+            if not ex_date or pd.isna(ex_date): continue
+            
+            try:
+                time.sleep(0.1) 
+                df_daily = self.pro.daily_basic(ts_code=ts_code, trade_date=ex_date, fields='total_share')
+                if not df_daily.empty:
+                    total_share = df_daily.iloc[0]['total_share'] 
+                    total_cash_dividend = (row['cash_div'] * total_share * 10000) 
+                    calced_divs.append({
+                        'year': int(str(row['end_date'])[:4]),
+                        'dividends': total_cash_dividend
+                    })
+            except: pass
+
+        if calced_divs:
+            df_calc = pd.DataFrame(calced_divs)
+            dividends_by_year = df_calc.groupby('year')['dividends'].sum().reset_index()
+            dividends_by_year['date'] = dividends_by_year['year'].astype(str) + '1231'
+            return dividends_by_year[['date', 'dividends']]
+            
+        return pd.DataFrame()
+
+    def get_repurchases(self, symbol: str) -> pd.DataFrame:
+        ts_code = self._get_ts_code(symbol)
+        df = self.pro.repurchase(ts_code=ts_code)
+
+        if not df.empty:
+             if 'ann_date' in df.columns and 'end_date' in df.columns:
+                 self._save_df_to_wide_table('tushare_repurchase', df, ['ts_code', 'ann_date', 'end_date'])
+
+        if df.empty or 'ann_date' not in df.columns or 'amount' not in df.columns:
+            return pd.DataFrame()
+
+        # Legacy processing
+        df = df[df['amount'] > 0]
+        if df.empty: return pd.DataFrame()
+
+        df['year'] = pd.to_datetime(df['ann_date']).dt.year
+        repurchases_by_year = df.groupby('year')['amount'].sum().reset_index()
+        repurchases_by_year['date_str'] = repurchases_by_year['year'].astype(str) + '1231'
+        repurchases_by_year.rename(columns={'amount': 'repurchases', 'date_str': 'date'}, inplace=True)
+        
+        return repurchases_by_year[['date', 'repurchases']]

backend/app/database.py

@@ -1,47 +1,33 @@
-from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
+"""
-from sqlalchemy import text
+数据库连接配置 (新架构)
-from app.models import Base
+使用 AsyncSession 支持异步操作
+"""
 import os
+from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
+from sqlalchemy.orm import declarative_base
 from dotenv import load_dotenv
 
 load_dotenv()
 
-DATABASE_URL = os.getenv("DATABASE_URL", "sqlite+aiosqlite:///./stock_analysis.db")
+# 数据库连接 URL
+DATABASE_URL = f"postgresql+asyncpg://{os.getenv('DB_USER')}:{os.getenv('DB_PASSWORD')}@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}/{os.getenv('DB_NAME')}"
 
-engine = create_async_engine(DATABASE_URL, echo=True)
+# 创建异步引擎
-AsyncSessionLocal = async_sessionmaker(engine, expire_on_commit=False)
+engine = create_async_engine(
+    DATABASE_URL,
+    echo=False,  # 设置为 True 可以看到 SQL 日志
+    future=True
+)
 
-async def init_db():
+# 创建异步 Session 工厂
-    async with engine.begin() as conn:
+SessionLocal = async_sessionmaker(
-        await conn.run_sync(Base.metadata.create_all)
+    engine,
-        
+    class_=AsyncSession,
-        # Migration: Add ai_model column if it doesn't exist (for SQLite)
+    expire_on_commit=False
-        try:
+)
-            await conn.execute(
-                text("ALTER TABLE reports ADD COLUMN ai_model VARCHAR(100) DEFAULT 'gemini-2.0-flash-exp'")
-            )
-            print("Migration: Added ai_model column to reports table")
-        except Exception as e:
-            # Column already exists or other error
-            if "duplicate column" not in str(e).lower() and "already exists" not in str(e).lower():
-                print(f"Migration check: {e}")
-
-        # Migration: Add token columns to report_sections
-        columns_to_add = [
-            ("prompt_tokens", "INTEGER DEFAULT 0"),
-            ("completion_tokens", "INTEGER DEFAULT 0"),
-            ("total_tokens", "INTEGER DEFAULT 0")
-        ]
-        
-        for col_name, col_type in columns_to_add:
-            try:
-                await conn.execute(text(f"ALTER TABLE report_sections ADD COLUMN {col_name} {col_type}"))
-                print(f"Migration: Added {col_name} to report_sections table")
-            except Exception as e:
-                # SQLite error for duplicate column usually contains "duplicate column name"
-                if "duplicate column" not in str(e).lower() and "already exists" not in str(e).lower():
-                     print(f"Migration check for {col_name}: {e}")
 
+# Dependency
 async def get_db():
-    async with AsyncSessionLocal() as session:
+    """获取数据库会话"""
+    async with SessionLocal() as session:
         yield session

backend/app/fetchers/__init__.py

@@ -0,0 +1,3 @@
+"""
+Fetchers package initialization
+"""

backend/app/fetchers/base.py

@@ -0,0 +1,41 @@
+"""
+Base Fetcher class
+"""
+from abc import ABC, abstractmethod
+import pandas as pd
+
+class DataFetcher(ABC):
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+
+    @abstractmethod
+    def get_income_statement(self, symbol: str) -> pd.DataFrame:
+        pass
+
+    @abstractmethod
+    def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
+        pass
+
+    @abstractmethod
+    def get_cash_flow(self, symbol: str) -> pd.DataFrame:
+        pass
+
+    @abstractmethod
+    def get_market_metrics(self, symbol: str) -> dict:
+        pass
+
+    def get_historical_metrics(self, symbol: str, dates: list) -> pd.DataFrame:
+        """Optional method, not all fetchers need to implement"""
+        return pd.DataFrame()
+
+    def get_dividends(self, symbol: str) -> pd.DataFrame:
+        """Optional method"""
+        return pd.DataFrame()
+
+    def get_repurchases(self, symbol: str) -> pd.DataFrame:
+        """Optional method"""
+        return pd.DataFrame()
+
+    def get_employee_count(self, symbol: str) -> pd.DataFrame:
+        """Optional method"""
+        return pd.DataFrame()

backend/app/fetchers/bloomberg_fetcher.py

@@ -0,0 +1,145 @@
+
+import pandas as pd
+import os
+import psycopg2
+from .base import DataFetcher
+from app.clients.bloomberg_client import BloombergClient
+
+class BloombergFetcher(DataFetcher):
+    """
+    Bloomberg Data Fetcher.
+    Fetches data via BloombergClient (Remote Jupyter) into 'stockcard' table.
+    Reads 'stockcard' table to provide standard DataFrames.
+    """
+    def __init__(self, api_key: str = None, market: str = "US"):
+        super().__init__(api_key) # api_key unused for Bloomberg usually (password based)
+        self.market = market
+        self.client = BloombergClient() # Uses env vars
+        self._fetched_symbols = set()  # Track symbols we've already fetched
+        
+    def _get_db_connection(self):
+        """Create a database connection."""
+        db_host = os.getenv("DB_HOST", "192.168.3.195")
+        db_user = os.getenv("DB_USER", "value")
+        db_pass = os.getenv("DB_PASSWORD", "Value609!")
+        db_name = os.getenv("DB_NAME", "fa3")
+        db_port = os.getenv("DB_PORT", "5432")
+
+        try:
+            conn = psycopg2.connect(
+                host=db_host, user=db_user, password=db_pass, dbname=db_name, port=db_port
+            )
+            return conn
+        except Exception as e:
+            print(f"DB Connection Error: {e}")
+            return None
+
+    def _get_company_code(self, symbol: str) -> str:
+        # Match logic in Client
+        if "Equity" in symbol:
+            return symbol
+        return f"{symbol} {self.market} Equity"
+
+    def _get_data_from_db(self, symbol: str, indicators: list) -> pd.DataFrame:
+        """
+        Query 'stockcard' for specific indicators and return pivoted DataFrame.
+        """
+        company_code = self._get_company_code(symbol)
+        conn = self._get_db_connection()
+        if not conn: return pd.DataFrame()
+        
+        try:
+            # placeholders for 'IN' clause
+            placeholders = ','.join(['%s'] * len(indicators))
+            query = f"""
+                SELECT indicator, value, value_date 
+                FROM stockcard 
+                WHERE Company_code = %s AND indicator IN ({placeholders})
+                ORDER BY value_date DESC
+            """
+            
+            with conn.cursor() as cur:
+                cur.execute(query, (company_code, *indicators))
+                rows = cur.fetchall()
+            
+            if not rows:
+                return pd.DataFrame()
+                
+            df = pd.DataFrame(rows, columns=['indicator', 'value', 'value_date'])
+            
+            # Pivot
+            # Index: value_date
+            # Columns: indicator
+            # Values: value
+            df_pivot = df.pivot(index='value_date', columns='indicator', values='value').reset_index()
+            df_pivot.rename(columns={'value_date': 'end_date'}, inplace=True)
+            
+            # Clean columns? No, they are standard from config.
+            return df_pivot
+                
+        except Exception as e:
+            print(f"Error querying stockcard: {e}")
+            return pd.DataFrame()
+        finally:
+            conn.close()
+
+    def _ensure_data_fetched(self, symbol: str, progress_callback=None):
+        """
+        Trigger fetch if needed. Only fetch once per symbol per instance.
+        """
+        if symbol in self._fetched_symbols:
+            return  # Already fetched for this symbol
+            
+        try:
+            # Check if fetch_company supports progress_callback
+            import inspect
+            sig = inspect.signature(self.client.fetch_company)
+            if 'progress_callback' in sig.parameters:
+                self.client.fetch_company(self.market, symbol, progress_callback=progress_callback)
+            else:
+                self.client.fetch_company(self.market, symbol)
+                
+            self._fetched_symbols.add(symbol)
+        except Exception as e:
+            print(f"Bloomberg fetch failed (ignoring, checking DB): {e}")
+
+    def sync_all_data(self, symbol: str, progress_callback=None) -> None:
+        """
+        全量同步 Bloomberg 数据
+        
+        触发一次全量抓取即可，不需要分别抓取各个报表。
+        数据会自动存储到数据库。
+        """
+        self._ensure_data_fetched(symbol, progress_callback=progress_callback)
+        
+    def get_income_statement(self, symbol: str) -> pd.DataFrame:
+        """兼容性空方法"""
+        return pd.DataFrame()
+
+    def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
+        """兼容性空方法"""
+        return pd.DataFrame()
+
+    def get_cash_flow(self, symbol: str) -> pd.DataFrame:
+        """兼容性空方法"""
+        return pd.DataFrame()
+
+    def get_market_metrics(self, symbol: str) -> pd.DataFrame:
+        """兼容性空方法"""
+        return pd.DataFrame()
+
+    def get_dividends(self, symbol: str) -> pd.DataFrame:
+        """兼容性空方法"""
+        return pd.DataFrame()
+
+    def get_repurchases(self, symbol: str) -> pd.DataFrame:
+        """兼容性空方法"""
+        return pd.DataFrame()
+    
+    def get_employee_count(self, symbol: str) -> pd.DataFrame:
+        """兼容性空方法"""
+        return pd.DataFrame()
+
+    def get_financial_ratios(self, symbol: str) -> pd.DataFrame:
+        """兼容性空方法"""
+        return pd.DataFrame()

backend/app/fetchers/cn_fetcher.py

@@ -0,0 +1,41 @@
+"""
+CN (China) Market Data Fetcher
+"""
+import pandas as pd
+from .base import DataFetcher
+
+class CnFetcher(DataFetcher):
+    def __init__(self, api_key: str, data_source: str = 'Tushare'):
+        super().__init__(api_key)
+        self.data_source = data_source
+        
+        if self.data_source == 'Tushare':
+            from app.clients.tushare_cn_client import TushareCnClient
+            self.client = TushareCnClient(api_key)
+        else:
+            # Default to Tushare
+            if self.data_source == 'Akshare':
+                 raise NotImplementedError("Akshare client not yet implemented")
+            from app.clients.tushare_cn_client import TushareCnClient
+            self.client = TushareCnClient(api_key)
+
+    def get_income_statement(self, symbol: str) -> pd.DataFrame:
+        return self.client.get_income_statement(symbol)
+
+    def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
+        return self.client.get_balance_sheet(symbol)
+
+    def get_cash_flow(self, symbol: str) -> pd.DataFrame:
+        return self.client.get_cash_flow(symbol)
+
+    def get_market_metrics(self, symbol: str) -> dict:
+        return self.client.get_market_metrics(symbol)
+
+    def get_historical_metrics(self, symbol: str, dates: list) -> pd.DataFrame:
+        return self.client.get_historical_metrics(symbol, dates)
+
+    def get_dividends(self, symbol: str) -> pd.DataFrame:
+        return self.client.get_dividends(symbol)
+
+    def get_repurchases(self, symbol: str) -> pd.DataFrame:
+        return self.client.get_repurchases(symbol)

backend/app/fetchers/factory.py

@@ -0,0 +1,106 @@
+"""
+Fetcher Factory - Backend Version
+Currently supports CN market with Tushare
+"""
+import os
+
+
+class FetcherFactory:
+    """
+    数据获取工厂类 (Backend版本)
+    
+    当前支持的数据源:
+    - Tushare: CN (中国A股)
+    - iFinD: HK, JP, US, VN
+    - Bloomberg: CN, HK, JP, US, VN
+    """
+    
+    @staticmethod
+    def get_fetcher(market: str, data_source: str = None, **kwargs):
+        """
+        获取对应市场和数据源的 Fetcher
+        
+        Args:
+            market: 市场代码 (CN, HK, JP, US, VN)
+            data_source: 数据源名称 (iFinD, Tushare, Bloomberg)
+            **kwargs: 额外参数（如 API tokens）
+        
+        Returns:
+            Fetcher 实例
+        """
+        market = market.upper()
+        
+        # 如果未指定数据源，使用默认值
+        if not data_source:
+            data_source = FetcherFactory._get_default_source(market)
+        
+        elif data_source == 'iFinD':
+            return FetcherFactory._get_ifind_fetcher(market, **kwargs)
+        elif data_source == 'Bloomberg':
+            return FetcherFactory._get_bloomberg_fetcher(market, **kwargs)
+        else:
+            # 其他数据源暂未迁移
+            raise NotImplementedError(
+                f"Data source '{data_source}' not supported in backend."
+            )
+    
+    @staticmethod
+    def _get_default_source(market: str) -> str:
+        """获取市场的默认数据源"""
+        defaults = {
+            'CN': 'Tushare',
+            'HK': 'iFinD',
+            'JP': 'iFinD',
+            'US': 'iFinD',
+            'VN': 'iFinD'
+        }
+        return defaults.get(market, 'iFinD')
+    
+    @staticmethod
+    def _get_tushare_fetcher(market: str, **kwargs):
+        """获取 Tushare Fetcher"""
+        if market != 'CN':
+            raise ValueError("Tushare only supports CN market")
+        
+        # 获取 Tushare token
+        tushare_token = kwargs.get('tushare_token') or os.getenv('TUSHARE_TOKEN')
+        
+        if not tushare_token:
+            raise ValueError("Tushare token is required for CN market")
+        
+        from app.fetchers.cn_fetcher import CnFetcher
+        return CnFetcher(tushare_token)
+
+    @staticmethod
+    def _get_ifind_fetcher(market: str, **kwargs):
+        """Get iFinD Fetcher"""
+        ifind_token = kwargs.get('ifind_token') or os.getenv('IFIND_REFRESH_TOKEN')
+        if not ifind_token:
+             # Try legacy env var name if needed, but IFIND_REFRESH_TOKEN is standard
+             ifind_token = os.getenv('IFIND_REFRESH_TOKEN')
+
+        if not ifind_token:
+             raise ValueError("iFinD refresh token is required (IFIND_REFRESH_TOKEN)")
+
+        if market == 'HK':
+            from app.fetchers.hk_fetcher import HkFetcher
+            return HkFetcher(ifind_token)
+        elif market == 'JP':
+            from app.fetchers.jp_fetcher import JpFetcher
+            return JpFetcher(ifind_token)
+        elif market == 'VN':
+            from app.fetchers.vn_fetcher import VnFetcher
+            return VnFetcher(ifind_token)
+        elif market == 'US':
+            from app.fetchers.us_fetcher import UsFetcher
+            return UsFetcher(ifind_token)
+        else:
+            raise NotImplementedError(f"iFinD fetcher for market {market} not yet migrated to backend.")
+
+    @staticmethod
+    def _get_bloomberg_fetcher(market: str, **kwargs):
+        """Get Bloomberg Fetcher"""
+        # Bloomberg client typically uses internal auth (password based) or default keys.
+        # We can pass kwargs but default init handles env vars.
+        from app.fetchers.bloomberg_fetcher import BloombergFetcher
+        return BloombergFetcher(market=market)

backend/app/fetchers/hk_fetcher.py

@@ -0,0 +1,49 @@
+
+import pandas as pd
+from .base import DataFetcher
+from app.clients.ifind_hk_client import IFindHKClient
+
+class HkFetcher(DataFetcher):
+    """
+    Hong Kong Market Fetcher using iFinD.
+    """
+    def __init__(self, api_key: str):
+        super().__init__(api_key)
+        self.data_source = 'iFinD'
+        self.client = IFindHKClient(api_key)
+        
+    def _get_ifind_code(self, symbol: str) -> str:
+        # HK stock codes are 4-5 digits, often 0 padded to 5 or 4 in other systems
+        # iFinD usually expects 4 digits like '0700.HK', '0005.HK'
+        if symbol.isdigit():
+            padded = symbol.zfill(4)
+            return f"{padded}.HK"
+        return symbol
+
+    def _fetch_basic_info(self, symbol: str):
+        code = self._get_ifind_code(symbol)
+        return self.client._fetch_basic_info(symbol, code)
+
+    def get_income_statement(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_income_statement(symbol, code)
+
+    def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_balance_sheet(symbol, code)
+
+    def get_cash_flow(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_cash_flow(symbol, code)
+
+    def get_market_metrics(self, symbol: str) -> dict:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_market_metrics(symbol, code)
+
+    def get_dividends(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_dividends(symbol, code)
+
+    def get_repurchases(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_repurchases(symbol, code)

backend/app/fetchers/jp_fetcher.py

@@ -0,0 +1,56 @@
+
+import pandas as pd
+from .base import DataFetcher
+from app.clients.ifind_int_client import IFindIntClient
+
+class JpFetcher(DataFetcher):
+    """
+    Japan Market Fetcher using iFinD International Client.
+    """
+    def __init__(self, api_key: str):
+        super().__init__(api_key)
+        self.data_source = 'iFinD'
+        self.client = IFindIntClient(api_key, 'JP')
+        
+    def _get_ifind_code(self, symbol: str) -> str:
+        # Simple logic: if pure digits, append .T (Tokyo SE). 
+        # Otherwise assume it's already a code or handled.
+        if symbol.isdigit():
+            return f"{symbol}.T"
+        return symbol
+
+    def _fetch_basic_info(self, symbol: str):
+        code = self._get_ifind_code(symbol)
+        return self.client._fetch_basic_info(symbol, code)
+
+    def get_income_statement(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_income_statement(symbol, code)
+
+    def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_balance_sheet(symbol, code)
+
+    def get_cash_flow(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_cash_flow(symbol, code)
+
+    def get_market_metrics(self, symbol: str) -> dict:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_market_metrics(symbol, code)
+
+    def get_dividends(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_dividends(symbol, code)
+
+    def get_repurchases(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_repurchases(symbol, code)
+        
+    def get_employee_count(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_employee_count(symbol, code)
+    
+    def get_financial_ratios(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_financial_ratios(symbol, code)

backend/app/fetchers/us_fetcher.py

@@ -0,0 +1,58 @@
+
+import pandas as pd
+from .base import DataFetcher
+from app.clients.ifind_int_client import IFindIntClient
+# Note: UsFetcher typically used AlphaVantage in legacy, but Factory defaults 'US' to 'iFinD' now.
+# Implementation plan says "US market supported by iFinD International Client".
+
+class UsFetcher(DataFetcher):
+    """
+    US Market Fetcher using iFinD International Client.
+    """
+    def __init__(self, api_key: str):
+        super().__init__(api_key)
+        self.data_source = 'iFinD'
+        self.client = IFindIntClient(api_key, 'US')
+        
+    def _get_ifind_code(self, symbol: str) -> str:
+        # US symbols usually just ticker, maybe suffix .N or .O
+        # Legacy system often handled this or client handled it.
+        # iFinD US symbols often need no suffix if unique, or specific exchange suffix.
+        # Let's assume input is correct for now or just upper.
+        return symbol.upper()
+
+    def _fetch_basic_info(self, symbol: str):
+        code = self._get_ifind_code(symbol)
+        return self.client._fetch_basic_info(symbol, code)
+
+    def get_income_statement(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_income_statement(symbol, code)
+
+    def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_balance_sheet(symbol, code)
+
+    def get_cash_flow(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_cash_flow(symbol, code)
+
+    def get_market_metrics(self, symbol: str) -> dict:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_market_metrics(symbol, code)
+
+    def get_dividends(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_dividends(symbol, code)
+
+    def get_repurchases(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_repurchases(symbol, code)
+
+    def get_employee_count(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_employee_count(symbol, code)
+
+    def get_financial_ratios(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_financial_ratios(symbol, code)

backend/app/fetchers/vn_fetcher.py

@@ -0,0 +1,57 @@
+
+import pandas as pd
+from .base import DataFetcher
+from app.clients.ifind_int_client import IFindIntClient
+
+class VnFetcher(DataFetcher):
+    """
+    Vietnam Market Fetcher using iFinD International Client.
+    """
+    def __init__(self, api_key: str):
+        super().__init__(api_key)
+        self.data_source = 'iFinD'
+        self.client = IFindIntClient(api_key, 'VN')
+        
+    def _get_ifind_code(self, symbol: str) -> str:
+        # Vietnam symbols are usually just uppercase tickers like 'VIC'
+        # iFinD might need a suffix like .VN or .HO depending on exchange
+        # Legacy VnFetcher didn't append suffix, assumes user provides full or iFinD smart match
+        # But wait, let's checking legacy code might be safer.
+        # Assuming simple uppercase for now as per previous knowledge.
+        return symbol.upper()
+
+    def _fetch_basic_info(self, symbol: str):
+        code = self._get_ifind_code(symbol)
+        return self.client._fetch_basic_info(symbol, code)
+
+    def get_income_statement(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_income_statement(symbol, code)
+
+    def get_balance_sheet(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_balance_sheet(symbol, code)
+
+    def get_cash_flow(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_cash_flow(symbol, code)
+
+    def get_market_metrics(self, symbol: str) -> dict:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_market_metrics(symbol, code)
+
+    def get_dividends(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_dividends(symbol, code)
+
+    def get_repurchases(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_repurchases(symbol, code)
+    
+    def get_employee_count(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_employee_count(symbol, code)
+
+    def get_financial_ratios(self, symbol: str) -> pd.DataFrame:
+        code = self._get_ifind_code(symbol)
+        return self.client.get_financial_ratios(symbol, code)

backend/app/legacy/__init__.py

@@ -0,0 +1 @@
+"""Legacy module for backward compatibility"""

backend/app/legacy/database_old.py

@@ -0,0 +1,68 @@
+"""
+DEPRECATED: This file is deprecated and will be removed in future versions.
+
+This module uses SQLite and the old database schema.
+Please use `database.py` for the new PostgreSQL-based architecture.
+
+Migration path:
+- Old: SQLite + CSV files + HTML reports
+- New: PostgreSQL + JSON API + Frontend rendering
+
+See QUICKSTART.md for usage of the new architecture.
+"""
+
+from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
+from sqlalchemy import text
+from app.models import Base
+import os
+from dotenv import load_dotenv
+import warnings
+
+# Issue deprecation warning
+warnings.warn(
+    "database_old.py is deprecated. Use database.py for the new architecture.",
+    DeprecationWarning,
+    stacklevel=2
+)
+
+load_dotenv()
+
+DATABASE_URL = os.getenv("DATABASE_URL", "sqlite+aiosqlite:///./stock_analysis.db")
+
+engine = create_async_engine(DATABASE_URL, echo=True)
+AsyncSessionLocal = async_sessionmaker(engine, expire_on_commit=False)
+
+async def init_db():
+    async with engine.begin() as conn:
+        await conn.run_sync(Base.metadata.create_all)
+        
+        # Migration: Add ai_model column if it doesn't exist (for SQLite)
+        try:
+            await conn.execute(
+                text("ALTER TABLE reports ADD COLUMN ai_model VARCHAR(100) DEFAULT 'gemini-2.0-flash-exp'")
+            )
+            print("Migration: Added ai_model column to reports table")
+        except Exception as e:
+            # Column already exists or other error
+            if "duplicate column" not in str(e).lower() and "already exists" not in str(e).lower():
+                print(f"Migration check: {e}")
+
+        # Migration: Add token columns to report_sections
+        columns_to_add = [
+            ("prompt_tokens", "INTEGER DEFAULT 0"),
+            ("completion_tokens", "INTEGER DEFAULT 0"),
+            ("total_tokens", "INTEGER DEFAULT 0")
+        ]
+        
+        for col_name, col_type in columns_to_add:
+            try:
+                await conn.execute(text(f"ALTER TABLE report_sections ADD COLUMN {col_name} {col_type}"))
+                print(f"Migration: Added {col_name} to report_sections table")
+            except Exception as e:
+                # SQLite error for duplicate column usually contains "duplicate column name"
+                if "duplicate column" not in str(e).lower() and "already exists" not in str(e).lower():
+                     print(f"Migration check for {col_name}: {e}")
+
+async def get_db():
+    async with AsyncSessionLocal() as session:
+        yield session

backend/app/legacy/models_old.py

@@ -0,0 +1,61 @@
+"""
+DEPRECATED: This file is deprecated. Use models.py instead.
+
+This module contains the old SQLite-based ORM models.
+The new architecture uses PostgreSQL with updated models in models.py.
+"""
+
+from sqlalchemy import Column, Integer, String, Text, DateTime, ForeignKey, Enum
+from sqlalchemy.orm import relationship, Mapped, mapped_column, DeclarativeBase
+from sqlalchemy.sql import func
+import enum
+import datetime
+import warnings
+
+warnings.warn(
+    "models_old.py is deprecated. Use models.py for the new architecture.",
+    DeprecationWarning,
+    stacklevel=2
+)
+
+class Base(DeclarativeBase):
+    pass
+
+class AnalysisStatus(str, enum.Enum):
+    PENDING = "pending"
+    IN_PROGRESS = "in_progress"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+class Report(Base):
+    __tablename__ = "reports"
+
+    id: Mapped[int] = mapped_column(primary_key=True, index=True)
+    market: Mapped[str] = mapped_column(String(10), index=True)
+    symbol: Mapped[str] = mapped_column(String(20), index=True)
+    company_name: Mapped[str] = mapped_column(String(200))
+    status: Mapped[AnalysisStatus] = mapped_column(Enum(AnalysisStatus), default=AnalysisStatus.PENDING)
+    ai_model: Mapped[str] = mapped_column(String(100), nullable=True, default="gemini-2.0-flash")
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
+    
+    sections: Mapped[list["ReportSection"]] = relationship(back_populates="report", cascade="all, delete-orphan")
+
+class ReportSection(Base):
+    __tablename__ = "report_sections"
+
+    id: Mapped[int] = mapped_column(primary_key=True, index=True)
+    report_id: Mapped[int] = mapped_column(ForeignKey("reports.id"))
+    section_name: Mapped[str] = mapped_column(String(50)) # e.g. company_profile, fundamental_analysis
+    content: Mapped[str] = mapped_column(Text) # Markdown content
+    total_tokens: Mapped[int] = mapped_column(Integer, nullable=True, default=0)
+    prompt_tokens: Mapped[int] = mapped_column(Integer, nullable=True, default=0)
+    completion_tokens: Mapped[int] = mapped_column(Integer, nullable=True, default=0)
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
+
+    report: Mapped["Report"] = relationship(back_populates="sections")
+
+class Setting(Base):
+    __tablename__ = "settings"
+
+    key: Mapped[str] = mapped_column(String(50), primary_key=True)
+    value: Mapped[str] = mapped_column(Text)

backend/app/legacy/schemas.py

@@ -0,0 +1,57 @@
+"""
+Legacy Schemas - 用于向后兼容
+
+这些是旧架构的 Pydantic 模型，仅用于 legacy API routes。
+新代码请使用 schemas.py 中的模型。
+"""
+
+from pydantic import BaseModel
+from typing import Optional, List
+from datetime import datetime
+from enum import Enum
+
+class AnalysisStatus(str, Enum):
+    PENDING = "pending"
+    IN_PROGRESS = "in_progress"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+class StockSearchRequest(BaseModel):
+    query: str
+
+class StockSearchResponse(BaseModel):
+    market: str
+    symbol: str
+    company_name: str
+
+class AnalysisRequest(BaseModel):
+    market: str
+    symbol: str
+    company_name: str
+    model: Optional[str] = None
+    data_source: Optional[str] = None
+
+class ReportSectionSchema(BaseModel):
+    section_name: str
+    content: str
+    created_at: datetime
+    
+    class Config:
+        from_attributes = True
+
+class ReportResponse(BaseModel):
+    id: int
+    market: str
+    symbol: str
+    company_name: str
+    status: AnalysisStatus
+    ai_model: Optional[str] = None
+    created_at: datetime
+    sections: List[ReportSectionSchema] = []
+
+    class Config:
+        from_attributes = True
+
+class ConfigUpdateRequest(BaseModel):
+    key: str
+    value: str

backend/app/main.py

@@ -1,37 +1,235 @@
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-from app.api import routes
-import os
-from dotenv import load_dotenv
 from contextlib import asynccontextmanager
-from app.database import init_db
+import os
+import sys
+from pathlib import Path
+from dotenv import load_dotenv
+import logging
+
+# 配置日志系统 - 在最开始配置,确保所有模块都能使用
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler("server.log", encoding='utf-8')
+    ]
+)
+
+# Ensure 'src' directory is in python path for 'storage' module imports
+# This is required because fetchers use 'from storage.file_io import ...'
+ROOT_DIR = Path(__file__).resolve().parent.parent.parent
+SRC_DIR = ROOT_DIR / "src"
+if str(SRC_DIR) not in sys.path:
+    sys.path.insert(0, str(SRC_DIR))
+
+# 导入新路由
+from app.api import data_routes, analysis_routes
 
 load_dotenv()
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    # Initialize DB on startup
+    # 新架构使用 AsyncSession，不需要显式初始化
-    await init_db()
     yield
 
-app = FastAPI(title="Stock Analysis API", lifespan=lifespan)
+app = FastAPI(
+    title="FA3 Stock Analysis API",
+    version="2.0.0",
+    description="架构重构后的股票分析 API",
+    lifespan=lifespan
+)
 
 # Configure CORS
-origins = [
-    "http://localhost:3000",
-    "http://127.0.0.1:3000",
-]
-
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"], # For development convenience
+    allow_origins=["*"],  # For development convenience
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-app.include_router(routes.router, prefix="/api")
+# 挂载新的 API 路由
+app.include_router(data_routes.router, prefix="/api")
+app.include_router(analysis_routes.router, prefix="/api")
 
 @app.get("/")
 def read_root():
-    return {"status": "ok", "message": "Stock Analysis API is running"}
+    return {
+        "status": "ok",
+        "message": "FA3 Stock Analysis API v2.0",
+        "architecture": "refactored",
+        "endpoints": {
+            "data": "/api/data/*",
+            "analysis": "/api/analysis/*",
+            "config": "/api/config"
+        },
+        "docs": "/docs"
+    }
+
+@app.get("/health")
+def health_check():
+    return {
+        "status": "healthy",
+        "database": "fa3",
+        "version": "2.0.0"
+    }
+
+# 配置端点
+from fastapi import Request, Depends
+from app.database import get_db
+from app.models import Setting
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select
+from pydantic import BaseModel
+
+class ConfigUpdateRequest(BaseModel):
+    key: str
+    value: str
+
+@app.get("/api/config")
+async def get_config_compat(db: AsyncSession = Depends(get_db)):
+    """获取配置（从 PostgreSQL）"""
+    try:
+        result = await db.execute(select(Setting))
+        settings = result.scalars().all()
+        if settings:
+            return {setting.key: setting.value for setting in settings}
+        # 返回默认配置
+        return {
+            "ai_model": "gemini-2.0-flash",
+            "data_source_cn": "Tushare",
+            "data_source_hk": "iFinD",
+            "data_source_us": "iFinD",
+            "data_source_jp": "iFinD",
+            "data_source_vn": "iFinD"
+        }
+    except Exception as e:
+        # 如果表不存在，返回默认配置
+        print(f"Config read error (using defaults): {e}")
+        return {
+            "ai_model": "gemini-2.0-flash",
+            "data_source_cn": "Tushare",
+            "data_source_hk": "iFinD",
+            "data_source_us": "iFinD",
+            "data_source_jp": "iFinD",
+            "data_source_vn": "iFinD"
+        }
+
+@app.post("/api/config")
+async def update_config_compat(
+    request: ConfigUpdateRequest,
+    db: AsyncSession = Depends(get_db)
+):
+    """更新配置（到 PostgreSQL）"""
+    try:
+        result = await db.execute(
+            select(Setting).where(Setting.key == request.key)
+        )
+        setting = result.scalar_one_or_none()
+        
+        if setting:
+            setting.value = request.value
+        else:
+            setting = Setting(key=request.key, value=request.value)
+            db.add(setting)
+        
+        await db.commit()
+        return {"status": "ok", "key": request.key, "value": request.value}
+    except Exception as e:
+        # 如果数据库操作失败，至少返回成功状态（配置会保存在前端）
+        print(f"Config update error: {e}")
+        return {"status": "ok", "key": request.key, "value": request.value, "note": "saved in memory only"}
+
+# 搜索端点
+from app.services.analysis_service import get_genai_client
+import json
+import logging
+import time
+
+logger = logging.getLogger(__name__)
+
+class StockSearchRequest(BaseModel):
+    query: str
+    model: str = "gemini-2.0-flash"  # 支持前端传入模型参数
+
+class StockSearchResponse(BaseModel):
+    market: str
+    symbol: str
+    company_name: str
+
+@app.post("/api/search", response_model=list[StockSearchResponse])
+async def search_stock(request: StockSearchRequest):
+    """使用 AI 搜索股票"""
+    logger.info(f"🔍 [搜索] 开始搜索股票: {request.query}")
+    start_time = time.time()
+    
+    try:
+        from google.genai import types
+        client = get_genai_client()
+        
+        prompt = f"""请利用Google搜索查找 "{request.query}" 对应的上市股票信息。
+        
+返回最匹配的股票，优先返回准确匹配的公司。
+        
+返回格式必须是 JSON 数组，每个元素包含：
+- market: 市场代码（CH/HK/US/JP/VN），例如：腾讯是 HK，茅台是 CH，英伟达是 US。
+- symbol: 股票代码（如果是CH，通常是6位数字；HK是5位；US是字母）。
+- company_name: 公司简称。
+
+示例：
+[{{"market": "HK", "symbol": "00700", "company_name": "腾讯控股"}}]
+
+请直接返回 JSON，不要添加任何其他文字。最多返回5个结果。"""
+
+        # 启用 Google Search Grounding
+        grounding_tool = types.Tool(google_search=types.GoogleSearch())
+        
+        # 使用请求中的模型，默认为 gemini-2.5-flash
+        model_name = request.model or "gemini-2.5-flash"
+        
+        logger.info(f"🤖 [搜索-LLM] 调用 {model_name} 进行股票搜索")
+        llm_start = time.time()
+
+        response = client.models.generate_content(
+            model=model_name,
+            contents=prompt,
+            config=types.GenerateContentConfig(
+                tools=[grounding_tool],
+                temperature=0.1
+            )
+        )
+        
+        llm_elapsed = time.time() - llm_start
+        usage = response.usage_metadata
+        prompt_tokens = usage.prompt_token_count if usage else 0
+        completion_tokens = usage.candidates_token_count if usage else 0
+        total_tokens = prompt_tokens + completion_tokens
+        
+        logger.info(f"✅ [搜索-LLM] 模型响应完成, 耗时: {llm_elapsed:.2f}秒, Tokens: prompt={prompt_tokens}, completion={completion_tokens}, total={total_tokens}")
+        
+        # 解析响应
+        text = response.text.strip()
+        # 移除可能的 markdown 代码块标记
+        if text.startswith("```json"):
+            text = text[7:]
+        if text.startswith("```"):
+            text = text[3:]
+        if text.endswith("```"):
+            text = text[:-3]
+        text = text.strip()
+        
+        results = json.loads(text)
+        
+        total_elapsed = time.time() - start_time
+        logger.info(f"✅ [搜索] 搜索完成, 找到 {len(results)} 个结果, 总耗时: {total_elapsed:.2f}秒")
+        
+        return results
+        
+    except Exception as e:
+        elapsed = time.time() - start_time
+        logger.error(f"❌ [搜索] 搜索失败: {e}, 耗时: {elapsed:.2f}秒")
+        print(f"Search error: {e}")
+        # 返回空结果而不是错误，避免前端崩溃
+        return []

backend/app/models.py

@@ -1,47 +1,121 @@
-from sqlalchemy import Column, Integer, String, Text, DateTime, ForeignKey, Enum
+"""
-from sqlalchemy.orm import relationship, Mapped, mapped_column, DeclarativeBase
+SQLAlchemy Models for FA3 Refactored Architecture
+新架构的数据库 ORM 模型
+"""
+from sqlalchemy import Column, Integer, String, Text, TIMESTAMP, Boolean, JSON, ForeignKey
+from sqlalchemy.orm import declarative_base, relationship
 from sqlalchemy.sql import func
-import enum
-import datetime
 
-class Base(DeclarativeBase):
+Base = declarative_base()
-    pass
 
-class AnalysisStatus(str, enum.Enum):
+class Company(Base):
-    PENDING = "pending"
+    """公司元数据表"""
-    IN_PROGRESS = "in_progress"
+    __tablename__ = 'companies'
-    COMPLETED = "completed"
-    FAILED = "failed"
-
-class Report(Base):
-    __tablename__ = "reports"
-
-    id: Mapped[int] = mapped_column(primary_key=True, index=True)
-    market: Mapped[str] = mapped_column(String(10), index=True)
-    symbol: Mapped[str] = mapped_column(String(20), index=True)
-    company_name: Mapped[str] = mapped_column(String(200))
-    status: Mapped[AnalysisStatus] = mapped_column(Enum(AnalysisStatus), default=AnalysisStatus.PENDING)
-    ai_model: Mapped[str] = mapped_column(String(100), nullable=True, default="gemini-2.0-flash")
-    created_at: Mapped[datetime.datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
     
-    sections: Mapped[list["ReportSection"]] = relationship(back_populates="report", cascade="all, delete-orphan")
+    id = Column(Integer, primary_key=True)
+    market = Column(String(10), nullable=False)
+    symbol = Column(String(50), nullable=False)
+    company_name = Column(String(255), nullable=False)
+    created_at = Column(TIMESTAMP, server_default=func.now())
+    updated_at = Column(TIMESTAMP, server_default=func.now(), onupdate=func.now())
+    
+    # 关系
+    data_updates = relationship("DataUpdate", back_populates="company", cascade="all, delete-orphan")
+    ai_analyses = relationship("AIAnalysis", back_populates="company", cascade="all, delete-orphan")
+    data_source_availability = relationship("DataSourceAvailability", back_populates="company", cascade="all, delete-orphan")
+    
+    def __repr__(self):
+        return f"<Company(id={self.id}, market={self.market}, symbol={self.symbol}, name={self.company_name})>"
 
-class ReportSection(Base):
-    __tablename__ = "report_sections"
 
-    id: Mapped[int] = mapped_column(primary_key=True, index=True)
+class DataUpdate(Base):
-    report_id: Mapped[int] = mapped_column(ForeignKey("reports.id"))
+    """数据更新记录表"""
-    section_name: Mapped[str] = mapped_column(String(50)) # e.g. company_profile, fundamental_analysis
+    __tablename__ = 'data_updates'
-    content: Mapped[str] = mapped_column(Text) # Markdown content
+    
-    total_tokens: Mapped[int] = mapped_column(Integer, nullable=True, default=0)
+    id = Column(Integer, primary_key=True)
-    prompt_tokens: Mapped[int] = mapped_column(Integer, nullable=True, default=0)
+    company_id = Column(Integer, ForeignKey('companies.id', ondelete='CASCADE'), nullable=False)
-    completion_tokens: Mapped[int] = mapped_column(Integer, nullable=True, default=0)
+    data_source = Column(String(50), nullable=False)
-    created_at: Mapped[datetime.datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
+    update_type = Column(String(50), nullable=False)
+    status = Column(String(20), nullable=False)
+    started_at = Column(TIMESTAMP, server_default=func.now())
+    completed_at = Column(TIMESTAMP, nullable=True)
+    error_message = Column(Text, nullable=True)
+    
+    # 数据覆盖范围
+    data_start_date = Column(String(8), nullable=True)
+    data_end_date = Column(String(8), nullable=True)
+    
+    # 元数据 (JSONB)
+    fetched_tables = Column(JSON, nullable=True)
+    row_counts = Column(JSON, nullable=True)
+    
+    # 进度跟踪
+    progress_message = Column(String(255), nullable=True)
+    progress_percentage = Column(Integer, default=0)
+    
+    # 关系
+    company = relationship("Company", back_populates="data_updates")
+    
+    def __repr__(self):
+        return f"<DataUpdate(id={self.id}, company_id={self.company_id}, source={self.data_source}, status={self.status})>"
+
+
+class AIAnalysis(Base):
+    """AI 分析报告表"""
+    __tablename__ = 'ai_analyses'
+    
+    id = Column(Integer, primary_key=True)
+    company_id = Column(Integer, ForeignKey('companies.id', ondelete='CASCADE'), nullable=False)
+    data_source = Column(String(50), nullable=False)
+    ai_model = Column(String(100), nullable=False)
+    status = Column(String(20), nullable=False)
+    
+    # 分析内容 (Markdown 格式)
+    company_profile = Column(Text, nullable=True)
+    fundamental_analysis = Column(Text, nullable=True)
+    insider_analysis = Column(Text, nullable=True)
+    bullish_analysis = Column(Text, nullable=True)
+    bearish_analysis = Column(Text, nullable=True)
+    
+    # Token 使用情况
+    total_tokens = Column(Integer, default=0)
+    tokens_by_section = Column(JSON, nullable=True)
+    
+    created_at = Column(TIMESTAMP, server_default=func.now())
+    completed_at = Column(TIMESTAMP, nullable=True)
+    error_message = Column(Text, nullable=True)
+    
+    # 关系
+    company = relationship("Company", back_populates="ai_analyses")
+    
+    def __repr__(self):
+        return f"<AIAnalysis(id={self.id}, company_id={self.company_id}, source={self.data_source}, status={self.status})>"
+
+
+class DataSourceAvailability(Base):
+    """数据源可用性表"""
+    __tablename__ = 'data_source_availability'
+    
+    company_id = Column(Integer, ForeignKey('companies.id', ondelete='CASCADE'), primary_key=True)
+    data_source = Column(String(50), primary_key=True)
+    has_data = Column(Boolean, default=False)
+    last_update = Column(TIMESTAMP, nullable=True)
+    row_count = Column(Integer, default=0)
+    
+    # 关系
+    company = relationship("Company", back_populates="data_source_availability")
+    
+    def __repr__(self):
+        return f"<DataSourceAvailability(company_id={self.company_id}, source={self.data_source}, has_data={self.has_data})>"
 
-    report: Mapped["Report"] = relationship(back_populates="sections")
 
 class Setting(Base):
-    __tablename__ = "settings"
+    """系统配置表"""
-
+    __tablename__ = 'settings'
-    key: Mapped[str] = mapped_column(String(50), primary_key=True)
+    
-    value: Mapped[str] = mapped_column(Text)
+    key = Column(String(100), primary_key=True)
+    value = Column(Text, nullable=False)
+    updated_at = Column(TIMESTAMP, server_default=func.now(), onupdate=func.now())
+    
+    def __repr__(self):
+        return f"<Setting(key={self.key}, value={self.value[:50]})>"

backend/app/schemas.py

@@ -1,50 +1,178 @@
-from pydantic import BaseModel
+"""
-from typing import Optional, List
+Pydantic Schemas for FA3 Refactored Architecture
+API 请求和响应的数据模型
+"""
+from pydantic import BaseModel, Field
+from typing import Optional, Dict, List
 from datetime import datetime
-from enum import Enum
 
-class AnalysisStatus(str, Enum):
+#  =============================================================================
-    PENDING = "pending"
+# Company Schemas
-    IN_PROGRESS = "in_progress"
+# =============================================================================
-    COMPLETED = "completed"
+class CompanyBase(BaseModel):
-    FAILED = "failed"
-
-class StockSearchRequest(BaseModel):
-    query: str
-
-class StockSearchResponse(BaseModel):
     market: str
     symbol: str
     company_name: str
 
-class AnalysisRequest(BaseModel):
+class CompanyCreate(CompanyBase):
-    market: str
+    pass
-    symbol: str
-    company_name: str
-    model: Optional[str] = None
-    data_source: Optional[str] = None
 
-class ReportSectionSchema(BaseModel):
+class CompanyResponse(CompanyBase):
-    section_name: str
+    id: int
-    content: str
     created_at: datetime
+    updated_at: datetime
     
     class Config:
         from_attributes = True
 
-class ReportResponse(BaseModel):
+
-    id: int
+# =============================================================================
+# Data Check & Fetch Schemas
+# =============================================================================
+class DataCheckRequest(BaseModel):
+    market: str
+    symbol: str
+    data_source: str
+
+class DataCheckResponse(BaseModel):
+    has_data: bool
+    company_id: Optional[int] = None
+    data_source: str
+    last_update: Optional[Dict] = None
+    message: Optional[str] = None
+
+class FetchDataRequest(BaseModel):
     market: str
     symbol: str
     company_name: str
-    status: AnalysisStatus
+    data_source: str
-    ai_model: Optional[str] = None
+    force_refresh: bool = False
-    created_at: datetime
-    sections: List[ReportSectionSchema] = []
 
+class FetchDataResponse(BaseModel):
+    update_id: int
+    data_source: str
+    status: str
+    message: str
+
+
+# =============================================================================
+# Data Update Schemas
+# =============================================================================
+class DataUpdateBase(BaseModel):
+    company_id: int
+    data_source: str
+    update_type: str
+    status: str
+
+class DataUpdateCreate(DataUpdateBase):
+    pass
+
+class DataUpdateResponse(DataUpdateBase):
+    id: int
+    started_at: datetime
+    completed_at: Optional[datetime] = None
+    error_message: Optional[str] = None
+    data_start_date: Optional[str] = None
+    data_end_date: Optional[str] = None
+    fetched_tables: Optional[List[str]] = None
+    row_counts: Optional[Dict[str, int]] = None
+    progress_message: Optional[str] = None
+    progress_percentage: Optional[int] = 0
+    
     class Config:
         from_attributes = True
 
+
+
+# =============================================================================
+# Financial Data Schemas
+# =============================================================================
+class FinancialDataResponse(BaseModel):
+    company: CompanyResponse
+    data_source: str
+    income_statement: List[Dict]
+    balance_sheet: List[Dict]
+    cash_flow: List[Dict]
+    daily_basic: List[Dict]
+    dividend: Optional[List[Dict]] = None
+    repurchase: Optional[List[Dict]] = None
+    employee: Optional[List[Dict]] = None
+    unified_data: Optional[List[Dict]] = None
+
+
+# =============================================================================
+# AI Analysis Schemas
+# =============================================================================
+class AnalysisStartRequest(BaseModel):
+    company_id: int
+    data_source: str
+    model: Optional[str] = "gemini-2.0-flash"
+
+class AnalysisStartResponse(BaseModel):
+    analysis_id: int
+    company_id: int
+    data_source: str
+    status: str
+    message: str
+
+class AnalysisStatusResponse(BaseModel):
+    id: int
+    company_id: int
+    data_source: str
+    ai_model: str
+    status: str
+    created_at: datetime
+    completed_at: Optional[datetime] = None
+    error_message: Optional[str] = None
+    
+    class Config:
+        from_attributes = True
+
+class AnalysisResultResponse(BaseModel):
+    id: int
+    company_id: int
+    data_source: str
+    ai_model: str
+    status: str
+    company_profile: Optional[str] = None
+    fundamental_analysis: Optional[str] = None
+    insider_analysis: Optional[str] = None
+    bullish_analysis: Optional[str] = None
+    bearish_analysis: Optional[str] = None
+    total_tokens: int
+    tokens_by_section: Optional[Dict[str, int]] = None
+    created_at: datetime
+    completed_at: Optional[datetime] = None
+    
+    class Config:
+        from_attributes = True
+
+
+# =============================================================================
+# Data Source Schemas
+# =============================================================================
+class DataSourceInfo(BaseModel):
+    source: str
+    available: bool
+    description: str
+    supported_markets: List[str]
+
+class DataSourceListResponse(BaseModel):
+    market: str
+    sources: List[DataSourceInfo]
+
+
+# =============================================================================
+# Configuration Schemas
+# =============================================================================
 class ConfigUpdateRequest(BaseModel):
     key: str
     value: str
+
+class ConfigResponse(BaseModel):
+    key: str
+    value: str
+    updated_at: datetime
+    
+    class Config:
+        from_attributes = True

backend/app/services/analysis_service.py

@@ -1,185 +1,195 @@
-import sys
+"""
+LLM 分析服务 (新架构)
+负责调用 LLM 生成分析报告
+"""
 import os
-import subprocess
+from typing import Dict, Optional
-import asyncio
-import json
 from sqlalchemy.ext.asyncio import AsyncSession
-from app.models import Report, ReportSection, AnalysisStatus
+from sqlalchemy import select
-from app.services import llm_engine
+from google import genai
-from datetime import datetime
-import google.genai as genai
 from google.genai import types
 
-async def search_stock(query: str, api_key: str, model: str = "gemini-2.0-flash"):
+from app.models import Company
-    if not api_key:
+from app.services.data_fetcher_service import get_financial_data_from_db
-        return {"error": "API Key not provided"}
 
-    client = genai.Client(api_key=api_key)
+# Gemini client（懒加载）
-    prompt = f"""
+_client: Optional[genai.Client] = None
-    你是一个专业的股票代码查询助手。请识别公司 '{query}' 的股票市场和代码。
+
-    
+def get_genai_client() -> genai.Client:
-    **重要提示**：
+    """获取 Gemini API 客户端（单例）"""
-    1. 用户输入可能是公司全称、简称、别名或股票代码
+    global _client
-    2. 请仔细匹配，优先完全匹配，避免返回不相关的公司
+    if _client is None:
-    3. 中国公司的简称经常省略"股份有限公司"等后缀
+        api_key = os.getenv('GEMINI_API_KEY')
-    4. 例如："茅台" = "贵州茅台酒股份有限公司" (600519.SH)
+        if not api_key:
-    
+            raise ValueError("GEMINI_API_KEY environment variable is not set")
-    请返回一个 JSON 数组，包含所有匹配的公司。每个对象包含以下字段：
+        _client = genai.Client(api_key=api_key)
-    - 'market': 'CN' (中国), 'US' (美国), 'HK' (香港), 'JP' (日本), 或 'VN' (越南) 之一
+    return _client
-    - 'symbol': 完整的股票代码 (例如 'AAPL', '600519.SH', '00700.HK', '688778.SH', '2503.T', 'SAB.HM')
+
-    - 'company_name': 公司的中文简称（如果有的话，优先使用中文；如果只有英文名，则使用英文名）
+
-    
+async def run_llm_analysis(
-    **匹配规则**：
+    company_id: int,
-    - 如果查询词与某公司简称、全称或股票代码完全匹配，返回该公司
+    data_source: str,
-    - 如果有多个可能的匹配，返回所有相关公司
+    model: str,
-    - 如果公司在中国但用户没有指定市场，默认为 CN（上海/深圳/北京交易所）
+    db: AsyncSession
-    - **越南股票后缀规则（iFinD数据源）**：
+) -> Dict:
-    - 胡志明交易所 (HOSE) -> 后缀 **.HM** (例如: VNM.HM, SAB.HM, VCB.HM)
-    - 河内交易所 (HNX) -> 后缀 **.HN** (例如: PVS.HN, SHS.HN)
-    - UPCoM 市场 -> iFinD 通常也使用 .HN 或 .HM，或者特定的 UPCoM 后缀，但绝不要使用 .VN (这是 Bloomberg 格式)
-    - 示例：MCH (Masan Consumer) -> MCH.HN 或 MCH.HM (UPCoM 在 iFinD 中可能归类不一，但 MCH.HN 是常见尝试，MCH.HM 也可以尝试。请根据搜索结果确认 iFinD 使用哪一个。**强烈倾向于使用 .HM 或 .HN，严禁使用 .VN**)
-    - MCH 具体案例：用户指出应为 MCH.HM (或 MCH.HN)，绝非 MCH.VN。请只返回 .HM 或 .HN。
-    - 如果不确定是 HM 还是 HN，优先返回 .HM。
-    - 如果完全没找到匹配，返回 {{ "error": "未找到相关公司" }}
-    
-    示例响应（单个结果）：
-    [
-        {{
-            "market": "CN",
-            "symbol": "600519.SH",
-            "company_name": "贵州茅台"
-        }}
-    ]
-    
-    示例响应（多个结果）：
-    [
-        {{
-            "market": "HK",
-            "symbol": "00700.HK",
-            "company_name": "腾讯控股"
-        }},
-        {{
-            "market": "VN",
-            "symbol": "VNM.HM",
-            "company_name": "Vinamilk"
-        }}
-    ]
-    
-    现在请处理查询: '{query}'
     """
+    执行 LLM 分析
+    
+    Args:
+        company_id: 公司ID
+        data_source: 数据源
+        model: LLM 模型名称
+        db: 数据库会话
+    
+    Returns:
+        包含所有分析内容和 token 使用情况的字典
+    """
+    # 1. 获取公司信息
+    result = await db.execute(
+        select(Company).where(Company.id == company_id)
+    )
+    company = result.scalar_one()
+    
+    # 2. 获取财务数据
+    financial_data = await get_financial_data_from_db(
+        company_id=company_id,
+        data_source=data_source,
+        db=db
+    )
+    
+    # 3. 准备数据上下文
+    context = format_financial_data_for_llm(financial_data)
+    
+    # 4. 调用 LLM 生成各个部分
+    sections = {}
+    total_tokens = 0
+    tokens_by_section = {}
+    
+    # 公司简介
+    profile_result = await generate_section(
+        model=model,
+        company=company,
+        section_name='company_profile',
+        context=context
+    )
+    sections['company_profile'] = profile_result['content']
+    tokens_by_section['company_profile'] = profile_result['tokens']
+    total_tokens += profile_result['tokens']
+    
+    # 基本面分析
+    fundamental_result = await generate_section(
+        model=model,
+        company=company,
+        section_name='fundamental_analysis',
+        context=context
+    )
+    sections['fundamental_analysis'] = fundamental_result['content']
+    tokens_by_section['fundamental_analysis'] = fundamental_result['tokens']
+    total_tokens += fundamental_result['tokens']
+    
+    # 内部人士分析
+    insider_result = await generate_section(
+        model=model,
+        company=company,
+        section_name='insider_analysis',
+        context=context
+    )
+    sections['insider_analysis'] = insider_result['content']
+    tokens_by_section['insider_analysis'] = insider_result['tokens']
+    total_tokens += insider_result['tokens']
+    
+    # 看涨分析
+    bullish_result = await generate_section(
+        model=model,
+        company=company,
+        section_name='bullish_analysis',
+        context=context
+    )
+    sections['bullish_analysis'] = bullish_result['content']
+    tokens_by_section['bullish_analysis'] = bullish_result['tokens']
+    total_tokens += bullish_result['tokens']
+    
+    # 看跌分析
+    bearish_result = await generate_section(
+        model=model,
+        company=company,
+        section_name='bearish_analysis',
+        context=context
+    )
+    sections['bearish_analysis'] = bearish_result['content']
+    tokens_by_section['bearish_analysis'] = bearish_result['tokens']
+    total_tokens += bearish_result['tokens']
+    
+    return {
+        **sections,
+        'total_tokens': total_tokens,
+        'tokens_by_section': tokens_by_section
+    }
+
+
+def format_financial_data_for_llm(financial_data: Dict) -> str:
+    """将财务数据格式化为 LLM 上下文"""
+    # 简化版本，实际应该更详细地格式化数据
+    context = f"""
+公司: {financial_data['company']['company_name']}
+市场: {financial_data['company']['market']}
+代码: {financial_data['company']['symbol']}
+数据源: {financial_data['data_source']}
+
+财务数据:
+- 利润表记录数: {len(financial_data.get('income_statement', []))}
+- 资产负债表记录数: {len(financial_data.get('balance_sheet', []))}
+- 现金流量表记录数: {len(financial_data.get('cash_flow', []))}
+- 估值数据记录数: {len(financial_data.get('daily_basic', []))}
+"""
+    return context
+
+
+async def generate_section(
+    model: str,
+    company: Company,
+    section_name: str,
+    context: str
+) -> Dict:
+    """
+    生成报告的一个部分
+    """
+    # 设置 prompt
+    prompts = {
+        'company_profile': f"请为 {company.company_name} ({company.symbol}) 生成公司简介，包括主营业务、市场地位等。\n\n财务数据:\n{context}",
+        'fundamental_analysis': f"请为 {company.company_name} 进行基本面分析，包括财务指标、盈利能力、成长性等。\n\n财务数据:\n{context}",
+        'insider_analysis': f"请分析 {company.company_name} 的内部人士交易情况、股权结构等。\n\n财务数据:\n{context}",
+        'bullish_analysis': f"请分析 {company.company_name} 的看涨因素和投资亮点。\n\n财务数据:\n{context}",
+        'bearish_analysis': f"请分析 {company.company_name} 的风险因素和潜在问题。\n\n财务数据:\n{context}"
+    }
+    
+    prompt = prompts.get(section_name, f"请分析 {company.company_name}。\n\n{context}")
     
     try:
-        # Enable Google Search for more accurate results
+        # 获取 Gemini client
-        grounding_tool = types.Tool(google_search=types.GoogleSearch())
+        client = get_genai_client()
-        # Note: Cannot use response_mime_type with tools
-        config = types.GenerateContentConfig(tools=[grounding_tool])
         
+        # 调用新的 genai API
         response = client.models.generate_content(
             model=model,
-            contents=prompt,
+            contents=prompt
-            config=config
         )
         
-        response_text = response.text.strip()
+        # 提取内容和 token 信息
-        print(f"Search API raw response: {response_text[:500]}")
+        content = response.text
         
-        # Extract JSON from response (may be wrapped in markdown code blocks)
+        # 获取 token 使用情况（新 API 的方式）
-        if "```json" in response_text:
+        tokens = 0
-            # Extract JSON from code block
+        if hasattr(response, 'usage_metadata'):
-            start = response_text.find("```json") + 7
+            tokens = response.usage_metadata.total_token_count
-            end = response_text.find("```", start)
-            json_str = response_text[start:end].strip()
-        elif "```" in response_text:
-            # Extract from generic code block
-            start = response_text.find("```") + 3
-            end = response_text.find("```", start)
-            json_str = response_text[start:end].strip()
-        else:
-            json_str = response_text
         
-        result = json.loads(json_str)
+        return {
-        
+            'content': content,
-        # Ensure result is always an array for consistent handling
+            'tokens': tokens
-        if not isinstance(result, list):
+        }
-            if isinstance(result, dict) and "error" in result:
-                return result  # Return error as-is
-            result = [result]  # Wrap single object in array
-        
-        return result
-    except json.JSONDecodeError as e:
-        print(f"JSON decode error: {e}, Response text: {response_text}")
-        return {"error": f"无法解析搜索结果: {str(e)}"}
     except Exception as e:
-        print(f"Search error: {e}")
+        # 如果 LLM 调用失败，返回错误信息
-        return {"error": f"搜索失败: {str(e)}"}
+        return {
-
+            'content': f"分析生成失败: {str(e)}",
-async def run_analysis_task(report_id: int, market: str, symbol: str, api_key: str, data_source: str = None):
+            'tokens': 0
-    """
+        }
-    Background task to run the full analysis pipeline.
-    Creates its own DB session.
-    """
-    print(f"Starting analysis for report {report_id}: {market} {symbol} (Source: {data_source})")
-    
-    # Create new session
-    from app.database import AsyncSessionLocal
-    
-    async with AsyncSessionLocal() as session:
-        try:
-            report = await session.get(Report, report_id)
-            if not report:
-                print(f"Report {report_id} not found in background task")
-                return
-            
-            report.status = AnalysisStatus.IN_PROGRESS
-            await session.commit()
-            
-            company_name_for_prompt = report.company_name
-            
-            # 2. Run Main Data Fetching Script (run_fetcher.py)
-            root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../"))
-            cmd = [sys.executable, "run_fetcher.py", market, symbol] 
-            if data_source:
-                cmd.extend(["--data-source", data_source]) 
-            
-            print(f"Executing data fetch command: {cmd} in {root_dir}")
-            process = await asyncio.create_subprocess_exec(
-                *cmd,
-                cwd=root_dir,
-                stdout=asyncio.subprocess.PIPE,
-                stderr=asyncio.subprocess.PIPE
-            )
-            stdout, stderr = await process.communicate()
-            
-            if process.returncode != 0:
-                error_msg = stderr.decode()
-                print(f"Data fetch failed: {error_msg}")
-                report.status = AnalysisStatus.FAILED
-                await session.commit()
-                return
-                
-            print("Data fetch successful.")
-            
-            # 3. Perform Analysis Logic
-            await llm_engine.process_analysis_steps(
-                report_id=report_id,
-                company_name=company_name_for_prompt, 
-                symbol=symbol,
-                market=market,
-                db=session,
-                api_key=api_key
-            )
-            
-            # 4. Finalize
-            report.status = AnalysisStatus.COMPLETED
-            await session.commit()
-            print(f"Analysis for report {report_id} completed.")
-            
-        except Exception as e:
-            print(f"Analysis task exception: {e}")
-            try:
-                report = await session.get(Report, report_id)
-                if report:
-                    report.status = AnalysisStatus.FAILED
-                    await session.commit()
-            except:
-                pass

backend/app/services/bloomberg_service.py

@@ -0,0 +1,108 @@
+"""
+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)
+        query = text("""
+            SELECT indicator, value, value_date, currency 
+            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
+            
+            if not v_date:
+                continue
+                
+            date_str = v_date.isoformat() if hasattr(v_date, 'isoformat') else str(v_date)
+            
+            if date_str not in data_by_date:
+                data_by_date[date_str] = {
+                    "end_date": date_str,
+                    "currency": curr
+                }
+            elif data_by_date[date_str].get("currency") is None and curr:
+                # If existing entry has no currency, but we have one now, update it
+                data_by_date[date_str]["currency"] = curr
+            
+            # 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[date_str][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 []

backend/app/services/data_fetcher_service.py

@@ -0,0 +1,528 @@
+"""
+数据获取服务层
+负责从不同数据源获取财务数据并存储到数据库
+"""
+import os
+from datetime import datetime
+from typing import Dict, List, Optional
+import pandas as pd
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, and_, text
+from sqlalchemy.orm import selectinload
+
+from app.models import Company, DataUpdate, DataSourceAvailability
+from app.schemas import DataCheckResponse, FetchDataRequest
+from app.fetchers.factory import FetcherFactory
+from app.services.bloomberg_service import get_bloomberg_data
+
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+def update_progress_sync(update_id: int, message: str, percentage: int):
+    """同步更新数据获取进度 - 使用psycopg2直连避免event loop冲突"""
+    import psycopg2
+    import os
+    
+    logger.info(f"🔄 [进度更新] ID={update_id}, Msg={message}, Progress={percentage}%")
+    
+    try:
+        conn = psycopg2.connect(
+            host=os.getenv('DB_HOST', '192.168.3.195'),
+            user=os.getenv('DB_USER', 'value'),
+            password=os.getenv('DB_PASSWORD', 'Value609!'),
+            dbname=os.getenv('DB_NAME', 'fa3'),
+            port=os.getenv('DB_PORT', '5432')
+        )
+        
+        cur = conn.cursor()
+        cur.execute(
+            """
+            UPDATE data_updates 
+            SET progress_message = %s, progress_percentage = %s
+            WHERE id = %s
+            """,
+            (message, percentage, update_id)
+        )
+        if cur.rowcount == 0:
+             logger.warning(f"⚠️ [进度更新] ID={update_id} 未找到记录!")
+        
+        conn.commit()
+        cur.close()
+        conn.close()
+    except Exception as e:
+        logger.error(f"❌ [进度更新失败] ID={update_id}: {e}", exc_info=True)
+
+
+
+async def check_data_status(
+    market: str,
+    symbol: str,
+    data_source: str,
+    db: AsyncSession
+) -> DataCheckResponse:
+    """
+    检查指定公司和数据源的数据状态
+    
+    Returns:
+        DataCheckResponse: 包含数据状态信息
+    """
+    # 1. 查询或创建公司记录
+    result = await db.execute(
+        select(Company).where(
+            and_(Company.market == market, Company.symbol == symbol)
+        )
+    )
+    company = result.scalar_one_or_none()
+    
+    if not company:
+        # 没有找到公司记录
+        return DataCheckResponse(
+            has_data=False,
+            company_id=None,
+            data_source=data_source,
+            message=f"该数据源暂无该公司数据"
+        )
+    
+    # 2. 查询最近一次成功的数据更新
+    result = await db.execute(
+        select(DataUpdate)
+        .where(
+            and_(
+                DataUpdate.company_id == company.id,
+                DataUpdate.data_source == data_source,
+                DataUpdate.status == 'completed'
+            )
+        )
+        .order_by(DataUpdate.completed_at.desc())
+        .limit(1)
+    )
+    latest_update = result.scalar_one_or_none()
+    
+    if not latest_update:
+        return DataCheckResponse(
+            has_data=False,
+            company_id=company.id,
+            data_source=data_source,
+            message="该数据源暂无该公司数据"
+        )
+    
+    # 3. 构建响应
+    return DataCheckResponse(
+        has_data=True,
+        company_id=company.id,
+        data_source=data_source,
+        last_update={
+            "date": latest_update.completed_at.isoformat(),
+            "data_start_date": latest_update.data_start_date,
+            "data_end_date": latest_update.data_end_date,
+            "table_counts": latest_update.row_counts or {}
+        }
+    )
+
+
+async def create_or_get_company(
+    market: str,
+    symbol: str,
+    company_name: str,
+    db: AsyncSession
+) -> Company:
+    """创建或获取公司记录"""
+    result = await db.execute(
+        select(Company).where(
+            and_(Company.market == market, Company.symbol == symbol)
+        )
+    )
+    company = result.scalar_one_or_none()
+    
+    if not company:
+        company = Company(
+            market=market,
+            symbol=symbol,
+            company_name=company_name
+        )
+        db.add(company)
+        await db.commit()
+        await db.refresh(company)
+    
+    return company
+
+
+async def create_data_update_record(
+    company_id: int,
+    data_source: str,
+    update_type: str,
+    db: AsyncSession
+) -> DataUpdate:
+    """创建数据更新记录"""
+    data_update = DataUpdate(
+        company_id=company_id,
+        data_source=data_source,
+        update_type=update_type,
+        status='in_progress'
+    )
+    db.add(data_update)
+    await db.commit()
+    await db.refresh(data_update)
+    return data_update
+
+
+async def update_data_update_record(
+    update_id: int,
+    status: str,
+    db: AsyncSession,
+    completed_at: Optional[datetime] = None,
+    error_message: Optional[str] = None,
+    data_start_date: Optional[str] = None,
+    data_end_date: Optional[str] = None,
+    fetched_tables: Optional[List[str]] = None,
+    row_counts: Optional[Dict[str, int]] = None
+):
+    """更新数据更新记录"""
+    result = await db.execute(
+        select(DataUpdate).where(DataUpdate.id == update_id)
+    )
+    data_update = result.scalar_one()
+    
+    data_update.status = status
+    if completed_at:
+        data_update.completed_at = completed_at
+    if error_message:
+        data_update.error_message = error_message
+    if data_start_date:
+        data_update.data_start_date = data_start_date
+    if data_end_date:
+        data_update.data_end_date = data_end_date
+    if fetched_tables:
+        data_update.fetched_tables = fetched_tables
+    if row_counts:
+        data_update.row_counts = row_counts
+    
+    await db.commit()
+    await db.refresh(data_update)
+    return data_update
+
+
+def fetch_financial_data_sync(
+    company_id: int,
+    market: str,
+    symbol: str,
+    data_source: str,
+    update_id: int
+):
+    """
+    同步方式获取财务数据（在后台任务中调用）
+    
+    此函数实际执行数据获取，使用同步的 Fetcher
+    """
+    try:
+        # 0. 初始化
+        update_progress_sync(update_id, "正在初始化数据获取...", 0)
+        
+        # 格式化股票代码 - CN市场需要添加.SH或.SZ后缀
+        formatted_symbol = symbol
+        if market.upper() == 'CN' and data_source == 'Tushare':
+            if '.' not in symbol:
+                # 判断是上海还是深圳
+                if symbol.startswith('6'):
+                    formatted_symbol = f"{symbol}.SH"
+                elif symbol.startswith(('0', '3', '2')):
+                    formatted_symbol = f"{symbol}.SZ"
+                else:
+                    formatted_symbol = f"{symbol}.SH"  # 默认上海
+                
+                import logging
+                logger = logging.getLogger(__name__)
+                logger.info(f"📝 [数据获取] 股票代码格式化: {symbol} -> {formatted_symbol}")
+        
+        # 1. 获取对应的 Fetcher
+        # 1. 获取对应的 Fetcher
+        fetcher = FetcherFactory.get_fetcher(market, data_source)
+        update_progress_sync(update_id, "数据源连接成功", 10)
+        
+        # 2. 触发数据同步
+        # 对于 Bloomberg，我们只需要触发一次全量同步
+        if data_source == 'Bloomberg':
+            update_progress_sync(update_id, "正在连接 Bloomberg 终端...", 20)
+            
+            # 定义进度回调
+            def progress_callback(msg, pct):
+                # 映射内部进度 (0-100) 到总体进度 (20-90)
+                mapped_pct = 20 + int(pct * 0.7)
+                update_progress_sync(update_id, msg, mapped_pct)
+            
+            # 使用新的 sync_all_data 方法（将在 fetcher 中实现）
+            if hasattr(fetcher, 'sync_all_data'):
+                # 检查 sync_all_data 是否接受 progress_callback 参数
+                import inspect
+                sig = inspect.signature(fetcher.sync_all_data)
+                if 'progress_callback' in sig.parameters:
+                    fetcher.sync_all_data(formatted_symbol, progress_callback=progress_callback)
+                else:
+                    fetcher.sync_all_data(formatted_symbol)
+            else:
+                # 兼容旧代码，虽然有了 sync_all_data 后这部分应该不需要了
+                fetcher.get_income_statement(formatted_symbol)
+            
+            update_progress_sync(update_id, "Bloomberg 数据同步完成", 100)
+            
+        else:
+            # 对于其他数据源，保持原有逻辑但简化日志
+            update_progress_sync(update_id, "正在获取财务报表数据...", 30)
+            fetcher.get_income_statement(formatted_symbol)
+            fetcher.get_balance_sheet(formatted_symbol)
+            fetcher.get_cash_flow(formatted_symbol)
+            fetcher.get_market_metrics(formatted_symbol)
+            
+            # 尝试获取辅助数据
+            try:
+                fetcher.get_dividends(symbol)
+                fetcher.get_repurchases(symbol)
+                fetcher.get_employee_count(symbol)
+            except Exception:
+                pass # 忽略辅助数据获取错误
+                
+            update_progress_sync(update_id, "数据同步完成", 100)
+
+        result_data = {
+            'status': 'completed',
+            'completed_at': datetime.now(),
+            'fetched_tables': ['unified_data'] if data_source == 'Bloomberg' else ['income_statement', 'balance_sheet', 'cash_flow', 'daily_basic'],
+            # 不再统计行数
+            'row_counts': {}
+        }
+        return result_data
+        
+    except Exception as e:
+        update_progress_sync(update_id, f"数据获取失败: {str(e)}", 0)
+        return {
+            'status': 'failed',
+            'error_message': str(e)
+        }
+
+
+async def get_financial_data_from_db(
+    company_id: int,
+    data_source: str,
+    db: AsyncSession
+) -> Dict:
+    """
+    从数据库读取财务数据
+    
+    Args:
+        company_id: 公司ID
+        data_source: 数据源 (iFinD, Bloomberg, Tushare)
+        db: 数据库会话
+    
+    Returns:
+        包含所有财务数据的字典
+    """
+    # 获取公司信息
+    result = await db.execute(
+        select(Company).where(Company.id == company_id)
+    )
+    company = result.scalar_one()
+    
+    # 准备返回数据结构
+    response_data = {
+        "company": {
+            "id": company.id,
+            "market": company.market,
+            "symbol": company.symbol,
+            "company_name": company.company_name,
+            "created_at": company.created_at,
+            "updated_at": company.updated_at
+        },
+        "data_source": data_source,
+        "income_statement": [],
+        "balance_sheet": [],
+        "cash_flow": [],
+        "daily_basic": [],
+        "dividend": [],
+        "repurchase": [],
+        "employee": [],
+        "unified_data": []
+    }
+    
+    # 根据数据源读取数据
+    if data_source == 'Tushare' and company.market == 'CN':
+        # 定义表映射
+        tables = {
+            "income_statement": "tushare_income_statement",
+            "balance_sheet": "tushare_balance_sheet",
+            "cash_flow": "tushare_cash_flow",
+            "daily_basic": "tushare_daily_basic",
+            "dividend": "tushare_dividend",
+            "repurchase": "tushare_repurchase",
+            "employee": "tushare_stock_company" # Tushare 员工信息通常在公司信息表中
+        }
+        
+        # 辅助函数：读取表数据
+        # from sqlalchemy import text (moved to top)
+        
+        async def fetch_table_data(table_name, symbol_code, order_by=None):
+            try:
+                # 检查表是否存在
+                check_sql = text("SELECT to_regclass(:table_name)")
+                exists = await db.execute(check_sql, {"table_name": f"public.{table_name}"})
+                if not exists.scalar():
+                    return []
+                
+                # 查询数据
+                query = f"SELECT * FROM {table_name} WHERE ts_code = :ts_code"
+                if order_by:
+                    query += f" ORDER BY {order_by} DESC"
+                query += " LIMIT 100" # 限制返回数量
+                
+                result = await db.execute(text(query), {"ts_code": symbol_code})
+                
+                # 转换为字典列表，处理日期格式
+                data = []
+                for row in result.mappings():
+                    item = dict(row)
+                    # 处理日期对象为字符串
+                    for k, v in item.items():
+                        if isinstance(v, datetime):
+                            item[k] = v.isoformat()
+                    data.append(item)
+                return data
+            except Exception as e:
+                print(f"Error fetching from {table_name}: {e}")
+                return []
+
+        # Tushare 使用 ts_code (symbol)
+        ts_code = company.symbol # 假设 symbol 已经是 ts_code 格式 (e.g. 300750.SZ)
+        # 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
+        # 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
+        # 在前端传递 symbol 时通常是 6 位数字，后端 TushareClient 用它去查询
+        # 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）
+        # 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)
+        
+        # 修正：Tushare 存储时用的是 ts_code (如 000001.SZ)。
+        # 但我们这里的 company.symbol 可能是没有后缀的 (000001)。
+        # 我们应该用 LIKE 查询或者获取存储时的 ts_code。
+        # 为简单起见，先尝试模糊匹配
+        
+        async def fetch_table_data_fuzzy(table_name, symbol_code, order_by=None):
+            try:
+                check_sql = text("SELECT to_regclass(:table_name)")
+                exists = await db.execute(check_sql, {"table_name": f"public.{table_name}"})
+                if not exists.scalar(): return []
+                
+                query = f"SELECT * FROM {table_name} WHERE ts_code LIKE :ts_code"
+                if order_by: query += f" ORDER BY {order_by} DESC"
+                query += " LIMIT 50"
+                
+                # 尝试匹配 symbol% (如 300750%)
+                result = await db.execute(text(query), {"ts_code": f"{symbol_code}%"})
+                
+                data = []
+                for row in result.mappings():
+                    item = dict(row)
+                    for k, v in item.items():
+                        if hasattr(v, 'isoformat'): item[k] = v.isoformat()
+                    data.append(item)
+                return data
+            except Exception as e:
+                print(f"Error fetching from {table_name}: {e}")
+                return []
+
+        # 并行或串行获取数据
+        response_data["income_statement"] = await fetch_table_data_fuzzy(tables["income_statement"], company.symbol, "end_date")
+        response_data["balance_sheet"] = await fetch_table_data_fuzzy(tables["balance_sheet"], company.symbol, "end_date")
+        response_data["cash_flow"] = await fetch_table_data_fuzzy(tables["cash_flow"], company.symbol, "end_date")
+        
+        # 日频数据
+        response_data["daily_basic"] = await fetch_table_data_fuzzy(tables["daily_basic"], company.symbol, "trade_date")
+        
+        # 其他数据
+        response_data["dividend"] = await fetch_table_data_fuzzy(tables["dividend"], company.symbol, "end_date")
+        response_data["repurchase"] = await fetch_table_data_fuzzy(tables["repurchase"], company.symbol, "ann_date")
+    
+    elif data_source == 'Bloomberg':
+        try:
+            # 使用独立的 Bloomberg 服务读取数据
+            unified_data = await get_bloomberg_data(company, db)
+            
+            response_data["unified_data"] = unified_data
+            response_data["income_statement"] = []
+                
+        except Exception as e:
+            logger.error(f"Error fetching Bloomberg data from stockcard: {e}", exc_info=True)
+
+    return response_data
+
+
+def get_available_data_sources(market: str) -> List[Dict]:
+    """
+    获取指定市场的可用数据源
+    
+    Returns:
+        数据源列表，每个包含 source, available, description, supported_markets
+    """
+    all_sources = {
+        'iFinD': {
+            'description': 'iFinD (同花顺) 数据源',
+            'supported_markets': ['HK', 'JP', 'US', 'VN']
+        },
+        'Tushare': {
+            '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: str,
+    db: AsyncSession,
+    limit: int = 20
+) -> List[Dict]:
+    """获取指定数据源下最近更新的公司"""
+    query = (
+        select(Company, DataUpdate.completed_at)
+        .join(DataUpdate, Company.id == DataUpdate.company_id)
+        .where(
+            and_(
+                DataUpdate.data_source == data_source,
+                DataUpdate.status == 'completed'
+            )
+        )
+        .order_by(DataUpdate.completed_at.desc())
+        .limit(limit * 3) # Fetch more to handle duplicates
+    )
+    
+    result = await db.execute(query)
+    rows = result.all()
+    
+    seen_ids = set()
+    companies = []
+    
+    for row in rows:
+        company, completed_at = 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,
+                "last_update": completed_at.strftime('%Y-%m-%d %H:%M') if completed_at else ""
+            })
+            if len(companies) >= limit:
+                break
+                
+    return companies

backend/app/services/llm_engine.py

@@ -7,6 +7,9 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.future import select
 from app.models import Report, ReportSection, Setting
 import asyncio
+import logging
+
+logger = logging.getLogger(__name__)
 
 async def load_prompts(db: AsyncSession, prompt_dir: str):
     prompts = {}
@@ -42,6 +45,9 @@ async def load_prompts(db: AsyncSession, prompt_dir: str):
 async def call_llm(api_key: str, model_name: str, system_prompt: str, user_prompt: str, context: str, enable_search: bool = True):
     full_prompt = f"{system_prompt}\n\n{user_prompt}\n\nExisting Report Data for context:\n{context}"
     
+    logger.info(f"🤖 [LLM] 开始调用模型: {model_name}, 启用搜索: {enable_search}")
+    start_time = time.time()
+    
     client = genai.Client(api_key=api_key)
     
     config_params = {}
@@ -63,6 +69,10 @@ async def call_llm(api_key: str, model_name: str, system_prompt: str, user_promp
         usage = response.usage_metadata
         prompt_tokens = usage.prompt_token_count if usage else 0
         completion_tokens = usage.candidates_token_count if usage else 0
+        total_tokens = prompt_tokens + completion_tokens
+        
+        elapsed = time.time() - start_time
+        logger.info(f"✅ [LLM] 模型响应完成, 耗时: {elapsed:.2f}秒, Tokens: prompt={prompt_tokens}, completion={completion_tokens}, total={total_tokens}")
         
         return {
             "text": response.text,
@@ -125,7 +135,9 @@ async def process_analysis_steps(report_id: int, company_name: str, symbol: str,
     
     # Prepare all API calls concurrently
     async def process_section(key: str, name: str):
-        print(f"Processing {name}...")
+        logger.info(f"📝 [LLM] 开始处理章节: {name}")
+        section_start = time.time()
+        
         prompt_template = prompts.get(key)
         if not prompt_template:
             return None
@@ -141,6 +153,9 @@ async def process_analysis_steps(report_id: int, company_name: str, symbol: str,
             
         result = await call_llm(api_key, model_name, system_content, user_content, current_data_context, enable_search=True)
         
+        section_elapsed = time.time() - section_start
+        logger.info(f"✅ [LLM] 章节 {name} 处理完成, 总耗时: {section_elapsed:.2f}秒")
+        
         return (key, result)
     
     # Run all sections concurrently

backend/scripts/fix_data_updates_schema.py

@@ -0,0 +1,55 @@
+
+import os
+import sys
+import psycopg2
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load .env
+ROOT_DIR = Path(__file__).resolve().parent.parent.parent
+load_dotenv(ROOT_DIR / ".env")
+
+def get_db_connection():
+    db_host = os.getenv("DB_HOST", "192.168.3.195")
+    db_user = os.getenv("DB_USER", "value")
+    db_pass = os.getenv("DB_PASSWORD", "Value609!")
+    db_name = os.getenv("DB_NAME", "fa3")
+    db_port = os.getenv("DB_PORT", "5432")
+
+    return psycopg2.connect(
+        host=db_host, user=db_user, password=db_pass, dbname=db_name, port=db_port
+    )
+
+def fix_schema():
+    print("🔧 Fixing data_updates table schema...")
+    try:
+        conn = get_db_connection()
+        conn.autocommit = True
+        with conn.cursor() as cur:
+            # 1. Alter status column to TEXT to avoid varchar(8) limits
+            print("Altering status column to TEXT...")
+            cur.execute("ALTER TABLE data_updates ALTER COLUMN status TYPE TEXT;")
+            
+            # 2. Check if error_message needs widening too
+            print("Altering error_message column to TEXT...")
+            cur.execute("ALTER TABLE data_updates ALTER COLUMN error_message TYPE TEXT;")
+            
+            # 3. Ensure other columns are safe
+            # fetched_tables and row_counts should be JSONB or TEXT
+            # Just in case they are restricted varchar
+            print("Altering fetched_tables and row_counts to TEXT...")
+            cur.execute("ALTER TABLE data_updates ALTER COLUMN fetched_tables TYPE TEXT;")
+            cur.execute("ALTER TABLE data_updates ALTER COLUMN row_counts TYPE TEXT;")
+            
+            # 4. Fix date columns that might also be varchar(8)
+            print("Altering data_start_date and data_end_date to TEXT...")
+            cur.execute("ALTER TABLE data_updates ALTER COLUMN data_start_date TYPE TEXT;")
+            cur.execute("ALTER TABLE data_updates ALTER COLUMN data_end_date TYPE TEXT;")
+            
+        print("✅ Schema fixed successfully.")
+        conn.close()
+    except Exception as e:
+        print(f"❌ Error fixing schema: {e}")
+
+if __name__ == "__main__":
+    fix_schema()

backend/scripts/reset_all_int_tables.py

@@ -0,0 +1,53 @@
+
+import os
+import sys
+import psycopg2
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load .env
+ROOT_DIR = Path(__file__).resolve().parent.parent.parent
+load_dotenv(ROOT_DIR / ".env")
+
+def get_db_connection():
+    db_host = os.getenv("DB_HOST", "192.168.3.195")
+    db_user = os.getenv("DB_USER", "value")
+    db_pass = os.getenv("DB_PASSWORD", "Value609!")
+    db_name = os.getenv("DB_NAME", "fa3")
+    db_port = os.getenv("DB_PORT", "5432")
+
+    return psycopg2.connect(
+        host=db_host, user=db_user, password=db_pass, dbname=db_name, port=db_port
+    )
+
+def drop_tables():
+    tables = [
+        "ifind_int_income_statement",
+        "ifind_int_balance_sheet",
+        "ifind_int_cash_flow",
+        "ifind_int_daily_basic",
+        "ifind_int_financial_ratios",
+        "ifind_int_dividend",
+        "ifind_int_repurchase",
+        "ifind_int_employee"
+    ]
+    
+    conn = get_db_connection()
+    conn.autocommit = True
+    cursor = conn.cursor()
+    
+    print("🧹 Starting cleanup of iFinD International tables...")
+    try:
+        for table in tables:
+            print(f"Dropping {table}...")
+            cursor.execute(f"DROP TABLE IF EXISTS {table}")
+            print(f"✅ Dropped {table}")
+    except Exception as e:
+        print(f"❌ Error during cleanup: {e}")
+    finally:
+        cursor.close()
+        conn.close()
+        print("🎉 Cleanup completed.")
+
+if __name__ == "__main__":
+    drop_tables()

backend/scripts/reset_tables.py

@@ -0,0 +1,36 @@
+
+import os
+import sys
+import psycopg2
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load .env
+ROOT_DIR = Path(__file__).resolve().parent.parent.parent
+load_dotenv(ROOT_DIR / ".env")
+
+def get_db_connection():
+    db_host = os.getenv("DB_HOST", "192.168.3.195")
+    db_user = os.getenv("DB_USER", "value")
+    db_pass = os.getenv("DB_PASSWORD", "Value609!")
+    db_name = os.getenv("DB_NAME", "fa3")
+    db_port = os.getenv("DB_PORT", "5432")
+
+    return psycopg2.connect(
+        host=db_host, user=db_user, password=db_pass, dbname=db_name, port=db_port
+    )
+
+def drop_table(table_name):
+    print(f"Dropping table {table_name}...")
+    try:
+        conn = get_db_connection()
+        conn.autocommit = True
+        with conn.cursor() as cur:
+            cur.execute(f"DROP TABLE IF EXISTS {table_name}")
+        print(f"✅ Dropped {table_name}")
+        conn.close()
+    except Exception as e:
+        print(f"❌ Error dropping {table_name}: {e}")
+
+if __name__ == "__main__":
+    drop_table("ifind_int_daily_basic")

backend/scripts/verify_migration.py

@@ -0,0 +1,66 @@
+
+import sys
+import os
+import pandas as pd
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Add backend to path
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+
+from app.fetchers.factory import FetcherFactory
+from app.clients.ifind_hk_client import IFindHKClient 
+
+# Load .env
+ROOT_DIR = Path(__file__).resolve().parent.parent.parent
+load_dotenv(ROOT_DIR / ".env")
+
+def verify_fetcher(market, symbol, source, method_name='get_market_metrics'):
+    print(f"\n--- Verifying {market} ({source}) for {symbol} ---")
+    try:
+        fetcher = FetcherFactory.get_fetcher(market, source)
+        print(f"✅ Fetcher instantiated: {type(fetcher).__name__}")
+        
+        if method_name == 'get_market_metrics':
+            print(f"Calling {method_name}...")
+            data = fetcher.get_market_metrics(symbol)
+            print(f"Result: {data}")
+            if data and isinstance(data, dict) and data.get('price') != 0:
+                 print("✅ Data looks valid (Price > 0)")
+            else:
+                 print("⚠️ Data might be empty or zero (Market closed or invalid symbol?)")
+                 
+        elif method_name == 'get_income_statement':
+            print(f"Calling {method_name}...")
+            df = fetcher.get_income_statement(symbol)
+            if not df.empty:
+                print(f"✅ Got DataFrame with shape: {df.shape}")
+                print(df.head(2))
+            else:
+                print("⚠️ Returned empty DataFrame")
+
+    except Exception as e:
+        print(f"❌ Error: {e}")
+
+def main():
+    print("Start Verification...")
+    
+    # 1. HK (iFinD) - Tencent
+    verify_fetcher('HK', '0700', 'iFinD', 'get_market_metrics')
+    
+    # 2. JP (iFinD) - Toyota
+    verify_fetcher('JP', '7203', 'iFinD', 'get_market_metrics')
+    
+    # 3. US (iFinD) - Apple
+    verify_fetcher('US', 'AAPL', 'iFinD', 'get_market_metrics')
+    
+    # 4. VN (iFinD) - VIC? (Vingroup)
+    verify_fetcher('VN', 'VIC', 'iFinD', 'get_market_metrics')
+    
+    # 5. Bloomberg - US - AAPL (Skip if no connection, but try)
+    # verify_fetcher('US', 'AAPL', 'Bloomberg', 'get_market_metrics') # Enabling for test
+    
+    print("\nVerification Script Completed.")
+
+if __name__ == "__main__":
+    main()

data/CN/300750.SZ/historical_metrics.csv

@@ -1,14 +0,0 @@
-date_str,Price,PE,PB,MarketCap,Shareholders
-20250930,402.0,36.1375,6.3157,1833784274046.0,226368.0
-20241231,266.0,26.5478,4.9395,1171322077828.0,212061.0
-20240930,251.89,25.1333,5.64,1108914407264.0,229043.0
-20231231,163.26,23.3715,3.9816,718187472189.0,260992.0
-20221231,393.42,60.3173,6.3972,960934064032.0,183317.0
-20211231,588.0,245.4697,18.7433,1370540505600.0,141963.0
-20201231,351.11,179.3523,13.3304,817901625971.0,133060.0
-20191231,106.4,69.3745,6.3603,234973728079.99997,76710.0
-20181231,73.8,41.7726,5.015,161992284120.0,82514.0
-20171231,,,,,
-20161231,,,,,
-20151231,,,,,
-20141231,,,,,

data/CN/300750.SZ/raw_balance_sheet.csv

@@ -1,61 +0,0 @@
-ts_code,ann_date,f_ann_date,end_date,report_type,comp_type,end_type,total_share,cap_rese,undistr_porfit,surplus_rese,special_rese,money_cap,trad_asset,notes_receiv,accounts_receiv,oth_receiv,prepayment,div_receiv,int_receiv,inventories,amor_exp,nca_within_1y,sett_rsrv,loanto_oth_bank_fi,premium_receiv,reinsur_receiv,reinsur_res_receiv,pur_resale_fa,oth_cur_assets,total_cur_assets,fa_avail_for_sale,htm_invest,lt_eqt_invest,invest_real_estate,time_deposits,oth_assets,lt_rec,fix_assets,cip,const_materials,fixed_assets_disp,produc_bio_assets,oil_and_gas_assets,intan_assets,r_and_d,goodwill,lt_amor_exp,defer_tax_assets,decr_in_disbur,oth_nca,total_nca,cash_reser_cb,depos_in_oth_bfi,prec_metals,deriv_assets,rr_reins_une_prem,rr_reins_outstd_cla,rr_reins_lins_liab,rr_reins_lthins_liab,refund_depos,ph_pledge_loans,refund_cap_depos,indep_acct_assets,client_depos,client_prov,transac_seat_fee,invest_as_receiv,total_assets,lt_borr,st_borr,cb_borr,depos_ib_deposits,loan_oth_bank,trading_fl,notes_payable,acct_payable,adv_receipts,sold_for_repur_fa,comm_payable,payroll_payable,taxes_payable,int_payable,div_payable,oth_payable,acc_exp,deferred_inc,st_bonds_payable,payable_to_reinsurer,rsrv_insur_cont,acting_trading_sec,acting_uw_sec,non_cur_liab_due_1y,oth_cur_liab,total_cur_liab,bond_payable,lt_payable,specific_payables,estimated_liab,defer_tax_liab,defer_inc_non_cur_liab,oth_ncl,total_ncl,depos_oth_bfi,deriv_liab,depos,agency_bus_liab,oth_liab,prem_receiv_adva,depos_received,ph_invest,reser_une_prem,reser_outstd_claims,reser_lins_liab,reser_lthins_liab,indept_acc_liab,pledge_borr,indem_payable,policy_div_payable,total_liab,treasury_share,ordin_risk_reser,forex_differ,invest_loss_unconf,minority_int,total_hldr_eqy_exc_min_int,total_hldr_eqy_inc_min_int,total_liab_hldr_eqy,lt_payroll_payable,oth_comp_income,oth_eqt_tools,oth_eqt_tools_p_shr,lending_funds,acc_receivable,st_fin_payable,payables,hfs_assets,hfs_sales,cost_fin_assets,fair_value_fin_assets,contract_assets,contract_liab,accounts_receiv_bill,accounts_pay,oth_rcv_total,fix_assets_total,cip_total,oth_pay_total,long_pay_total,debt_invest,oth_debt_invest,update_flag
-300750.SZ,20251021,20251021,20250930,1,1,3,4562854001.0,156365576000.0,151715049000.0,2272701000.0,38434000.0,324241586000.0,43260528000.0,418837000.0,66481235000.0,,13557167000.0,75434000.0,,80211558000.0,,98616000.0,,,,,,,10578550000.0,574228137000.0,,,59831799000.0,,,,193099000.0,128622702000.0,37365635000.0,,,,,15025388000.0,,890939000.0,4908611000.0,28542552000.0,,23937431000.0,321853995000.0,,,,36492000.0,,,,,,,,,,,,,896082131000.0,78441925000.0,15314463000.0,,,,,82967668000.0,131958477000.0,,,,21821691000.0,8851546000.0,,98000000.0,,,,,,,,,25005358000.0,4985363000.0,340940328000.0,8422115000.0,,,85843047000.0,2261098000.0,25224182000.0,4739536000.0,208129732000.0,,,,,,,,,,,,,,,,,549070060000.0,7097993000.0,,,,32764095000.0,314247976000.0,347012071000.0,896082131000.0,,6391356000.0,,,,,,,,,,,398446000.0,40678197000.0,66900072000.0,214926145000.0,2862290000.0,128622702000.0,37365635000.0,9357565000.0,1519965000.0,,,1
-300750.SZ,20251021,20251021,20250930,1,1,3,4562854000.0,156365576000.0,151715049000.0,2272701000.0,38434000.0,324241586000.0,43260528000.0,418837000.0,66481235000.0,,13557167000.0,75434000.0,,80211558000.0,,98616000.0,,,,,,,10578550000.0,574228137000.0,,,59831799000.0,,,,193099000.0,128622702000.0,37365635000.0,,,,,15025388000.0,,890939000.0,4908611000.0,28542552000.0,,23937431000.0,321853995000.0,,,,36492000.0,,,,,,,,,,,,,896082131000.0,78441925000.0,15314463000.0,,,,,82967668000.0,131958477000.0,,,,21821691000.0,8851546000.0,,98000000.0,,,,,,,,,25005358000.0,4985363000.0,340940328000.0,8422115000.0,,,85843047000.0,2261098000.0,25224182000.0,4739536000.0,208129732000.0,,,,,,,,,,,,,,,,,549070060000.0,7097993000.0,,,,32764095000.0,314247976000.0,347012071000.0,896082131000.0,,6391356000.0,,,,,,,,,,,398446000.0,40678197000.0,66900072000.0,214926145000.0,2862290000.0,128622702000.0,37365635000.0,9357565000.0,1519965000.0,,,0
-300750.SZ,20250731,20250731,20250630,1,1,2,4559310311.0,154314344000.0,137146982000.0,2272701000.0,32301000.0,350577746000.0,22447277000.0,314830000.0,63800375000.0,2223575000.0,9752799000.0,78013000.0,0.0,72272139000.0,,88019000.0,,,,,,,9410883000.0,567700337000.0,,,57636957000.0,,,,136825000.0,118696651000.0,35086190000.0,,,,,14684829000.0,,892909000.0,4301351000.0,26965253000.0,,25414858000.0,299481094000.0,,,,,,,,,,,,,,,,,867181431000.0,82416083000.0,19009443000.0,,,,,76968647000.0,133420147000.0,,,,20115797000.0,8643847000.0,1000.0,1107999000.0,10373854000.0,,,,,,,,26031681000.0,3506536000.0,336005110000.0,11970602000.0,1640002000.0,0.0,80137122000.0,1140246000.0,23683078000.0,4976542000.0,206786909000.0,,185496000.0,,,,,,,,,,,,,,,542792019000.0,4262590000.0,,,,29466392000.0,294923020000.0,324389412000.0,867181431000.0,,859971000.0,,,,,,,,,,,346467000.0,36641662000.0,64115205000.0,210388794000.0,2301588000.0,118696651000.0,35086190000.0,11481854000.0,1640002000.0,,,0
-300750.SZ,20250415,20250415,20250331,1,1,1,4403394911.0,116857798000.0,140632845000.0,2194779000.0,48242000.0,321323654000.0,21421660000.0,168477000.0,60182430000.0,,7417713000.0,65098000.0,,65639666000.0,,78040000.0,,,,,,,7769187000.0,530298544000.0,,,56483298000.0,,,,176608000.0,112142665000.0,35221700000.0,,,,,14496931000.0,,893898000.0,4373265000.0,25303240000.0,,25136769000.0,289798725000.0,,,,,,,,,,,,,,,,,820097269000.0,84826153000.0,15782433000.0,,,,,79844165000.0,128662495000.0,,,,20320028000.0,9561191000.0,,,,,,,,,,,23013434000.0,2866697000.0,328208754000.0,11914211000.0,,,73507580000.0,1209610000.0,23847636000.0,5215845000.0,202749160000.0,,899889000.0,,,,,,,,,,,,,,,530957915000.0,2711392000.0,,,,27581708000.0,261557647000.0,289139355000.0,820097269000.0,,131980000.0,,,,,,,,,,,261473000.0,37088532000.0,60350907000.0,208506660000.0,2125282000.0,112142665000.0,35221700000.0,10169890000.0,1656337000.0,,,1
-300750.SZ,20250315,20250315,20241231,1,1,4,4403466458.0,116756136000.0,126601541000.0,2194779000.0,35551000.0,303511993000.0,14282253000.0,130403000.0,64135510000.0,2141729000.0,5969685000.0,65217000.0,1000.0,59835533000.0,,72972000.0,,,,,,,6286465000.0,510142089000.0,,,54791525000.0,,,,151342000.0,112589053000.0,29754703000.0,,,,,14419804000.0,,894757000.0,4593980000.0,24118834000.0,,19275483000.0,276516035000.0,,,,,,,,,,,,,,,,,786658123000.0,81238456000.0,19696282000.0,,,,,67356323000.0,130977408000.0,,,,18653079000.0,9436442000.0,0.0,5400161000.0,10761762000.0,,,,,,,,22881417000.0,2058196000.0,317171534000.0,11922623000.0,1606480000.0,0.0,71926943000.0,1231236000.0,22041069000.0,5400795000.0,196030416000.0,,2116017000.0,,,,,,,,,,,,,,,513201949000.0,2712804000.0,,,,26526141000.0,246930033000.0,273456174000.0,786658123000.0,,-348637000.0,,,,,,,,,,,400626000.0,27834446000.0,64265913000.0,198333731000.0,2206947000.0,112589053000.0,29754703000.0,16161923000.0,1606480000.0,,,0
-300750.SZ,20241019,20241019,20240930,1,1,3,4402375669.0,115137443600.0,117181223700.0,2192566200.0,33103400.0,264675795100.0,22002410300.0,292441400.0,66702709300.0,,6887337600.0,504261300.0,,55215275300.0,,200635500.0,,,,,,,6577524400.0,466764516700.0,,,54175331200.0,,,,128324700.0,110653484500.0,25200973600.0,,,,,14448153400.0,,883700900.0,4528856500.0,22121062800.0,,22428362600.0,271470487700.0,,,,,,,,,,,,,,,,,738235004400.0,85444436200.0,15239389800.0,,,,,63651540000.0,123140013700.0,,,,17368871900.0,8144202500.0,,22722000.0,,,,,,,,,16081511700.0,2926818100.0,281070698000.0,12104785500.0,,,65430208700.0,1180577400.0,21899574000.0,5492450500.0,193863663000.0,,2499377200.0,,,,,,,,,,,,,,,474934361000.0,2776131700.0,,,,26344412200.0,236956231200.0,263300643400.0,738235004400.0,,785650400.0,,,,,,,,,,,363142300.0,22651661600.0,66995150700.0,186791553700.0,3444015600.0,110653484500.0,25200973600.0,9367311600.0,1594834100.0,,,0
-300750.SZ,20240727,20240727,20240630,1,1,2,4398807222.0,88643885800.0,104031988700.0,2192566200.0,21079900.0,255002207100.0,17300865500.0,228380300.0,58099476000.0,2869209400.0,7826466800.0,1246594600.0,,48050676200.0,,230227700.0,,,,,,,7030728600.0,442116937300.0,,,52121325000.0,,,,145385900.0,113142792200.0,27257067100.0,,,,,15718068700.0,,890937400.0,5000170700.0,20892871600.0,,21694056900.0,273134951500.0,,,,,,,,,,,,,,,,,715251888900.0,88819915800.0,18048659700.0,,,,,66052159100.0,116979636700.0,,,,16596608800.0,6093742000.0,,24643100.0,10590190600.0,,,,,,,,7123836300.0,2496093000.0,270831777000.0,19414874600.0,1651515800.0,,59165819700.0,1090238200.0,21242202400.0,32547613200.0,224580485000.0,,4363213700.0,,,,,,,,,,,,,,,495412262100.0,2511358500.0,,,,23608483400.0,196231143400.0,219839626800.0,715251888900.0,,-545826100.0,,,,,,,,,,,284662400.0,22462994100.0,58327856300.0,183031795800.0,4115804000.0,113142792200.0,27257067100.0,10614833600.0,1651515800.0,,,0
-300750.SZ,20240416,20240416,20240331,1,1,1,4399041236.0,88213595700.0,113775363800.0,2192566200.0,12724600.0,288572136900.0,166802200.0,817044100.0,51186897200.0,,7886550900.0,426713900.0,,43979311300.0,,251362300.0,,,,,,,7332498400.0,459955986900.0,,,50586279900.0,,,,191694400.0,114498533800.0,26216109200.0,,,,,15581536000.0,,887894800.0,4958130700.0,19434638000.0,,22482818500.0,271330494700.0,,,,,,,,,,,,,,,,,731286481500.0,88444675700.0,16535118900.0,,,,,72631731700.0,112507178900.0,,,,16630284100.0,12277242700.0,,6975500.0,,,,,,,,,8263102400.0,2669102500.0,278962389700.0,19236444600.0,,,57296406400.0,1241289000.0,21767081900.0,31866021200.0,221855700200.0,,4754753700.0,,,,,,,,,,,,,,,500818089800.0,1966347800.0,,,,22738517700.0,207729874000.0,230468391700.0,731286481500.0,,1102930300.0,,,,,,,,,,,261354300.0,20599513300.0,52003941300.0,185138910600.0,3240165600.0,114498533800.0,26216109200.0,12094361500.0,1670358000.0,,,1
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-300750.SZ,20190824,20190824,20190630,1,1,2,2194445000.0,20966274995.41,11305749304.2,985878418.69,,32556486370.73,58980000.0,9444111464.19,8193892175.59,824534244.85,1029818178.9,2452287.27,177853201.6,9623692125.75,,,,,,,,,1233554253.66,63881882652.5,,,947416921.82,,,,,14883961290.06,3506528066.14,,,,,1555293801.94,,100419270.78,316656012.08,1736570054.02,,1425909205.95,26006748934.49,,,,736508349.96,,,,,,,,,,,,,89888631586.99,3638729122.03,1294039502.4,,,,314247518.1,17059577651.6,10098811857.43,7534909203.04,,,1317233803.46,803066668.27,22426699.31,311692470.8,2664652254.88,,,,,,,,830307415.47,,42250965044.76,,925232995.13,,4313623912.01,58389832.13,635800994.99,,9571776856.29,,,,,,,,,,,,,,,,,51822741901.05,773581200.0,,,,3488400716.48,34577488969.46,38065889685.94,89888631586.99,,-101277548.84,,,,,,,,,,,,,17638003639.78,27158389509.03,1004839733.72,14883961290.06,3506528066.14,2998771424.99,925232995.13,,,0
-300750.SZ,20190427,20190427,20190331,1,1,1,2195017400.0,21452320895.65,10562239859.25,985878418.69,,32224951522.9,37900000.0,9162783167.49,7933064764.17,,1153320036.04,,182452713.88,7686537622.67,,,,,,,,,870074607.47,60520697494.05,,,1047709787.58,,,,,12718283064.26,4002146735.88,,,,,1331981184.25,,100419270.78,320180603.51,1481495508.21,,1008321047.6,23580574965.47,,,,652958907.43,,,,,,,,,,,,,84101272459.52,3394334761.13,1403970598.49,,,,314247518.1,,,5404891136.69,,,1336293223.26,1122299621.77,20963013.91,,,,,,,,,,990251685.25,,38597161467.45,,,,3507541924.93,41726802.59,626870612.25,,8505238976.51,,,,,,,,,,,,,,,,,47102400443.96,793701060.0,,,,2772478669.16,34226393346.4,36998872015.56,84101272459.52,,-175362167.19,,,,,,,,,,,,,17095847931.66,24124867219.59,799106865.88,12718283064.26,4002146735.88,3900340464.3,934764875.61,,,0
-300750.SZ,20190427,20190427,20190331,1,1,1,2195017400.0,21452320895.65,10562239859.25,985878418.69,,32224951522.9,37900000.0,9162783167.49,7933064764.17,,1153320036.04,,182452713.88,7686537622.67,,,,,,,,,870074607.47,60520697494.05,,,1047709787.58,,,,,12718283064.26,4002146735.88,,,,,1331981184.25,,100419270.78,320180603.51,1481495508.21,,1008321047.6,23580574965.47,,,,652958907.43,,,,,,,,,,,,,84101272459.52,3394334761.13,1403970598.49,,,,314247518.1,,,5404891136.69,,,1336293223.26,1122299621.77,20963013.91,,,,,,,,,,990251685.25,,38597161467.45,,,,3507541924.93,41726802.59,626870612.25,,8505238976.51,,,,,,,,,,,,,,,,,47102400443.96,793701060.0,,,,2772478669.16,34226393346.4,36998872015.56,84101272459.52,,-175362167.19,,,,,,,,,,,,,17095847931.66,24124867219.59,799106865.88,12718283064.26,4002146735.88,3900340464.3,934764875.61,,,1
-300750.SZ,20190425,20190425,20181231,1,1,4,2195017400.0,21372918712.25,9515006632.3,985878418.69,,27731189739.92,,9742890628.44,6224857396.53,589281314.94,864640798.47,,92808117.05,7076101849.47,,,,,,,,,1076991664.73,53911422755.37,1516521098.2,,965198180.81,,,,,11574665757.11,1623838222.94,,,,,1346171137.42,,100419270.78,305828515.4,1240737742.63,,1298901335.85,19972281261.14,,,,512661245.82,,,,,,,,,,,,,73883704016.51,3490767815.96,1180092100.11,,,,314247518.1,11841128076.55,7057075077.4,4994400867.91,,,1122253456.83,722536564.72,19842845.23,,2904341329.33,,,,,,,,929024032.37,,31084941868.55,,943414523.31,,2512382681.52,40984489.33,611042047.22,,7598591557.34,,,,,,,,,,,,,,,,,38683533425.89,793701060.0,,,,2261889695.31,32938280895.31,35200170590.62,73883704016.51,,-336839207.93,,,,,,,,,,,,,15967748024.97,18898203153.95,682089431.99,11574665757.11,1623838222.94,2924184174.56,943414523.31,,,0
-300750.SZ,20190425,20190425,20181231,1,1,4,2195017400.0,21372918712.25,9515006632.3,985878418.69,,27731189739.92,,9742890628.44,6224857396.53,589281314.94,864640798.47,,92808117.05,7076101849.47,,,,,,,,,1076991664.73,53911422755.37,1516521098.2,,965198180.81,,,,,11574665757.11,1623838222.94,,,,,1346171137.42,,100419270.78,305828515.4,1240737742.63,,1298901335.85,19972281261.14,,,,512661245.82,,,,,,,,,,,,,73883704016.51,3490767815.96,1180092100.11,,,,314247518.1,11841128076.55,7057075077.4,4994400867.91,,,1122253456.83,722536564.72,19842845.23,,2904341329.33,,,,,,,,929024032.37,,31084941868.55,,943414523.31,,2512382681.52,40984489.33,611042047.22,,7598591557.34,,,,,,,,,,,,,,,,,38683533425.89,793701060.0,,,,2261889695.31,32938280895.31,35200170590.62,73883704016.51,,-336839207.93,,,,,,,,,,,,,15967748024.97,18898203153.95,682089431.99,11574665757.11,1623838222.94,2924184174.56,943414523.31,,,1
-300750.SZ,20181026,20181026,20180930,1,1,3,2195017400.0,21332400491.65,8854096176.97,638253676.69,,23009873374.81,,4105020050.57,9849744045.57,,453947445.22,,,5286472060.07,,,,,,,,,1368390591.89,45173587344.33,1749328770.25,,984646309.7,,,,,10313102145.17,2612119399.87,,,,,1362641804.84,,100419270.78,176881170.51,902206805.13,,415681574.9,18617027251.15,,,,416495571.63,,,,,,,,,,,,,63790614595.48,3245327066.37,1248978938.95,,,,,,,1704582797.09,,,596544931.31,705588018.28,,,,,,,,,,,826658774.41,,21825445438.14,,,,2821540586.26,42052130.71,360701916.84,,7437101080.2,,,,,,,,,,,,,,,,,29262546518.34,793701060.0,,,,2226764875.78,32301303201.36,34528068077.14,63790614595.48,,75236516.05,,,,,,,,,,,,,13954764096.14,15096667354.42,683644204.57,10313102145.17,2612119399.87,1646424623.68,967479380.02,,,1
-300750.SZ,20181026,20181026,20180930,1,1,3,2195017400.0,21332400491.65,8854096176.97,638253676.69,,23009873374.81,,4105020050.57,9849744045.57,,453947445.22,,,5286472060.07,,,,,,,,,1368390591.89,45173587344.33,1749328770.25,,984646309.7,,,,,10313102145.17,2612119399.87,,,,,1362641804.84,,100419270.78,176881170.51,902206805.13,,415681574.9,18617027251.15,,,,416495571.63,,,,,,,,,,,,,63790614595.48,3245327066.37,1248978938.95,,,,,,,1704582797.09,,,596544931.31,705588018.28,,,,,,,,,,,826658774.41,,21825445438.14,,,,2821540586.26,42052130.71,360701916.84,,7437101080.2,,,,,,,,,,,,,,,,,29262546518.34,793701060.0,,,,2226764875.78,32301303201.36,34528068077.14,63790614595.48,,75236516.05,,,,,,,,,,,,,13954764096.14,15096667354.42,683644204.57,10313102145.17,2612119399.87,1646424623.68,967479380.02,,,0
-300750.SZ,20180824,20180824,20180630,1,1,2,2172437000.0,20520157651.05,7415859086.9,638253676.69,,17800002418.59,,2571239620.97,8528717101.73,474148632.84,678003502.29,,64274152.08,4919942324.71,,,,,,,,,2632034286.33,37691162710.81,1729158500.0,,913752605.6,,,,,9819507470.69,2645070047.6,,,,,1384785545.17,,100419270.78,158460787.9,708272329.32,,293403603.49,17752830160.55,,,,22800671.27,,,,,,,,,,,,,55443992871.36,3194631603.51,1401956157.12,,,,,6675843483.7,5740618051.51,349064690.97,,,524449445.7,271459638.63,16260284.94,,283008992.25,,,,,,,,785188697.21,,16047849442.03,,899399594.33,,2018155174.56,34549547.01,425180664.33,,6571916583.74,,,,,,,,,,,,,,,,,22619766025.77,,,,,2005370552.71,30818856292.88,32824226845.59,55443992871.36,,72148878.24,,,,,,,,,,,,,11099956722.7,12416461535.21,538422784.92,9819507470.69,2645070047.6,299269277.19,899399594.33,,,1
-300750.SZ,20180824,20180824,20180630,1,1,2,2172437000.0,20520157651.05,7415859086.9,638253676.69,,17800002418.59,,2571239620.97,8528717101.73,474148632.84,678003502.29,,64274152.08,4919942324.71,,,,,,,,,2632034286.33,37691162710.81,1729158500.0,,913752605.6,,,,,9819507470.69,2645070047.6,,,,,1384785545.17,,100419270.78,158460787.9,708272329.32,,293403603.49,17752830160.55,,,,22800671.27,,,,,,,,,,,,,55443992871.36,3194631603.51,1401956157.12,,,,,6675843483.7,5740618051.51,349064690.97,,,524449445.7,271459638.63,16260284.94,,283008992.25,,,,,,,,785188697.21,,16047849442.03,,899399594.33,,2018155174.56,34549547.01,425180664.33,,6571916583.74,,,,,,,,,,,,,,,,,22619766025.77,,,,,2005370552.71,30818856292.88,32824226845.59,55443992871.36,,72148878.24,,,,,,,,,,,,,11099956722.7,12416461535.21,538422784.92,9819507470.69,2645070047.6,299269277.19,899399594.33,,,0
-300750.SZ,20180522,20180522,20180331,1,1,1,1955193267.0,15374442887.79,6918251958.12,638253676.69,,9676141216.63,,7527750188.08,6715688989.42,149376993.55,847303512.7,,47382489.87,4113040896.1,,,,,,,,,1558593622.51,30651507998.01,1962716000.0,,780030501.29,,,,,8446673663.32,3122587039.87,,,,,1390279968.16,,100419270.78,163939750.97,580079605.61,,625001731.36,17171727531.36,,,,16230089.15,,,,,,,,,,,,,47823235529.37,2755256273.24,1163670014.17,,,,,7362937604.85,4308621518.19,518067888.02,,,509591839.94,306609488.43,16269159.28,,296573893.69,,,,,,,,449364718.01,,14931706124.58,,894088692.78,,1787562369.82,69115257.32,404289790.79,,5910312383.95,,,,,,,,,,,,,,,,,20842018508.53,,,,,1843395945.35,25137821075.49,26981217020.84,47823235529.37,,251679285.89,,,,,,,,,,,,,14243439177.5,11671559123.04,196759483.42,8446673663.32,3122587039.87,312843052.97,894088692.78,,,0
-300750.SZ,20180522,20180522,20171231,1,1,4,1955193267.0,15354587816.94,6504904798.9,638253676.69,,14080936626.3,,5458335290.35,6918521550.9,90630850.1,305835456.79,,52310504.72,3417757092.32,,,,,,,,,2695030983.77,33033102914.27,1961291000.0,,791027220.9,,,,,8219496581.74,2974364031.46,,,,,1408760249.33,,100419270.78,139310483.53,510045198.34,,525068808.1,16629782844.18,,,,13744559.02,,,,,,,,,,,,,49662885758.45,2129095275.13,2245096000.7,,,,,8812715586.2,4978257325.43,203165478.74,,,517308034.99,436196766.94,15205262.28,,317156883.9,,,,,,,,364944599.97,,17890045939.15,,895045177.66,,1789007284.61,68992112.81,419460871.47,,5301600721.68,,,,,,,,,,,,,,,,,23191646660.83,,,,,1769799526.51,24701439571.11,26471239097.62,49662885758.45,,248500011.58,,,,,,,,,,,,,12376856841.25,13790972911.63,142941354.82,8219496581.74,2974364031.46,332362146.18,895045177.66,,,0
-300750.SZ,20180522,20180522,20171231,1,1,4,1955193267.0,15354587816.94,6504904798.9,638253676.69,,14080936626.3,,5458335290.35,6918521550.9,90630850.1,305835456.79,,52310504.72,3417757092.32,,,,,,,,,2695030983.77,33033102914.27,1961291000.0,,791027220.9,,,,,8219496581.74,2974364031.46,,,,,1408760249.33,,100419270.78,139310483.53,510045198.34,,525068808.1,16629782844.18,,,,13744559.02,,,,,,,,,,,,,49662885758.45,2129095275.13,2245096000.7,,,,,8812715586.2,4978257325.43,203165478.74,,,517308034.99,436196766.94,15205262.28,,317156883.9,,,,,,,,364944599.97,,17890045939.15,,895045177.66,,1789007284.61,68992112.81,419460871.47,,5301600721.68,,,,,,,,,,,,,,,,,23191646660.83,,,,,1769799526.51,24701439571.11,26471239097.62,49662885758.45,,248500011.58,,,,,,,,,,,,,12376856841.25,13790972911.63,142941354.82,8219496581.74,2974364031.46,332362146.18,895045177.66,,,1
-300750.SZ,20171110,20171110,20170630,1,1,2,1955193267.0,15073392635.47,4992362867.75,299841288.9,,1817625421.27,,2875309020.6,6005154594.85,129673951.35,156199612.05,,89547067.51,3955037968.76,,,,,,,,,11660834659.89,26689382296.28,1547503500.0,,310725425.65,,,,,5635475096.25,4237842734.96,,,,,1162449058.99,,100419270.78,48928917.03,332451118.62,,818438956.14,14194234078.42,,,,,,,,,,,,,,,,,40883616374.7,719950000.0,1257975977.42,,,,,5427777223.94,6190098389.53,192644288.31,,,402945039.76,396746820.17,8666706.59,,361005463.17,,,,,,,,129784955.93,,14367644864.82,,913849883.01,,1268416291.8,82065980.53,377532756.18,,3361814911.52,,,,,,,,,,,,,,,,,17729459776.34,,,,,519861419.41,22634295178.95,23154156598.36,40883616374.7,,313505119.83,,,,,,,,,,,,,8880463615.45,11617875613.47,219221018.86,5635475096.25,4237842734.96,369672169.76,913849883.01,,,0
-300750.SZ,20171110,20171110,20170630,1,1,2,1955193267.0,15073392635.47,4992362867.75,299841288.9,,1817625421.27,,2875309020.6,6005154594.85,129673951.35,156199612.05,,89547067.51,3955037968.76,,,,,,,,,11660834659.89,26689382296.28,1547503500.0,,310725425.65,,,,,5635475096.25,4237842734.96,,,,,1162449058.99,,100419270.78,48928917.03,332451118.62,,818438956.14,14194234078.42,,,,,,,,,,,,,,,,,40883616374.7,719950000.0,1257975977.42,,,,,5427777223.94,6190098389.53,192644288.31,,,402945039.76,396746820.17,8666706.59,,361005463.17,,,,,,,,129784955.93,,14367644864.82,,913849883.01,,1268416291.8,82065980.53,377532756.18,,3361814911.52,,,,,,,,,,,,,,,,,17729459776.34,,,,,519861419.41,22634295178.95,23154156598.36,40883616374.7,,313505119.83,,,,,,,,,,,,,8880463615.45,11617875613.47,219221018.86,5635475096.25,4237842734.96,369672169.76,913849883.01,,,1
-300750.SZ,20180522,20180522,20161231,1,1,4,613321800.0,11608553987.09,2965362316.99,299841288.9,,2456530834.16,,570522837.1,7315840024.05,183806946.21,101183102.22,,7564477.11,1359772316.12,,,,,,,,,9760704235.56,21761324772.53,143400000.0,,169658176.39,,,,,3727473865.79,1226129855.44,,,,,621645950.26,,100419270.78,14206421.31,264912007.47,,559144024.11,6826989571.55,,,,,,,,,,,,,,,,,28588314344.08,302000000.0,1226587998.8,,,,,4394039468.71,3173395264.55,87727135.56,,,446249267.28,248297683.21,5776292.16,,360240077.32,,,,,,,,240511234.77,,10182824422.36,,950079201.39,,930580458.65,28130130.54,403581087.31,,2614370877.89,,,,,,,,,,,,,,,,,12797195300.25,,,,,302472760.51,15488646283.32,15791119043.83,28588314344.08,,1566890.34,,,,,,,5400000.0,,,,,,7886362861.15,7567434733.26,191371423.32,3727473865.79,1226129855.44,366016369.48,950079201.39,,,0
-300750.SZ,20180522,20180522,20161231,1,1,4,613321800.0,11608553987.09,2965362316.99,299841288.9,,2456530834.16,,570522837.1,7315840024.05,183806946.21,101183102.22,,7564477.11,1359772316.12,,,,,,,,,9760704235.56,21761324772.53,143400000.0,,169658176.39,,,,,3727473865.79,1226129855.44,,,,,621645950.26,,100419270.78,14206421.31,264912007.47,,559144024.11,6826989571.55,,,,,,,,,,,,,,,,,28588314344.08,302000000.0,1226587998.8,,,,,4394039468.71,3173395264.55,87727135.56,,,446249267.28,248297683.21,5776292.16,,360240077.32,,,,,,,,240511234.77,,10182824422.36,,950079201.39,,930580458.65,28130130.54,403581087.31,,2614370877.89,,,,,,,,,,,,,,,,,12797195300.25,,,,,302472760.51,15488646283.32,15791119043.83,28588314344.08,,1566890.34,,,,,,,5400000.0,,,,,,7886362861.15,7567434733.26,191371423.32,3727473865.79,1226129855.44,366016369.48,950079201.39,,,1
-300750.SZ,20161001,20161001,20160630,1,1,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12726241200.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10186685700.0,,,,,,2539555500.0,,,,,,,,,,,,,,,,,,,,,,,,,,0
-300750.SZ,20161001,20161001,20160630,1,1,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12726241200.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10186685700.0,,,,,,2539555500.0,,,,,,,,,,,,,,,,,,,,,,,,,,1
-300750.SZ,20160729,20160729,20160331,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11042108000.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9037644600.0,,,,,,2004463400.0,,,,,,,,,,,,,,,,,,,,,,,,,,0
-300750.SZ,20180522,20180522,20151231,1,1,4,470590000.0,369014163.84,376303017.19,37079169.44,,1292935327.52,,422182294.7,2393930005.73,55484188.21,70469398.58,,,1041611188.85,,,,,,,,,227855161.28,5504467564.87,5000000.0,,,,,,,1313098299.52,474893794.61,,6710.03,,,502758023.88,,100419270.78,6133369.98,135241432.42,,630939035.37,3168489936.59,,,,,,,,,,,,,,,,,8672957501.46,,576558942.87,,,,,1041204473.76,1472399950.48,170045760.44,,,198490932.79,252617557.72,3639309.42,,311755056.01,,,,,,,,1322574459.48,,5349286442.97,,917666570.26,,581118102.95,30220779.54,296337217.09,,1825342669.84,,,,,,,,,,,,,,,,,7174629112.81,,,,,244663210.07,1253665178.58,1498328388.65,8672957501.46,,678828.11,,,,,,,,,,,,,2816112300.43,2513604424.24,55484188.21,1313105009.55,474893794.61,315394365.43,917666570.26,,,0
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data/CN/300750.SZ/raw_cash_flow.csv

@@ -1,52 +0,0 @@
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-300750.SZ,20191026,20191026,20190930,1,1,3,,,38036360575.38,163298908.7,,,,,,,,,,,,1774836858.05,39974496342.13,23046083793.97,2611212349.97,2332476003.68,,,,,,1659794653.67,29649566801.29,10324929540.84,15178453020.7,,2563085.15,14210702.04,,15195226807.89,6661477871.25,321750000.0,,932156630.0,,7915384501.25,7279842306.64,3058463176.85,,,4001503667.85,596534389.9903,1693048366.19,492320693.78,,62601933.18,2247970993.15,1753532674.7,23661063.46,19381965585.64,3688654649.03,23070620234.67,943040491.0,447230000.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0
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-300750.SZ,20190824,20190824,20190630,1,1,2,2346495300.44,87744066.16,24838903624.97,109269862.19,,,,,,,,,,,,781516763.62,25729690250.78,13700891263.83,1752894045.03,1655079077.44,,,,,,1344522955.73,18453387342.03,7276302908.75,2657818813.56,,,,,2657818813.56,4623112893.49,90750000.0,,,,4713862893.49,-2056044079.93,1354369492.97,,,1796249492.97,5528716163.5267,1222988045.56,121183356.39,,45862760.81,1390034162.76,406215330.21,15718117.48,5642192276.51,3688654649.03,9330846925.54,441880000.0,441880000.0,,530322585.73,1817060732.44,52188372.72,71233411.93,,,-305715.96,6164952.02,,-8807709.54,-495832311.39,17405342.8,-3575604570.39,-2426304894.46,8706398499.9,148144846.35,7276302908.75,,,,5642192276.51,,,,,,9330846925.54,3688654649.03,,,0
-300750.SZ,20190824,20190824,20190630,1,1,2,2346495300.44,87744066.16,24838903624.97,109269862.19,,,,,,,,,,,,781516763.62,25729690250.78,13700891263.83,1752894045.03,1655079077.44,,,,,,1344522955.73,18453387342.03,7276302908.75,2657818813.56,,,,,2657818813.56,4623112893.49,90750000.0,,,,4713862893.49,-2056044079.93,1354369492.97,,,1796249492.97,5783236226.2656,1222988045.56,121183356.39,,45862760.81,1390034162.76,406215330.21,15718117.48,5642192276.51,3688654649.03,9330846925.54,441880000.0,441880000.0,,530322585.73,1817060732.44,52188372.72,71233411.93,,,-305715.96,6164952.02,,-8807709.54,-495832311.39,17405342.8,-3575604570.39,-2426304894.46,8706398499.9,,7276302908.75,,,,5642192276.51,,,,,,9330846925.54,3688654649.03,,,1
-300750.SZ,20190427,20190427,20190331,1,1,1,,,12878552646.43,299432.71,,,,,,,,,,,,421498351.55,13300350430.69,6221938005.9,748693434.59,701069344.37,,,,,,651798444.58,8323499229.44,4976851201.25,14605433.53,,,,,14605433.53,2019864308.52,75250000.0,,626495569.66,,2721609878.18,-2707004444.65,274025394.22,,,715025394.22,4255915886.1405,279493169.29,61759955.59,,24669985.03,365923109.91,349102284.31,-22720393.29,2596228647.62,3688654649.03,6284883296.65,441000000.0,441000000.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-300750.SZ,20190425,20190425,20181231,1,1,4,3735896487.72,262258592.79,33853639616.99,37127687.41,,,,,,,,,,,,2274383357.82,36165150662.22,19041614626.94,2299961469.07,1868081717.0,,,,,,1639227148.68,24848884961.69,11316265700.53,70629509.16,5258600.0,3655688.2,294506.1,,79838303.46,6629274672.98,192025942.3,,12746210861.04,,19567511476.32,-19487673172.86,4123196326.07,,510282473.44,10908434734.88,5771848024.1698,3493721841.0,216119536.13,,155877429.51,3865718806.64,7042715928.24,27440781.07,-1101250763.02,4789905412.05,3688654649.03,6274955935.37,108785163.6,,974912150.01,2125110234.63,95733856.19,83377065.66,,,91538964.57,15782022.0,314247518.1,-184397531.48,-669755258.27,15459358.23,-4371312786.43,-3143244270.65,11851141407.59,119517889.87,11316265700.53,,,,-1101250763.02,,,,,,3688654649.03,4789905412.05,,,0
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-300750.SZ,20180522,20180522,20180331,1,1,1,469264488.71,65166429.44,2561651184.82,136600925.36,,,,,,,,,,,,211514708.3,2909766818.48,4899971184.88,451661730.79,339098883.01,,,,,,488869504.21,6179601302.89,-3269834484.41,4220209248.47,,,,,4220209248.47,1667611627.4,,,151925834.11,,1819537461.51,2400671786.96,997738375.18,,20695110.88,1037619613.06,-4701407794.046,1052569621.31,42973564.05,,66470960.3,1162014145.66,-124394532.6,-13184201.95,-1006741432.0,4789905412.05,3783163980.05,19186127.0,19186127.0,,127717465.75,418689739.66,22774627.17,14666290.5,,,328223.0,,,-27972938.86,-70034407.27,,-606145268.44,-2419349974.35,-1284794230.57,19855070.85,-3269834484.41,,,,-1006741432.0,,,,,,3783163980.05,4789905412.05,,,0
-300750.SZ,20180522,20180522,20171231,1,1,4,4194056577.66,112588566.26,18872908615.92,21013193.89,,,,,,,,,,,,529176792.58,19423098602.39,12357592298.17,2122637417.2,1493296692.69,,,,,,1108823984.08,17082350392.14,2340748210.25,758831340.52,448002008.14,2565089.09,572921.94,,1209971359.69,7180281117.24,1665503314.78,,656.0,,8845785088.02,-7635813728.33,4476769278.83,,9028894.69,10664444151.52,4099608510.9298,1421628377.86,81600198.58,,228481941.8,1731710518.24,8932733633.28,-13763656.75,3623904458.45,1166000953.6,4789905412.05,6178645978.0,1168645100.0,,244744030.88,1279538608.41,75101189.69,26541967.12,,,78311541.52,,,-1344305303.77,-245133190.87,-2604999.44,-2290181545.88,-5075995986.77,5163299958.56,124786796.88,2340748210.25,,,,3623904458.45,,,,,,4789905412.05,1166000953.6,,,0
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-300750.SZ,20171110,20171110,20170630,1,1,2,2020743800.0,34312300.0,6701189072.6,6314977.79,,,,,,,,,,,,236369322.05,6943873372.44,5680819173.2,954740070.6,544344685.49,,,,,,383613562.34,7563517491.63,-619644119.19,198806392.58,445502008.14,1122943.61,572921.94,,646004266.27,3978747353.07,651932500.0,,1534321000.0,,6165000853.07,-5518996586.8,1515912025.59,,,6599412903.59,-3817342677.722,1109776166.12,25777423.37,,184404131.28,1319957720.77,5279455182.82,2908894.84,-856276628.33,1166000953.6,309724325.27,5083500878.0,73500000.0,,49177200.0,477360300.0,31487700.0,5951300.0,,,2279700.0,,,-1245985200.0,-67539100.0,-997900.0,-2641681800.0,-1366773700.0,2082021300.0,,-619644100.0,,,,,,,,,,,,,,0
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-300750.SZ,20171110,20171110,20141231,1,1,4,55563800.0,24221600.0,732813440.92,4165547.69,,,,,,,,,,,,11345197.89,748324186.5,601009216.98,98419099.09,28689220.56,,,,,,159111051.94,887228588.57,-138904402.07,3180178.72,,,,,3180178.72,300525204.81,,,976061675.94,,1276586880.75,-1273406702.03,201635144.87,,1556561925.62,1782597070.49,,4760000.0,4752841.5,,307574012.42,317086853.92,1465510216.57,-4995362.09,48203750.38,5847240.63,54050991.01,24400000.0,24400000.0,,2591100.0,44569700.0,22656600.0,64800.0,,,102500.0,,,-16117600.0,-19814600.0,,-220368300.0,-313910300.0,281536200.0,,-138904400.0,,,,,,,,,,,,,,0

data/CN/300750.SZ/raw_dividends_raw.csv

@@ -1,35 +0,0 @@
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data/CN/300750.SZ/raw_historical_metrics_shareholder_number.csv

@@ -1,37 +0,0 @@
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-300750.SZ,20190427,20190331,76045
-300750.SZ,20190425,20181231,82514
-300750.SZ,20181026,20180930,99847
-300750.SZ,20180824,20180630,164739
-300750.SZ,20180608,20180611,368718

data/CN/300750.SZ/raw_income_statement.csv

@@ -1,53 +0,0 @@
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-300750.SZ,20200827,20200827,20200630,1,1,2,0.8833,0.8805,18829453132.97,18829453132.97,,,,,,,,,,,,,,,286915936.0,-406292308.07,-88395180.45,,16777260846.52,13717117142.14,,,101445380.16,792140085.18,809663593.83,-401913973.1,-338341629.6,,,,,,,,,,,2594040723.68,25328338.96,22713511.25,,2596655551.39,452737521.27,2143918030.12,1937281090.5,206636939.62,42364310.59,2186282340.71,1967490112.44,218792228.27,2311503122.65,4774464802.73,,,,1298453447.42,256688048.79,623732754.44,,,,,,,,,,,,,,,2143918030.12,0
-300750.SZ,20200827,20200827,20200630,1,1,2,0.8833,0.8805,18829453132.97,18829453132.97,,,,,,,,,,,,,,,286915936.0,-406292308.07,-88395180.45,,16777260846.52,13717117142.14,,,101445380.16,792140085.18,809663593.83,-401913973.1,-338341629.6,,,,,,,,,,,2594040723.68,25328338.96,22713511.25,,2596655551.39,452737521.27,2143918030.12,1937281090.5,206636939.62,42364310.59,2186282340.71,1967490112.44,218792228.27,2311503122.65,4774464802.73,,,,1298453447.42,256688048.79,623732754.44,,,,,,,,,,,,,,,2143918030.12,1
-300750.SZ,20200428,20200428,20200331,1,1,1,0.3406,0.3396,9030794052.46,9030794052.46,,,,,,,,,,,,,,,286915936.0,-348146749.47,-29567547.69,,8188919060.9,6764865485.58,,,54015270.55,352566282.28,412747503.97,-139684257.75,-96898077.07,,,,,,,,,,,1106046439.49,7194638.06,16264436.89,,1096976640.66,191963042.83,905013597.83,742043183.2,162970414.63,-118995658.48,786017939.35,651959025.75,134058913.6,1133338722.94,,,,,638663048.54,116204500.18,304532042.79,,,,,,,,,,,,,,,905013597.83,0
-300750.SZ,20200428,20200428,20200331,1,1,1,0.3406,0.3396,9030794052.46,9030794052.46,,,,,,,,,,,,,,,286915936.0,-348146749.47,-29567547.69,,8188919060.9,6764865485.58,,,54015270.55,352566282.28,412747503.97,-139684257.75,-96898077.07,,,,,,,,,,,1106046439.49,7194638.06,16264436.89,,1096976640.66,191963042.83,905013597.83,742043183.2,162970414.63,-118995658.48,786017939.35,651959025.75,134058913.6,1312818614.89,,,,,638663048.54,116204500.18,304532042.79,,,,,,,,,,,,,,,905013597.83,1
-300750.SZ,20200425,20200425,20191231,1,1,4,2.0937,2.0887,45788020642.41,45788020642.41,,,,,,,,,,,,,,,27331582.1,-79604902.02,-11899568.84,,40624707856.41,32482760512.62,,,272228105.62,2156553541.51,1832673929.87,-781621299.53,-1434329163.69,,,,,,,,,,,5758793258.1,62428112.63,60456806.48,,5760764564.25,748090666.38,5012673897.87,4560307432.71,452366465.16,957658852.86,5970332750.73,5517966285.57,452366465.16,5722463917.76,10131504260.02,,,,2992107516.52,289254465.49,1078256966.28,,,,,,,,,,,,,,,5012673897.87,0
-300750.SZ,20200425,20200425,20191231,1,1,4,2.0937,2.0887,45788020642.41,45788020642.41,,,,,,,,,,,,,,,27331582.1,-79604902.02,-11899568.84,,40624707856.41,32482760512.62,,,272228105.62,2156553541.51,1832673929.87,-781621299.53,-1434329163.69,,,,,,,,,,,5758793258.1,62428112.63,60456806.48,,5760764564.25,748090666.38,5012673897.87,4560307432.71,452366465.16,957658852.86,5970332750.73,5517966285.57,452366465.16,5722463917.76,10131504260.02,,,,2992107516.52,289254465.49,1078256966.28,,,,,,,,,,,,,,,5012673897.87,1
-300750.SZ,20191026,20191026,20190930,1,1,3,1.6002,1.5959,32855706536.08,32855706536.08,,,,,,,,,,,,,,,,-62620220.59,-7917302.55,,28894615247.49,23301405480.87,,,201508967.69,1477529588.94,1629018744.75,-632605668.62,-550692987.35,,,,,,,,,,,4446278015.84,46102640.95,34688668.76,,4457691988.03,697474456.56,3760217531.47,3464305267.56,295912263.91,971764392.34,4731981923.81,4436069659.9,295912263.91,5059302675.96,,,,,2255954070.6,192122654.09,828347798.84,,,,,,,,,,,,,,,3760217531.47,1
-300750.SZ,20191026,20191026,20190930,1,1,3,1.6002,1.5959,32855706536.08,32855706536.08,,,,,,,,,,,,,,,,-62620220.59,-7917302.55,,28894615247.49,23301405480.87,,,201508967.69,1477529588.94,1629018744.75,-632605668.62,-550692987.35,,,,,,,,,,,4446278015.84,46102640.95,34688668.76,,4457691988.03,697474456.56,3760217531.47,3464305267.56,295912263.91,971764392.34,4731981923.81,4436069659.9,295912263.91,4534188607.12,,,,,2255954070.6,192122654.09,828347798.84,,,,,,,,,,,,,,,3760217531.47,0
-300750.SZ,20190824,20190824,20190630,1,1,2,0.9674,0.9666,20263844170.44,20263844170.44,,,,,,,,,,,,,,,,-45894420.7,-6203272.34,,17726032155.44,14226726997.42,,,117905051.7,804073640.0,1061946966.06,-428054892.49,-421525991.07,,,,,,,,,,,2812549873.07,10005017.82,17588816.33,,2804966074.56,458470774.12,2346495300.44,2102435142.7,244060157.74,235561659.09,2582056959.53,2337996801.79,244060157.74,2945403830.85,4885886347.94,,,,1413111807.02,119945783.05,532991928.58,,,,,,,,,,,,,,,2346495300.44,0
-300750.SZ,20190427,20190427,20190331,1,1,1,0.48,0.48,9981855666.38,9981855666.38,,,,,,,,,,,,,,,,16802477.33,2097769.4,,8817890513.83,7116134804.4,,,86639222.05,401176198.42,525048115.72,-110462345.72,36205911.5,,,,,,,,,,,1321125127.01,6548666.78,5256479.6,,1322417314.19,205595113.39,1116822200.8,1047233226.95,69588973.85,161477040.74,1278299241.54,1208710267.69,69588973.85,1426164772.5,,,,,748586098.54,58662867.4,255070916.72,,,,,,,,,,,,,,,1116822200.8,0
-300750.SZ,20190425,20190425,20181231,1,1,4,1.6412,1.6407,29611265434.22,29611265434.22,,,,,,,,,,,,,,,-314247518.1,184397531.48,-4264014.31,,25729175371.38,19902284153.15,,,171183911.7,1378868425.55,1590659572.27,-279733226.14,974912150.01,,,,,,,,,,,4168476326.68,62303262.42,25966337.17,,4204813251.93,468916764.21,3735896487.72,3387035207.64,348861280.08,-585339219.51,3150557268.21,2801695988.13,348861280.08,4110132051.73,6414353208.21,,,,1991000384.84,204435332.83,565817388.3,,,,,,,,,,,,,,,3735896487.72,0
-300750.SZ,20190425,20190425,20181231,1,1,4,1.6412,1.6407,29611265434.22,29611265434.22,,,,,,,,,,,,,,,-314247518.1,184397531.48,-4264014.31,,25729175371.38,19902284153.15,,,171183911.7,1378868425.55,1590659572.27,-279733226.14,974912150.01,,,,,,,,,,,4168476326.68,62303262.42,25966337.17,,4204813251.93,468916764.21,3735896487.72,3387035207.64,348861280.08,-585339219.51,3150557268.21,2801695988.13,348861280.08,4110132051.73,6414353208.21,,,,1991000384.84,204435332.83,565817388.3,,,,,,,,,,,,,,,3735896487.72,1
-300750.SZ,20181026,20181026,20180930,1,1,3,1.17,1.17,19135923760.37,19135923760.37,,,,,,,,,,,,,,,,69427412.54,9583242.07,,16534427281.6,13150477803.26,,,101462647.63,846876034.25,983822491.58,-150717424.86,454657985.55,,,,,,,,,,,3071726107.71,15983636.65,4285602.67,,3083424141.69,441187671.13,2642236470.56,2378500010.06,263736460.5,-208586704.74,2433649765.82,2169913305.32,263736460.5,3467983671.62,,,,,1147847744.19,234936100.17,369419073.26,,,,,,,,,,,,,,,2642236470.56,1
-300750.SZ,20181026,20181026,20180930,1,1,3,1.17,1.17,19135923760.37,19135923760.37,,,,,,,,,,,,,,,,69427412.54,9583242.07,,16534427281.6,13150477803.26,,,101462647.63,846876034.25,983822491.58,-150717424.86,454657985.55,,,,,,,,,,,3071726107.71,15983636.65,4285602.67,,3083424141.69,441187671.13,2642236470.56,2378500010.06,263736460.5,-208586704.74,2433649765.82,2169913305.32,263736460.5,3467983671.62,,,,,1147847744.19,234936100.17,369419073.26,,,,,,,,,,,,,,,2642236470.56,0
-300750.SZ,20180824,20180824,20180630,1,1,2,0.4659,,9359580668.12,9359580668.12,,,,,,,,,,,,,,,,59838864.61,20009437.82,,8420213004.66,6431566540.72,,,35368966.71,448795383.07,1320335349.34,-22979347.63,207126112.45,,,,,,,,,,,1215792945.89,8159715.82,3277772.87,,1220674888.84,150781194.54,1069893694.3,910954288.0,158939406.3,-176351133.34,893542560.96,734603154.66,158939406.3,1135419727.12,2153190022.79,,,,,,,,,,,,,,,,,,,,,1069893694.3,0
-300750.SZ,20180522,20180522,20180331,1,1,1,0.212,,3711641230.77,3711641230.77,,,,,,,,,,,,,,,,27972938.86,12043585.12,,3353698085.04,2495640398.99,,,14792182.15,147749265.04,565033595.37,2765177.74,127717465.75,,,,,,,,,,,539889042.27,3447660.53,2328501.22,,541008201.58,71743712.87,469264488.71,413347159.22,55917329.49,3179274.31,472443763.02,416526433.53,55917329.49,642726969.9,,,,,,,,,,,,,,,,,,,,,,469264488.71,0
-300750.SZ,20180522,20180522,20171231,1,1,4,2.0084,,19996860806.33,19996860806.33,,,,,,,,,,,,,,,,1344305303.77,-49976783.37,,16875255721.27,12740187148.7,,,95900521.0,795766091.83,2956488278.51,42169650.35,244744030.88,,,,,,,,,,,4832020495.66,18655542.93,2575814.31,,4848100224.28,654043646.62,4194056577.66,3877954869.7,316101707.96,246933121.24,4440989698.9,4124887990.94,316101707.96,3608196383.76,4989378148.98,,,,,,,,,,,,,,,,,,,,,4194056577.66,0
-300750.SZ,20181026,20181026,20170930,1,1,3,1.33,1.33,11970899899.45,11970899899.45,,,,,,,,,,,,,,,,1344112981.19,-19477989.06,,10402804866.65,7697664142.61,,,44337735.52,504161719.26,905233643.98,57697766.78,115122025.1,,,,,,,,,,,3293200146.4,13549843.33,4540268.55,,3302209721.18,470922060.98,2831287660.2,2570510205.15,260777455.05,344145542.58,3175433202.78,2914655747.73,260777455.05,2071982527.37,,,,,1078587833.4,85878529.21,33616243.84,,,,,,,,,,,,,,,2831287660.2,1
-300750.SZ,20181026,20181026,20170930,1,1,3,1.33,1.33,11970899899.45,11970899899.45,,,,,,,,,,,,,,,,1344112981.19,-19477989.06,,10402804866.65,7697664142.61,,,44337735.52,504161719.26,905233643.98,57697766.78,115122025.1,,,,,,,,,,,3293200146.4,13549843.33,4540268.55,,3302209721.18,470922060.98,2831287660.2,2570510205.15,260777455.05,344145542.58,3175433202.78,2914655747.73,260777455.05,2124244812.74,,,,,1078587833.4,85878529.21,33616243.84,,,,,,,,,,,,,,,2831287660.2,0
-300750.SZ,20171110,20171110,20170630,1,1,2,0.974,,6294695149.0,6294695149.0,,,,,,,,,,,,,,,,1245985248.03,-13175301.07,,5484178667.47,3934602921.36,,,24025547.56,269397374.58,1158841331.86,48134260.94,49177231.17,,,,,,,,,,,2328662915.71,6979859.43,2825898.64,2284479.0,2332816876.5,312073095.21,2020743781.29,1856935080.43,163808700.86,311938229.49,2332682010.78,2168873309.92,163808700.86,1179989159.79,1694788459.79,,,,,,,,,,,,,,,,,,,,,,0
-300750.SZ,20180522,20180522,20170331,1,1,1,-0.0189,,1454385691.84,1454385691.84,,,,,,,,,,,,,,,,85172231.78,-12978308.38,,1551974320.73,905259335.22,,,4410984.45,74633237.11,538433567.86,29237196.09,,,,,,,,,,,,-12738473.39,43140845.75,31953.67,,30370418.69,13875218.84,16495199.85,-11687915.1,35990121.09,81363.73,24383569.72,-11606551.37,35990121.09,-68351432.8,,,,,,,,,,,,,,,,,,,,,,16495199.85,0
-300750.SZ,20180522,20180522,20161231,1,1,4,1.8736,,14878985098.12,14878985098.12,,,,,,,,,,,,,,,,76080348.84,21669756.19,,11584739495.37,8376801892.4,,,109215284.54,632190722.09,2152228887.34,80443838.96,233858870.04,,,,,,,,,,,3212120702.65,188974466.62,881463.97,,3400213705.3,481776851.01,2918436854.29,2851821419.26,66615435.03,888062.23,2919324916.52,2852709481.49,66615435.03,3374689441.71,4159176537.97,,,,,,,,,,,,,,,,,,,,,2918436854.29,0
-300750.SZ,20161001,20161001,20160630,1,1,2,,,4786106400.0,4786106400.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,995792500.0,,,,,,4786106400.0,4786106400.0,,,,,,,,,,,,,,,,,,,,,,0
-300750.SZ,20160729,20160729,20160331,1,1,1,,,2088372900.0,2088372900.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,476764100.0,,,,,,2088372900.0,,,,,,,,,,,,,,,,,,,,,,,0
-300750.SZ,20180522,20180522,20151231,1,1,4,0.7755,,5702884874.34,5702884874.34,,,,,,,,,,,,,,,,1455431.76,-9658518.48,,4658155646.48,3499202013.73,,,39629825.6,330920265.24,620575323.94,109186539.31,58641678.66,,,,,,,,,,,1045594555.74,71190367.14,16752779.33,,1100032143.55,149451069.1,950581074.45,930646375.1,19934699.35,494869.71,951075944.16,931141244.81,19934699.35,1153915767.17,1346241339.09,,,,,,,,,,,,,,,,,,,,,950581074.45,0
-300750.SZ,20171110,20171110,20141231,1,1,4,,,866786361.55,866786361.55,,,,,,,,,,,,,,,,16117579.09,13024101.96,,866520694.75,643729807.47,,,10300.8,43294226.16,152321605.53,24573664.53,2591090.26,,,,,,,,,,,16383245.89,46216154.07,169330.58,102484.05,62430069.38,6866277.79,55563791.59,54425751.28,1138040.31,183958.4,55747749.99,54609709.68,1138040.31,27430421.59,94721521.59,,,,,,,,,,,,,,,,,,,,,,0

data/CN/300750.SZ/raw_market_metrics_daily_basic.csv

@@ -1,2 +0,0 @@
-ts_code,trade_date,close,turnover_rate,turnover_rate_f,volume_ratio,pe,pe_ttm,pb,ps,ps_ttm,dv_ratio,dv_ttm,total_share,float_share,free_share,total_mv,circ_mv
-300750.SZ,20251231,367.26,0.3765,0.5701,0.73,33.0287,26.2793,5.3318,4.6298,4.3416,1.3628,1.8399,456361.226,425638.5861,281129.1965,167603223.8608,156320027.1311

data/CN/300750.SZ/raw_market_metrics_shareholder_number.csv

@@ -1,2 +0,0 @@
-ts_code,ann_date,end_date,holder_num
-300750.SZ,20251021,20250930,227474

data/CN/300750.SZ/raw_market_metrics_stock_basic.csv

@@ -1,2 +0,0 @@
-name,list_date
-宁德时代,20180611

data/CN/300750.SZ/raw_repurchases.csv

@@ -1,84 +0,0 @@
-ts_code,ann_date,end_date,proc,exp_date,vol,amount,high_limit,low_limit
-300750.SZ,20251010,20250930,实施,,15990782.0,4385504687.9,317.63,231.5
-300750.SZ,20250902,20250831,实施,,8690006.0,2130545080.93,290.0,231.5
-300750.SZ,20250506,20250430,实施,,6640986.0,1550809971.05,237.38,231.5
-300750.SZ,20250425,20250425,实施,,1075275.0,254677939.51,237.38,236.01
-300750.SZ,20250407,,预案,,,8000000000.0,392.32,
-300750.SZ,20250224,20250221,完成,,71547.0,1412292.78,19.74,19.74
-300750.SZ,20241227,,股东大会通过,,,1412292.78,19.74,19.74
-300750.SZ,20241227,,股东大会通过,,,1412300.0,19.74,19.74
-300750.SZ,20241030,20241030,完成,,15991524.0,2710713907.1,194.1,146.31
-300750.SZ,20240909,,预案,,,1412292.78,19.74,19.74
-300750.SZ,20240909,,预案,,,1412300.0,19.74,19.74
-300750.SZ,20240801,20240731,实施,,15991524.0,2710713907.1,194.1,146.31
-300750.SZ,20240701,20240630,实施,,14483830.0,2446006804.38,194.1,146.31
-300750.SZ,20240618,20240617,完成,,46944.0,926700.0,19.74,19.74
-300750.SZ,20240618,20240617,完成,,187070.0,3665800.0,19.74,19.53
-300750.SZ,20240316,,预案,,,926700.0,19.74,19.74
-300750.SZ,20240316,,预案,,,926700.0,19.74,19.74
-300750.SZ,20240301,20240229,实施,,11609630.0,1896541059.43,181.16,146.31
-300750.SZ,20240201,20240131,实施,,10367912.0,1700392748.16,181.16,146.31
-300750.SZ,20240102,20231231,实施,,9086912.0,1503263150.63,181.16,146.31
-300750.SZ,20231201,20231130,实施,,4377000.0,764172035.62,181.16,165.39
-300750.SZ,20231120,20231117,实施,,1043098.0,188400157.46,181.16,179.53
-300750.SZ,20231031,,预案,,,3000000000.0,294.45,
-300750.SZ,20231031,,预案,,,3000000000.0,294.45,
-300750.SZ,20231026,,提议,,,3000000000.0,,
-300750.SZ,20231026,,预案,,,3000000000.0,,
-300750.SZ,20230908,,预案,,,3665800.0,19.74,19.53
-300750.SZ,20230908,,预案,,,3665800.0,19.74,19.53
-300750.SZ,20230415,20230414,完成,,49240.0,1739161.2,,
-300750.SZ,20230415,20230414,完成,,80320.0,2840089.6,,35.15
-300750.SZ,20230415,20230414,完成,,49240.0,1739161.2,,
-300750.SZ,20230415,20230414,完成,,80320.0,2840089.6,,35.15
-300750.SZ,20230401,,股东大会通过,,,1739161.2,,
-300750.SZ,20230401,,股东大会通过,,,2840089.6,,35.15
-300750.SZ,20230401,,股东大会通过,,,1739161.2,,
-300750.SZ,20230401,,股东大会通过,,,2840089.6,,35.15
-300750.SZ,20230310,,预案,,,2840089.6,,35.15
-300750.SZ,20230310,,预案,,,2840089.6,,35.15
-300750.SZ,20220908,,预案,,,1739161.2,,
-300750.SZ,20220908,,预案,,,1739161.2,,
-300750.SZ,20220822,20220819,完成,,64050.0,2262867.7,,
-300750.SZ,20220822,20220819,完成,,72240.0,2544799.2,,
-300750.SZ,20220822,20220819,完成,,64050.0,2262867.7,,
-300750.SZ,20220822,20220819,完成,,64050.0,2262900.0,,
-300750.SZ,20220822,20220819,完成,,72240.0,2544799.2,,
-300750.SZ,20220518,,股东大会通过,,,2262867.7,,35.53
-300750.SZ,20220518,,股东大会通过,,,2544799.2,,
-300750.SZ,20220518,,股东大会通过,,,2262867.7,,35.53
-300750.SZ,20220518,,股东大会通过,,,2262900.0,,35.53
-300750.SZ,20220518,,股东大会通过,,,2544799.2,,
-300750.SZ,20220422,,预案,,,2544799.2,,
-300750.SZ,20220422,,预案,,,2544799.2,,
-300750.SZ,20210913,,预案,,,2262867.7,,35.53
-300750.SZ,20210913,,预案,,,2262867.7,,35.53
-300750.SZ,20210913,,预案,,,2262900.0,,35.53
-300750.SZ,20210622,20210621,完成,,234010.0,8333884.58,35.53,35.15
-300750.SZ,20210622,20210621,完成,,232216.0,8253933.71,35.53,35.15
-300750.SZ,20210622,20210621,完成,,234010.0,8333884.58,35.53,35.15
-300750.SZ,20210622,20210621,完成,,232216.0,8253933.71,35.53,35.15
-300750.SZ,20210521,,股东大会通过,,,8253933.71,35.5442,35.5442
-300750.SZ,20210521,,股东大会通过,,,8333884.58,,35.15
-300750.SZ,20210521,,股东大会通过,,,8253933.71,35.5442,35.5442
-300750.SZ,20210521,,股东大会通过,,,8333884.58,,35.15
-300750.SZ,20210428,,预案,,,8333884.58,,35.15
-300750.SZ,20210428,,预案,,,8333884.58,,35.15
-300750.SZ,20200911,,预案,,,8253933.71,35.5442,35.5442
-300750.SZ,20200911,,预案,,,8253933.71,35.5442,35.5442
-300750.SZ,20200526,20200522,完成,,1034140.0,36630244.54,,
-300750.SZ,20200526,20200522,完成,,251780.0,8871401.33,35.2347,35.2347
-300750.SZ,20200526,20200522,完成,,1034140.0,36630244.54,,
-300750.SZ,20200526,20200522,完成,,251780.0,8871401.33,35.2347,35.2347
-300750.SZ,20200519,,股东大会通过,,,36630244.54,,
-300750.SZ,20200519,,股东大会通过,,,36630244.54,,
-300750.SZ,20200425,,预案,,,36630244.54,,
-300750.SZ,20200425,,预案,,,36630244.54,,
-300750.SZ,20190903,,预案,,,8871401.33,35.2347,35.2347
-300750.SZ,20190903,,预案,,,8871401.33,35.2347,35.2347
-300750.SZ,20190712,20190710,完成,,572400.0,20144982.22,,
-300750.SZ,20190712,20190710,完成,,572400.0,20144982.22,,
-300750.SZ,20190522,,股东大会通过,,,20144982.22,,
-300750.SZ,20190522,,股东大会通过,,,20144982.22,,
-300750.SZ,20190425,,预案,,,20144982.22,,
-300750.SZ,20190425,,预案,,,20144982.22,,

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-                    <h1>宁德时代 (300750.SZ) - Financial Report</h1>
-<p><em>Report generated on: 2026-01-03</em></p>
-
-        <table class="company-table">
-            <thead>
-                <tr>
-                    <th>代码</th>
-                    <th>简称</th>
-                    <th>上市日期</th>
-                    <th>PE</th>
-                    <th>PB</th>
-                    <th>股息率(%)</th>
-                </tr>
-            </thead>
-            <tbody>
-                <tr>
-                    <td>300750.SZ</td>
-                    <td>宁德时代</td>
-                    <td>2018-06-11</td>
-                    <td>33.03</td>
-                    <td>5.33</td>
-                    <td>1.84%</td>
-                </tr>
-            </tbody>
-        </table>
-        
-<div class="table-gap"></div>
-
-            <table class="metrics-table" data-table="metrics" data-scrollable="true">
-                <thead>
-                    <tr>
-                        <th>指标</th>
-                        <th>2025Q3</th><th>2024A</th><th>2023A</th><th>2022A</th><th>2021A</th><th>2020A</th><th>2019A</th><th>2018A</th><th>2017A</th><th>2016A</th><th>2015A</th><th>2014A</th>
-                    </tr>
-                </thead>
-                <tbody>
-                    <tr class="section-row"><td class="section-label">主要指标</td><td class="section-spacer" colspan="12"></td></tr>
-<tr><td class="metric-name">ROE</td><td>15.60%</td><td>20.55%</td><td>22.32%</td><td>18.68%</td><td>18.85%</td><td>8.70%</td><td>11.96%</td><td>10.28%</td><td>15.70%</td><td>18.41%</td><td>74.23%</td><td>21.19%</td></tr>
-<tr><td class="metric-name">ROA</td><td>5.47%</td><td>6.45%</td><td>6.15%</td><td>5.11%</td><td>5.18%</td><td>3.56%</td><td>4.50%</td><td>4.58%</td><td>7.81%</td><td>9.98%</td><td>10.73%</td><td>1.89%</td></tr>
-<tr><td class="metric-name">ROCE/ROIC</td><td>11.87%</td><td>16.58%</td><td>16.87%</td><td>12.81%</td><td>14.82%</td><td>8.07%</td><td>12.35%</td><td>10.67%</td><td>12.26%</td><td>19.55%</td><td>36.60%</td><td>4.72%</td></tr>
-<tr><td class="metric-name">毛利率</td><td>25.31%</td><td>24.44%</td><td>22.91%</td><td>20.25%</td><td>26.28%</td><td>27.76%</td><td>29.06%</td><td>32.79%</td><td>36.29%</td><td>43.70%</td><td>38.64%</td><td>25.73%</td></tr>
-<tr><td class="metric-name">净利润率</td><td>17.32%</td><td>14.02%</td><td>11.01%</td><td>9.35%</td><td>12.22%</td><td>11.10%</td><td>9.96%</td><td>11.44%</td><td>19.39%</td><td>19.17%</td><td>16.32%</td><td>6.28%</td></tr>
-<tr><td class="metric-name">收入(亿)</td><td>2,830.72</td><td>3,620.13</td><td>4,009.17</td><td>3,285.94</td><td>1,303.56</td><td>503.19</td><td>457.88</td><td>296.11</td><td>199.97</td><td>148.79</td><td>57.03</td><td>8.67</td></tr>
-<tr><td class="metric-name">收入增速</td><td>9.28%</td><td>-9.70%</td><td>22.01%</td><td>152.07%</td><td>159.06%</td><td>9.90%</td><td>54.63%</td><td>48.08%</td><td>34.40%</td><td>160.90%</td><td>557.93%</td><td>-</td></tr>
-<tr><td class="metric-name">净利润(亿)</td><td>490.34</td><td>507.45</td><td>441.21</td><td>307.29</td><td>159.31</td><td>55.83</td><td>45.60</td><td>33.87</td><td>38.78</td><td>28.52</td><td>9.31</td><td>0.54</td></tr>
-<tr><td class="metric-name">净利润增速</td><td>36.20%</td><td>15.01%</td><td>43.58%</td><td>92.89%</td><td>185.34%</td><td>22.43%</td><td>34.64%</td><td>-12.66%</td><td>35.98%</td><td>206.43%</td><td>1609.94%</td><td>-</td></tr>
-<tr><td class="metric-name">经营净现金流(亿)</td><td>806.60</td><td>969.90</td><td>928.26</td><td>612.09</td><td>429.08</td><td>184.30</td><td>134.72</td><td>113.16</td><td>23.41</td><td>21.09</td><td>6.65</td><td>-1.39</td></tr>
-<tr><td class="metric-name">资本开支(亿)</td><td>300.88</td><td>311.80</td><td>336.25</td><td>482.15</td><td>437.68</td><td>133.02</td><td>96.27</td><td>66.29</td><td>71.80</td><td>28.01</td><td>15.54</td><td>3.01</td></tr>
-<tr><td class="metric-name">自由现金流(亿)</td><td>505.73</td><td>658.10</td><td>592.01</td><td>129.94</td><td>-8.60</td><td>51.28</td><td>38.45</td><td>46.87</td><td>-48.40</td><td>-6.92</td><td>-8.89</td><td>-4.39</td></tr>
-<tr><td class="metric-name">分红(亿)</td><td>-</td><td>152.80</td><td>221.18</td><td>126.72</td><td>-</td><td>5.59</td><td>4.86</td><td>3.12</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">回购(亿)</td><td>-</td><td>129.80</td><td>129.84</td><td>0.32</td><td>0.90</td><td>2.54</td><td>1.39</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">总资产(亿)</td><td>8,960.82</td><td>7,866.58</td><td>7,171.68</td><td>6,009.52</td><td>3,076.67</td><td>1,566.18</td><td>1,013.52</td><td>738.84</td><td>496.63</td><td>285.88</td><td>86.73</td><td>28.75</td></tr>
-<tr><td class="metric-name">净资产(亿)</td><td>3,142.48</td><td>2,469.30</td><td>1,977.08</td><td>1,644.81</td><td>845.13</td><td>642.07</td><td>381.35</td><td>329.38</td><td>247.01</td><td>154.89</td><td>12.54</td><td>2.57</td></tr>
-<tr><td class="metric-name">商誉(亿)</td><td>8.91</td><td>8.95</td><td>7.08</td><td>7.04</td><td>5.28</td><td>1.48</td><td>1.48</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td><td>-</td></tr>
-<tr class="section-row"><td class="section-label">费用指标</td><td class="section-spacer" colspan="12"></td></tr>
-<tr><td class="metric-name">销售费用率</td><td>0.85%</td><td>0.98%</td><td>4.48%</td><td>3.38%</td><td>3.35%</td><td>4.41%</td><td>4.71%</td><td>4.66%</td><td>3.98%</td><td>4.25%</td><td>5.80%</td><td>4.99%</td></tr>
-<tr><td class="metric-name">管理费用率</td><td>2.91%</td><td>2.68%</td><td>2.11%</td><td>2.12%</td><td>2.58%</td><td>3.51%</td><td>4.00%</td><td>5.37%</td><td>14.78%</td><td>14.46%</td><td>10.88%</td><td>17.57%</td></tr>
-<tr><td class="metric-name">SG&A比例</td><td>3.76%</td><td>3.66%</td><td>6.59%</td><td>5.50%</td><td>5.94%</td><td>7.92%</td><td>8.71%</td><td>10.03%</td><td>18.76%</td><td>18.71%</td><td>16.68%</td><td>22.57%</td></tr>
-<tr><td class="metric-name">研发费用率</td><td>5.32%</td><td>5.14%</td><td>4.58%</td><td>4.72%</td><td>5.90%</td><td>7.09%</td><td>6.53%</td><td>6.72%</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">其他费用率</td><td>-1.09%</td><td>1.63%</td><td>0.74%</td><td>0.68%</td><td>2.23%</td><td>1.66%</td><td>3.85%</td><td>4.60%</td><td>-1.87%</td><td>5.82%</td><td>5.64%</td><td>-3.11%</td></tr>
-<tr><td class="metric-name">折旧费用占比</td><td>-</td><td>6.20%</td><td>5.26%</td><td>3.64%</td><td>4.49%</td><td>9.10%</td><td>9.04%</td><td>7.18%</td><td>6.40%</td><td>4.92%</td><td>2.88%</td><td>5.14%</td></tr>
-<tr><td class="metric-name">所得税率</td><td>13.86%</td><td>14.52%</td><td>13.27%</td><td>8.77%</td><td>10.19%</td><td>12.58%</td><td>12.99%</td><td>11.15%</td><td>13.49%</td><td>14.17%</td><td>13.59%</td><td>11.00%</td></tr>
-<tr class="section-row"><td class="section-label">资产占比</td><td class="section-spacer" colspan="12"></td></tr>
-<tr><td class="metric-name">现金占比</td><td>36.18%</td><td>38.58%</td><td>36.85%</td><td>31.79%</td><td>28.95%</td><td>43.69%</td><td>31.84%</td><td>37.53%</td><td>28.35%</td><td>8.59%</td><td>14.91%</td><td>2.07%</td></tr>
-<tr><td class="metric-name">库存占比</td><td>8.95%</td><td>7.61%</td><td>6.34%</td><td>12.76%</td><td>13.07%</td><td>8.44%</td><td>11.33%</td><td>9.58%</td><td>6.88%</td><td>4.76%</td><td>12.01%</td><td>10.85%</td></tr>
-<tr><td class="metric-name">应收款占比</td><td>7.42%</td><td>8.15%</td><td>8.93%</td><td>9.65%</td><td>7.72%</td><td>7.21%</td><td>8.23%</td><td>8.43%</td><td>13.93%</td><td>25.59%</td><td>27.60%</td><td>12.92%</td></tr>
-<tr><td class="metric-name">预付款占比</td><td>1.51%</td><td>0.76%</td><td>0.97%</td><td>2.64%</td><td>2.10%</td><td>0.64%</td><td>0.53%</td><td>1.17%</td><td>0.62%</td><td>0.35%</td><td>0.81%</td><td>0.53%</td></tr>
-<tr><td class="metric-name">固定资产占比</td><td>14.35%</td><td>14.31%</td><td>16.09%</td><td>14.82%</td><td>13.42%</td><td>12.53%</td><td>17.19%</td><td>15.67%</td><td>16.55%</td><td>13.04%</td><td>15.14%</td><td>8.45%</td></tr>
-<tr><td class="metric-name">长期投资占比</td><td>6.68%</td><td>6.97%</td><td>6.98%</td><td>2.93%</td><td>3.56%</td><td>3.07%</td><td>1.52%</td><td>1.31%</td><td>1.59%</td><td>0.59%</td><td>-</td><td>7.90%</td></tr>
-<tr><td class="metric-name">商誉占比</td><td>0.10%</td><td>0.11%</td><td>0.10%</td><td>0.12%</td><td>0.17%</td><td>0.09%</td><td>0.15%</td><td>0.14%</td><td>0.20%</td><td>0.35%</td><td>1.16%</td><td>-</td></tr>
-<tr class="other-assets-row"><td class="metric-name">其他资产占比</td><td>24.80%</td><td>23.51%</td><td>23.75%</td><td>25.30%</td><td>31.02%</td><td>24.32%</td><td>29.22%</td><td>26.19%</td><td>31.87%</td><td>46.72%</td><td>28.37%</td><td>57.27%</td></tr>
-<tr><td class="metric-name">应付款占比</td><td>14.73%</td><td>16.65%</td><td>16.32%</td><td>15.73%</td><td>15.86%</td><td>9.98%</td><td>10.55%</td><td>9.55%</td><td>10.02%</td><td>11.10%</td><td>16.98%</td><td>13.38%</td></tr>
-<tr><td class="metric-name">预收款占比</td><td>4.54%</td><td>3.54%</td><td>3.34%</td><td>3.73%</td><td>3.75%</td><td>4.39%</td><td>6.08%</td><td>6.76%</td><td>0.41%</td><td>0.31%</td><td>1.96%</td><td>0.30%</td></tr>
-<tr><td class="metric-name">短期借款占比</td><td>4.50%</td><td>5.41%</td><td>3.09%</td><td>3.60%</td><td>5.09%</td><td>4.91%</td><td>3.16%</td><td>2.85%</td><td>5.26%</td><td>5.13%</td><td>21.90%</td><td>11.29%</td></tr>
-<tr><td class="metric-name">长期借款占比</td><td>8.75%</td><td>10.33%</td><td>11.64%</td><td>9.83%</td><td>7.19%</td><td>3.87%</td><td>4.91%</td><td>4.72%</td><td>4.29%</td><td>1.06%</td><td>0.00%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">运营资产占比</td><td>-1.38%</td><td>-3.67%</td><td>-3.43%</td><td>5.57%</td><td>3.28%</td><td>1.92%</td><td>3.46%</td><td>2.86%</td><td>11.00%</td><td>19.29%</td><td>21.49%</td><td>10.63%</td></tr>
-<tr><td class="metric-name">有息负债率</td><td>13.25%</td><td>15.74%</td><td>14.73%</td><td>13.44%</td><td>12.28%</td><td>8.78%</td><td>8.07%</td><td>7.58%</td><td>9.54%</td><td>6.19%</td><td>21.90%</td><td>11.29%</td></tr>
-<tr class="section-row"><td class="section-label">周转能力</td><td class="section-spacer" colspan="12"></td></tr>
-<tr><td class="metric-name">存货周转天数</td><td>138</td><td>79</td><td>53</td><td>106</td><td>152</td><td>132</td><td>129</td><td>129</td><td>97</td><td>59</td><td>108</td><td>176</td></tr>
-<tr><td class="metric-name">应收款周转天数</td><td>85</td><td>64</td><td>58</td><td>64</td><td>66</td><td>81</td><td>66</td><td>76</td><td>126</td><td>179</td><td>153</td><td>156</td></tr>
-<tr><td class="metric-name">应付款周转天数</td><td>227</td><td>174</td><td>138</td><td>131</td><td>185</td><td>156</td><td>120</td><td>129</td><td>142</td><td>138</td><td>153</td><td>218</td></tr>
-<tr><td class="metric-name">固定资产周转率</td><td>2.20</td><td>3.22</td><td>3.47</td><td>3.69</td><td>3.16</td><td>2.56</td><td>2.63</td><td>2.56</td><td>2.43</td><td>3.99</td><td>4.34</td><td>3.57</td></tr>
-<tr><td class="metric-name">总资产周转率</td><td>0.32</td><td>0.46</td><td>0.56</td><td>0.55</td><td>0.42</td><td>0.32</td><td>0.45</td><td>0.40</td><td>0.40</td><td>0.52</td><td>0.66</td><td>0.30</td></tr>
-<tr class="section-row"><td class="section-label">人均效率</td><td class="section-spacer" colspan="12"></td></tr>
-<tr><td class="metric-name">员工人数</td><td>-</td><td>131,988</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均创收(万)</td><td>-</td><td>274.28</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均创利(万)</td><td>-</td><td>38.45</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均薪酬(万)</td><td>-</td><td>19.32</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr class="section-row"><td class="section-label">市场表现</td><td class="section-spacer" colspan="12"></td></tr>
-<tr><td class="metric-name">股价</td><td>367.26</td><td>266.00</td><td>163.26</td><td>393.42</td><td>588.00</td><td>351.11</td><td>106.40</td><td>73.80</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">市值(亿)</td><td>16,760</td><td>11,713</td><td>7,182</td><td>9,609</td><td>13,705</td><td>8,179</td><td>2,350</td><td>1,620</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">PE</td><td>33.03</td><td>26.55</td><td>23.37</td><td>60.32</td><td>245.47</td><td>179.35</td><td>69.37</td><td>41.77</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">PB</td><td>5.33</td><td>4.94</td><td>3.98</td><td>6.40</td><td>18.74</td><td>13.33</td><td>6.36</td><td>5.01</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">股东户数</td><td>227,474</td><td>212,061</td><td>260,992</td><td>183,317</td><td>141,963</td><td>133,060</td><td>76,710</td><td>82,514</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
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data/CN/300750.SZ/report.md

@@ -1,89 +0,0 @@
-# 宁德时代 (300750.SZ) - Financial Report
-*Report generated on: 2026-01-03*
-
-| 代码 | 简称 | 上市日期 | PE | PB | 股息率(%) |
-|:---|:---|:---|:---|:---|:---|
-| 300750.SZ | 宁德时代 | 2018-06-11 | 33.03 | 5.33 | 1.84% |
-
-
-## 主要指标
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A | 2015A | 2014A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| ROE | 15.60% | 20.55% | 22.32% | 18.68% | 18.85% | 8.70% | 11.96% | 10.28% | 15.70% | 18.41% | 74.23% | 21.19% |
-| ROA | 5.47% | 6.45% | 6.15% | 5.11% | 5.18% | 3.56% | 4.50% | 4.58% | 7.81% | 9.98% | 10.73% | 1.89% |
-| ROCE/ROIC | 11.87% | 16.58% | 16.87% | 12.81% | 14.82% | 8.07% | 12.35% | 10.67% | 12.26% | 19.55% | 36.60% | 4.72% |
-| 毛利率 | 25.31% | 24.44% | 22.91% | 20.25% | 26.28% | 27.76% | 29.06% | 32.79% | 36.29% | 43.70% | 38.64% | 25.73% |
-| 净利润率 | 17.32% | 14.02% | 11.01% | 9.35% | 12.22% | 11.10% | 9.96% | 11.44% | 19.39% | 19.17% | 16.32% | 6.28% |
-| 收入(亿) | 2,830.72 | 3,620.13 | 4,009.17 | 3,285.94 | 1,303.56 | 503.19 | 457.88 | 296.11 | 199.97 | 148.79 | 57.03 | 8.67 |
-| 收入增速 | 9.28% | -9.70% | 22.01% | 152.07% | 159.06% | 9.90% | 54.63% | 48.08% | 34.40% | 160.90% | 557.93% | - |
-| 净利润(亿) | 490.34 | 507.45 | 441.21 | 307.29 | 159.31 | 55.83 | 45.60 | 33.87 | 38.78 | 28.52 | 9.31 | 0.54 |
-| 净利润增速 | 36.20% | 15.01% | 43.58% | 92.89% | 185.34% | 22.43% | 34.64% | -12.66% | 35.98% | 206.43% | 1609.94% | - |
-| 经营净现金流(亿) | 806.60 | 969.90 | 928.26 | 612.09 | 429.08 | 184.30 | 134.72 | 113.16 | 23.41 | 21.09 | 6.65 | -1.39 |
-| 资本开支(亿) | 300.88 | 311.80 | 336.25 | 482.15 | 437.68 | 133.02 | 96.27 | 66.29 | 71.80 | 28.01 | 15.54 | 3.01 |
-| 自由现金流(亿) | 505.73 | 658.10 | 592.01 | 129.94 | -8.60 | 51.28 | 38.45 | 46.87 | -48.40 | -6.92 | -8.89 | -4.39 |
-| 分红(亿) | - | 152.80 | 221.18 | 126.72 | - | 5.59 | 4.86 | 3.12 | - | - | - | - |
-| 回购(亿) | - | 129.80 | 129.84 | 0.32 | 0.90 | 2.54 | 1.39 | - | - | - | - | - |
-| 总资产(亿) | 8,960.82 | 7,866.58 | 7,171.68 | 6,009.52 | 3,076.67 | 1,566.18 | 1,013.52 | 738.84 | 496.63 | 285.88 | 86.73 | 28.75 |
-| 净资产(亿) | 3,142.48 | 2,469.30 | 1,977.08 | 1,644.81 | 845.13 | 642.07 | 381.35 | 329.38 | 247.01 | 154.89 | 12.54 | 2.57 |
-| 商誉(亿) | 8.91 | 8.95 | 7.08 | 7.04 | 5.28 | 1.48 | 1.48 | 1.00 | 1.00 | 1.00 | 1.00 | - |
-
-
-## 费用指标
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A | 2015A | 2014A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 销售费用率 | 0.85% | 0.98% | 4.48% | 3.38% | 3.35% | 4.41% | 4.71% | 4.66% | 3.98% | 4.25% | 5.80% | 4.99% |
-| 管理费用率 | 2.91% | 2.68% | 2.11% | 2.12% | 2.58% | 3.51% | 4.00% | 5.37% | 14.78% | 14.46% | 10.88% | 17.57% |
-| SG&A比例 | 3.76% | 3.66% | 6.59% | 5.50% | 5.94% | 7.92% | 8.71% | 10.03% | 18.76% | 18.71% | 16.68% | 22.57% |
-| 研发费用率 | 5.32% | 5.14% | 4.58% | 4.72% | 5.90% | 7.09% | 6.53% | 6.72% | - | - | - | - |
-| 其他费用率 | -1.09% | 1.63% | 0.74% | 0.68% | 2.23% | 1.66% | 3.85% | 4.60% | -1.87% | 5.82% | 5.64% | -3.11% |
-| 折旧费用占比 | - | 6.20% | 5.26% | 3.64% | 4.49% | 9.10% | 9.04% | 7.18% | 6.40% | 4.92% | 2.88% | 5.14% |
-| 所得税率 | 13.86% | 14.52% | 13.27% | 8.77% | 10.19% | 12.58% | 12.99% | 11.15% | 13.49% | 14.17% | 13.59% | 11.00% |
-
-
-## 资产占比
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A | 2015A | 2014A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 现金占比 | 36.18% | 38.58% | 36.85% | 31.79% | 28.95% | 43.69% | 31.84% | 37.53% | 28.35% | 8.59% | 14.91% | 2.07% |
-| 库存占比 | 8.95% | 7.61% | 6.34% | 12.76% | 13.07% | 8.44% | 11.33% | 9.58% | 6.88% | 4.76% | 12.01% | 10.85% |
-| 应收款占比 | 7.42% | 8.15% | 8.93% | 9.65% | 7.72% | 7.21% | 8.23% | 8.43% | 13.93% | 25.59% | 27.60% | 12.92% |
-| 预付款占比 | 1.51% | 0.76% | 0.97% | 2.64% | 2.10% | 0.64% | 0.53% | 1.17% | 0.62% | 0.35% | 0.81% | 0.53% |
-| 固定资产占比 | 14.35% | 14.31% | 16.09% | 14.82% | 13.42% | 12.53% | 17.19% | 15.67% | 16.55% | 13.04% | 15.14% | 8.45% |
-| 长期投资占比 | 6.68% | 6.97% | 6.98% | 2.93% | 3.56% | 3.07% | 1.52% | 1.31% | 1.59% | 0.59% | - | 7.90% |
-| 商誉占比 | 0.10% | 0.11% | 0.10% | 0.12% | 0.17% | 0.09% | 0.15% | 0.14% | 0.20% | 0.35% | 1.16% | - |
-| 其他资产占比 | 24.80% | 23.51% | 23.75% | 25.30% | 31.02% | 24.32% | 29.22% | 26.19% | 31.87% | 46.72% | 28.37% | 57.27% |
-| 应付款占比 | 14.73% | 16.65% | 16.32% | 15.73% | 15.86% | 9.98% | 10.55% | 9.55% | 10.02% | 11.10% | 16.98% | 13.38% |
-| 预收款占比 | 4.54% | 3.54% | 3.34% | 3.73% | 3.75% | 4.39% | 6.08% | 6.76% | 0.41% | 0.31% | 1.96% | 0.30% |
-| 短期借款占比 | 4.50% | 5.41% | 3.09% | 3.60% | 5.09% | 4.91% | 3.16% | 2.85% | 5.26% | 5.13% | 21.90% | 11.29% |
-| 长期借款占比 | 8.75% | 10.33% | 11.64% | 9.83% | 7.19% | 3.87% | 4.91% | 4.72% | 4.29% | 1.06% | 0.00% | 0.00% |
-| 运营资产占比 | -1.38% | -3.67% | -3.43% | 5.57% | 3.28% | 1.92% | 3.46% | 2.86% | 11.00% | 19.29% | 21.49% | 10.63% |
-| 有息负债率 | 13.25% | 15.74% | 14.73% | 13.44% | 12.28% | 8.78% | 8.07% | 7.58% | 9.54% | 6.19% | 21.90% | 11.29% |
-
-
-## 周转能力
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A | 2015A | 2014A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 存货周转天数 | 138 | 79 | 53 | 106 | 152 | 132 | 129 | 129 | 97 | 59 | 108 | 176 |
-| 应收款周转天数 | 85 | 64 | 58 | 64 | 66 | 81 | 66 | 76 | 126 | 179 | 153 | 156 |
-| 应付款周转天数 | 227 | 174 | 138 | 131 | 185 | 156 | 120 | 129 | 142 | 138 | 153 | 218 |
-| 固定资产周转率 | 2.20 | 3.22 | 3.47 | 3.69 | 3.16 | 2.56 | 2.63 | 2.56 | 2.43 | 3.99 | 4.34 | 3.57 |
-| 总资产周转率 | 0.32 | 0.46 | 0.56 | 0.55 | 0.42 | 0.32 | 0.45 | 0.40 | 0.40 | 0.52 | 0.66 | 0.30 |
-
-
-## 人均效率
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A | 2015A | 2014A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 员工人数 | - | 131,988 | - | - | - | - | - | - | - | - | - | - |
-| 人均创收(万) | - | 274.28 | - | - | - | - | - | - | - | - | - | - |
-| 人均创利(万) | - | 38.45 | - | - | - | - | - | - | - | - | - | - |
-| 人均薪酬(万) | - | 19.32 | - | - | - | - | - | - | - | - | - | - |
-
-
-## 市场表现
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A | 2015A | 2014A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 股价 | 367.26 | 266.00 | 163.26 | 393.42 | 588.00 | 351.11 | 106.40 | 73.80 | - | - | - | - |
-| 市值(亿) | 16,760 | 11,713 | 7,182 | 9,609 | 13,705 | 8,179 | 2,350 | 1,620 | - | - | - | - |
-| PE | 33.03 | 26.55 | 23.37 | 60.32 | 245.47 | 179.35 | 69.37 | 41.77 | - | - | - | - |
-| PB | 5.33 | 4.94 | 3.98 | 6.40 | 18.74 | 13.33 | 6.36 | 5.01 | - | - | - | - |
-| 股东户数 | 227,474 | 212,061 | 260,992 | 183,317 | 141,963 | 133,060 | 76,710 | 82,514 | - | - | - | - |
-

data/CN/688334.SH/historical_metrics.csv

@@ -1,9 +0,0 @@
-date_str,Price,PE,PB,MarketCap,Shareholders
-20250930,19.28,26.6914,2.1851,6103652104.0,12225.0
-20241231,16.33,31.8397,1.8835,5169742680.0,12364.0
-20240930,17.66,34.4329,1.844,5590793370.000001,12979.0
-20231231,17.33,42.0868,1.8442,5486322145.999999,15965.0
-20221231,,,,,7.0
-20211231,,,,,
-20201231,,,,,
-20191231,,,,,

data/CN/688334.SH/raw_balance_sheet.csv

@@ -1,20 +0,0 @@
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data/CN/688334.SH/raw_cash_flow.csv

@@ -1,26 +0,0 @@
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data/CN/688334.SH/raw_dividends_raw.csv

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data/CN/688334.SH/raw_historical_metrics_daily_basic.csv

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data/CN/688334.SH/raw_historical_metrics_shareholder_number.csv

@@ -1,14 +0,0 @@
-ts_code,ann_date,end_date,holder_num
-688334.SH,20250821,20250630,12225
-688334.SH,20250411,20250331,12082
-688334.SH,20250411,20241231,12364
-688334.SH,20241030,20240930,12979
-688334.SH,20240830,20240630,14145
-688334.SH,20240411,20240331,14265
-688334.SH,20240430,20240331,14265
-688334.SH,20240411,20231231,15965
-688334.SH,20240103,20231229,15965
-688334.SH,20231028,20230930,22857
-688334.SH,20230830,20230630,30695
-688334.SH,20230616,20230619,66464
-688334.SH,20220630,20220630,7

data/CN/688334.SH/raw_income_statement.csv

@@ -1,21 +0,0 @@
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data/CN/688334.SH/raw_market_metrics_daily_basic.csv

@@ -1,2 +0,0 @@
-ts_code,trade_date,close,turnover_rate,turnover_rate_f,volume_ratio,pe,pe_ttm,pb,ps,ps_ttm,dv_ratio,dv_ttm,total_share,float_share,free_share,total_mv,circ_mv
-688334.SH,20251231,19.36,0.9076,1.4243,0.73,26.8022,23.2755,2.1444,7.677,6.9371,,,31657.9466,15690.4698,9998.6609,612897.8462,303767.4953

data/CN/688334.SH/raw_market_metrics_shareholder_number.csv

@@ -1,2 +0,0 @@
-ts_code,ann_date,end_date,holder_num
-688334.SH,20251022,20250930,11192

data/CN/688334.SH/raw_market_metrics_stock_basic.csv

@@ -1,2 +0,0 @@
-name,list_date
-西高院,20230619

data/CN/688334.SH/report.html

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-                    <h1>西高院 (688334.SH) - Financial Report</h1>
-<p><em>Report generated on: 2026-01-03</em></p>
-
-        <table class="company-table">
-            <thead>
-                <tr>
-                    <th>代码</th>
-                    <th>简称</th>
-                    <th>上市日期</th>
-                    <th>PE</th>
-                    <th>PB</th>
-                    <th>股息率(%)</th>
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-                    <td>688334.SH</td>
-                    <td>西高院</td>
-                    <td>2023-06-19</td>
-                    <td>26.80</td>
-                    <td>2.14</td>
-                    <td>0.00%</td>
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-                        <th>指标</th>
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-                    <tr class="section-row"><td class="section-label">主要指标</td><td class="section-spacer" colspan="7"></td></tr>
-<tr><td class="metric-name">ROE</td><td>6.91%</td><td>8.23%</td><td>5.39%</td><td>7.04%</td><td>4.74%</td><td>9.54%</td><td>18.05%</td></tr>
-<tr><td class="metric-name">ROA</td><td>5.36%</td><td>6.45%</td><td>4.73%</td><td>5.78%</td><td>3.76%</td><td>5.10%</td><td>9.45%</td></tr>
-<tr><td class="metric-name">ROCE/ROIC</td><td>7.86%</td><td>8.18%</td><td>4.71%</td><td>6.85%</td><td>5.53%</td><td>11.12%</td><td>20.30%</td></tr>
-<tr><td class="metric-name">毛利率</td><td>55.09%</td><td>52.72%</td><td>47.67%</td><td>48.18%</td><td>39.31%</td><td>43.95%</td><td>43.85%</td></tr>
-<tr><td class="metric-name">净利润率</td><td>30.34%</td><td>28.64%</td><td>26.97%</td><td>25.01%</td><td>18.24%</td><td>18.89%</td><td>23.76%</td></tr>
-<tr><td class="metric-name">收入(亿)</td><td>6.51</td><td>7.98</td><td>6.02</td><td>5.21</td><td>4.47</td><td>3.45</td><td>4.00</td></tr>
-<tr><td class="metric-name">收入增速</td><td>15.05%</td><td>32.60%</td><td>15.53%</td><td>16.54%</td><td>29.71%</td><td>-13.81%</td><td>-</td></tr>
-<tr><td class="metric-name">净利润(亿)</td><td>1.98</td><td>2.29</td><td>1.62</td><td>1.30</td><td>0.82</td><td>0.65</td><td>0.95</td></tr>
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-<tr><td class="metric-name">经营净现金流(亿)</td><td>3.53</td><td>4.82</td><td>2.63</td><td>1.97</td><td>1.54</td><td>1.36</td><td>1.32</td></tr>
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-<tr><td class="metric-name">自由现金流(亿)</td><td>2.55</td><td>3.39</td><td>1.88</td><td>1.18</td><td>0.95</td><td>0.66</td><td>0.83</td></tr>
-<tr><td class="metric-name">分红(亿)</td><td>-</td><td>1.26</td><td>0.81</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
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-<tr><td class="metric-name">总资产(亿)</td><td>36.81</td><td>35.48</td><td>34.36</td><td>22.55</td><td>21.72</td><td>12.78</td><td>10.06</td></tr>
-<tr><td class="metric-name">净资产(亿)</td><td>28.58</td><td>27.78</td><td>30.14</td><td>18.51</td><td>17.19</td><td>6.82</td><td>5.26</td></tr>
-<tr><td class="metric-name">商誉(亿)</td><td>0.24</td><td>0.24</td><td>0.24</td><td>0.24</td><td>0.26</td><td>-</td><td>-</td></tr>
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-<tr><td class="metric-name">销售费用率</td><td>1.87%</td><td>2.37%</td><td>2.35%</td><td>2.27%</td><td>1.59%</td><td>1.99%</td><td>1.64%</td></tr>
-<tr><td class="metric-name">管理费用率</td><td>10.35%</td><td>11.79%</td><td>14.27%</td><td>15.29%</td><td>14.18%</td><td>16.69%</td><td>14.27%</td></tr>
-<tr><td class="metric-name">SG&A比例</td><td>12.22%</td><td>14.15%</td><td>16.63%</td><td>17.55%</td><td>15.77%</td><td>18.67%</td><td>15.91%</td></tr>
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-<tr><td class="metric-name">折旧费用占比</td><td>-</td><td>15.47%</td><td>16.90%</td><td>21.16%</td><td>23.19%</td><td>26.46%</td><td>20.07%</td></tr>
-<tr><td class="metric-name">所得税率</td><td>14.78%</td><td>9.54%</td><td>10.90%</td><td>7.44%</td><td>13.99%</td><td>14.35%</td><td>14.49%</td></tr>
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-<tr><td class="metric-name">固定资产占比</td><td>-</td><td>30.55%</td><td>24.03%</td><td>38.67%</td><td>42.10%</td><td>57.06%</td><td>56.15%</td></tr>
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-<tr><td class="metric-name">应付款占比</td><td>2.00%</td><td>3.51%</td><td>1.12%</td><td>1.97%</td><td>1.82%</td><td>2.90%</td><td>7.02%</td></tr>
-<tr><td class="metric-name">预收款占比</td><td>7.97%</td><td>7.96%</td><td>5.54%</td><td>7.20%</td><td>7.46%</td><td>13.49%</td><td>12.66%</td></tr>
-<tr><td class="metric-name">短期借款占比</td><td>0.13%</td><td>0.21%</td><td>0.22%</td><td>0.31%</td><td>0.31%</td><td>0.30%</td><td>0.38%</td></tr>
-<tr><td class="metric-name">长期借款占比</td><td>0.91%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">运营资产占比</td><td>-4.82%</td><td>-7.08%</td><td>-3.91%</td><td>-2.17%</td><td>-1.92%</td><td>-4.55%</td><td>-8.49%</td></tr>
-<tr><td class="metric-name">有息负债率</td><td>1.05%</td><td>0.21%</td><td>0.22%</td><td>0.31%</td><td>0.31%</td><td>0.30%</td><td>0.38%</td></tr>
-<tr class="section-row"><td class="section-label">周转能力</td><td class="section-spacer" colspan="7"></td></tr>
-<tr><td class="metric-name">存货周转天数</td><td>69</td><td>50</td><td>62</td><td>107</td><td>100</td><td>174</td><td>104</td></tr>
-<tr><td class="metric-name">应收款周转天数</td><td>68</td><td>44</td><td>21</td><td>40</td><td>61</td><td>55</td><td>40</td></tr>
-<tr><td class="metric-name">应付款周转天数</td><td>92</td><td>120</td><td>44</td><td>60</td><td>53</td><td>70</td><td>114</td></tr>
-<tr><td class="metric-name">固定资产周转率</td><td>-</td><td>0.74</td><td>0.73</td><td>0.60</td><td>0.49</td><td>0.47</td><td>0.71</td></tr>
-<tr><td class="metric-name">总资产周转率</td><td>0.18</td><td>0.23</td><td>0.18</td><td>0.23</td><td>0.21</td><td>0.27</td><td>0.40</td></tr>
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-<tr><td class="metric-name">人均创收(万)</td><td>-</td><td>114.05</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均创利(万)</td><td>-</td><td>32.67</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均薪酬(万)</td><td>-</td><td>31.98</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
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-<tr><td class="metric-name">股价</td><td>19.36</td><td>16.33</td><td>17.33</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
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-<tr><td class="metric-name">股东户数</td><td>11,192</td><td>12,364</td><td>15,965</td><td>7</td><td>-</td><td>-</td><td>-</td></tr>
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data/CN/688334.SH/report.md

@@ -1,89 +0,0 @@
-# 西高院 (688334.SH) - Financial Report
-*Report generated on: 2026-01-03*
-
-| 代码 | 简称 | 上市日期 | PE | PB | 股息率(%) |
-|:---|:---|:---|:---|:---|:---|
-| 688334.SH | 西高院 | 2023-06-19 | 26.80 | 2.14 | 0.00% |
-
-
-## 主要指标
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A |
-|:---|--:|--:|--:|--:|--:|--:|--:|
-| ROE | 6.91% | 8.23% | 5.39% | 7.04% | 4.74% | 9.54% | 18.05% |
-| ROA | 5.36% | 6.45% | 4.73% | 5.78% | 3.76% | 5.10% | 9.45% |
-| ROCE/ROIC | 7.86% | 8.18% | 4.71% | 6.85% | 5.53% | 11.12% | 20.30% |
-| 毛利率 | 55.09% | 52.72% | 47.67% | 48.18% | 39.31% | 43.95% | 43.85% |
-| 净利润率 | 30.34% | 28.64% | 26.97% | 25.01% | 18.24% | 18.89% | 23.76% |
-| 收入(亿) | 6.51 | 7.98 | 6.02 | 5.21 | 4.47 | 3.45 | 4.00 |
-| 收入增速 | 15.05% | 32.60% | 15.53% | 16.54% | 29.71% | -13.81% | - |
-| 净利润(亿) | 1.98 | 2.29 | 1.62 | 1.30 | 0.82 | 0.65 | 0.95 |
-| 净利润增速 | 21.28% | 40.84% | 24.56% | 59.83% | 25.24% | -31.46% | - |
-| 经营净现金流(亿) | 3.53 | 4.82 | 2.63 | 1.97 | 1.54 | 1.36 | 1.32 |
-| 资本开支(亿) | 0.98 | 1.43 | 0.75 | 0.79 | 0.59 | 0.69 | 0.48 |
-| 自由现金流(亿) | 2.55 | 3.39 | 1.88 | 1.18 | 0.95 | 0.66 | 0.83 |
-| 分红(亿) | - | 1.26 | 0.81 | - | - | - | - |
-| 回购(亿) | - | - | - | - | - | - | - |
-| 总资产(亿) | 36.81 | 35.48 | 34.36 | 22.55 | 21.72 | 12.78 | 10.06 |
-| 净资产(亿) | 28.58 | 27.78 | 30.14 | 18.51 | 17.19 | 6.82 | 5.26 |
-| 商誉(亿) | 0.24 | 0.24 | 0.24 | 0.24 | 0.26 | - | - |
-
-
-## 费用指标
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A |
-|:---|--:|--:|--:|--:|--:|--:|--:|
-| 销售费用率 | 1.87% | 2.37% | 2.35% | 2.27% | 1.59% | 1.99% | 1.64% |
-| 管理费用率 | 10.35% | 11.79% | 14.27% | 15.29% | 14.18% | 16.69% | 14.27% |
-| SG&A比例 | 12.22% | 14.15% | 16.63% | 17.55% | 15.77% | 18.67% | 15.91% |
-| 研发费用率 | 7.91% | 10.62% | 10.12% | 9.56% | 7.75% | 7.66% | 6.07% |
-| 其他费用率 | 4.62% | -0.70% | -6.04% | -3.94% | -2.44% | -1.28% | -1.88% |
-| 折旧费用占比 | - | 15.47% | 16.90% | 21.16% | 23.19% | 26.46% | 20.07% |
-| 所得税率 | 14.78% | 9.54% | 10.90% | 7.44% | 13.99% | 14.35% | 14.49% |
-
-
-## 资产占比
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A |
-|:---|--:|--:|--:|--:|--:|--:|--:|
-| 现金占比 | 26.03% | 29.72% | 25.14% | 12.70% | 20.97% | 14.62% | 22.31% |
-| 库存占比 | 1.52% | 1.46% | 1.57% | 3.51% | 3.43% | 7.21% | 6.39% |
-| 应收款占比 | 3.34% | 2.74% | 1.04% | 2.56% | 3.46% | 4.14% | 4.45% |
-| 预付款占比 | 0.29% | 0.19% | 0.14% | 0.94% | 0.47% | 0.49% | 0.35% |
-| 固定资产占比 | - | 30.55% | 24.03% | 38.67% | 42.10% | 57.06% | 56.15% |
-| 长期投资占比 | 0.91% | 0.94% | 0.96% | 1.49% | 1.66% | 3.27% | - |
-| 商誉占比 | 0.64% | 0.67% | 0.69% | 1.08% | 1.18% | - | - |
-| 其他资产占比 | 67.27% | 33.74% | 46.43% | 39.05% | 26.73% | 13.21% | 10.36% |
-| 应付款占比 | 2.00% | 3.51% | 1.12% | 1.97% | 1.82% | 2.90% | 7.02% |
-| 预收款占比 | 7.97% | 7.96% | 5.54% | 7.20% | 7.46% | 13.49% | 12.66% |
-| 短期借款占比 | 0.13% | 0.21% | 0.22% | 0.31% | 0.31% | 0.30% | 0.38% |
-| 长期借款占比 | 0.91% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
-| 运营资产占比 | -4.82% | -7.08% | -3.91% | -2.17% | -1.92% | -4.55% | -8.49% |
-| 有息负债率 | 1.05% | 0.21% | 0.22% | 0.31% | 0.31% | 0.30% | 0.38% |
-
-
-## 周转能力
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A |
-|:---|--:|--:|--:|--:|--:|--:|--:|
-| 存货周转天数 | 69 | 50 | 62 | 107 | 100 | 174 | 104 |
-| 应收款周转天数 | 68 | 44 | 21 | 40 | 61 | 55 | 40 |
-| 应付款周转天数 | 92 | 120 | 44 | 60 | 53 | 70 | 114 |
-| 固定资产周转率 | - | 0.74 | 0.73 | 0.60 | 0.49 | 0.47 | 0.71 |
-| 总资产周转率 | 0.18 | 0.23 | 0.18 | 0.23 | 0.21 | 0.27 | 0.40 |
-
-
-## 人均效率
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A |
-|:---|--:|--:|--:|--:|--:|--:|--:|
-| 员工人数 | - | 700 | - | - | - | - | - |
-| 人均创收(万) | - | 114.05 | - | - | - | - | - |
-| 人均创利(万) | - | 32.67 | - | - | - | - | - |
-| 人均薪酬(万) | - | 31.98 | - | - | - | - | - |
-
-
-## 市场表现
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A |
-|:---|--:|--:|--:|--:|--:|--:|--:|
-| 股价 | 19.36 | 16.33 | 17.33 | - | - | - | - |
-| 市值(亿) | 61.29 | 51.70 | 54.86 | - | - | - | - |
-| PE | 26.80 | 31.84 | 42.09 | - | - | - | - |
-| PB | 2.14 | 1.88 | 1.84 | - | - | - | - |
-| 股东户数 | 11,192 | 12,364 | 15,965 | 7 | - | - | - |
-

data/CN/688778.SH/historical_metrics.csv

@@ -1,12 +0,0 @@
-date_str,Price,PE,PB,MarketCap,Shareholders
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-20241231,45.64,36.4088,2.2251,19203988486.0,14740.0
-20240930,36.74,29.3089,1.8186,15459126577.000002,13770.0
-20231231,39.64,14.885,1.9711,16679362480.0,13732.0
-20221231,77.68,42.0398,2.934,23346779541.000004,14462.0
-20211231,103.35,103.7733,7.2703,25999993794.0,15688.0
-20201231,,,,,
-20191231,,,,,
-20181231,,,,,
-20171231,,,,,
-20161231,,,,,

data/CN/688778.SH/raw_balance_sheet.csv

@@ -1,37 +0,0 @@
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data/CN/688778.SH/raw_cash_flow.csv

@@ -1,37 +0,0 @@
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data/CN/688778.SH/raw_dividends_raw.csv

@@ -1,19 +0,0 @@
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data/CN/688778.SH/raw_historical_metrics_shareholder_number.csv

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data/CN/688778.SH/raw_income_statement.csv

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data/CN/688778.SH/raw_market_metrics_daily_basic.csv

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data/CN/688778.SH/raw_market_metrics_shareholder_number.csv

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data/CN/688778.SH/raw_market_metrics_stock_basic.csv

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data/CN/688778.SH/raw_repurchases.csv

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-                    <h1>厦钨新能 (688778.SH) - Financial Report</h1>
-<p><em>Report generated on: 2026-01-03</em></p>
-
-        <table class="company-table">
-            <thead>
-                <tr>
-                    <th>代码</th>
-                    <th>简称</th>
-                    <th>上市日期</th>
-                    <th>PE</th>
-                    <th>PB</th>
-                    <th>股息率(%)</th>
-                </tr>
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-            <tbody>
-                <tr>
-                    <td>688778.SH</td>
-                    <td>厦钨新能</td>
-                    <td>2021-08-05</td>
-                    <td>79.03</td>
-                    <td>4.33</td>
-                    <td>0.00%</td>
-                </tr>
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-                        <th>指标</th>
-                        <th>2025Q3</th><th>2024A</th><th>2023A</th><th>2022A</th><th>2021A</th><th>2020A</th><th>2019A</th><th>2018A</th><th>2017A</th><th>2016A</th>
-                    </tr>
-                </thead>
-                <tbody>
-                    <tr class="section-row"><td class="section-label">主要指标</td><td class="section-spacer" colspan="10"></td></tr>
-<tr><td class="metric-name">ROE</td><td>6.13%</td><td>5.65%</td><td>6.15%</td><td>13.67%</td><td>14.87%</td><td>14.33%</td><td>9.93%</td><td>10.73%</td><td>24.90%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">ROA</td><td>3.17%</td><td>3.35%</td><td>3.91%</td><td>7.29%</td><td>5.28%</td><td>4.03%</td><td>2.81%</td><td>1.44%</td><td>4.40%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">ROCE/ROIC</td><td>6.71%</td><td>5.83%</td><td>6.83%</td><td>13.99%</td><td>13.65%</td><td>9.48%</td><td>6.43%</td><td>9.16%</td><td>29.07%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">毛利率</td><td>10.00%</td><td>9.76%</td><td>7.99%</td><td>8.60%</td><td>9.55%</td><td>10.48%</td><td>8.06%</td><td>10.09%</td><td>12.70%</td><td>-</td></tr>
-<tr><td class="metric-name">净利润率</td><td>4.23%</td><td>3.72%</td><td>3.05%</td><td>3.90%</td><td>3.57%</td><td>3.14%</td><td>2.15%</td><td>1.14%</td><td>3.94%</td><td>-</td></tr>
-<tr><td class="metric-name">收入(亿)</td><td>130.59</td><td>132.97</td><td>173.11</td><td>287.51</td><td>155.66</td><td>79.90</td><td>69.78</td><td>70.26</td><td>42.11</td><td>0.00</td></tr>
-<tr><td class="metric-name">收入增速</td><td>32.15%</td><td>-23.19%</td><td>-39.79%</td><td>84.71%</td><td>94.82%</td><td>14.50%</td><td>-0.69%</td><td>66.84%</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">净利润(亿)</td><td>5.52</td><td>4.94</td><td>5.27</td><td>11.21</td><td>5.55</td><td>2.51</td><td>1.50</td><td>0.80</td><td>1.66</td><td>0.00</td></tr>
-<tr><td class="metric-name">净利润增速</td><td>50.26%</td><td>-6.33%</td><td>-52.93%</td><td>101.77%</td><td>121.66%</td><td>66.94%</td><td>87.35%</td><td>-51.76%</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">经营净现金流(亿)</td><td>8.97</td><td>17.53</td><td>25.80</td><td>-15.61</td><td>4.32</td><td>3.97</td><td>1.72</td><td>1.11</td><td>-5.94</td><td>-</td></tr>
-<tr><td class="metric-name">资本开支(亿)</td><td>4.89</td><td>9.66</td><td>11.45</td><td>6.51</td><td>5.56</td><td>3.33</td><td>5.86</td><td>6.13</td><td>3.79</td><td>-</td></tr>
-<tr><td class="metric-name">自由现金流(亿)</td><td>4.08</td><td>7.87</td><td>14.35</td><td>-22.12</td><td>-1.24</td><td>0.64</td><td>-4.14</td><td>-5.01</td><td>-9.73</td><td>-</td></tr>
-<tr><td class="metric-name">分红(亿)</td><td>-</td><td>2.02</td><td>2.95</td><td>2.10</td><td>1.26</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">回购(亿)</td><td>-</td><td>1.71</td><td>0.50</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">总资产(亿)</td><td>174.13</td><td>147.47</td><td>134.81</td><td>153.80</td><td>105.16</td><td>62.22</td><td>53.48</td><td>55.49</td><td>37.74</td><td>1.00</td></tr>
-<tr><td class="metric-name">净资产(亿)</td><td>90.09</td><td>87.37</td><td>85.82</td><td>82.00</td><td>37.36</td><td>17.48</td><td>15.12</td><td>7.47</td><td>6.67</td><td>1.00</td></tr>
-<tr><td class="metric-name">商誉(亿)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
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-<tr><td class="metric-name">销售费用率</td><td>0.25%</td><td>0.31%</td><td>0.19%</td><td>0.09%</td><td>0.19%</td><td>0.33%</td><td>0.58%</td><td>0.37%</td><td>0.51%</td><td>-</td></tr>
-<tr><td class="metric-name">管理费用率</td><td>1.18%</td><td>1.42%</td><td>1.11%</td><td>0.54%</td><td>0.84%</td><td>1.13%</td><td>1.12%</td><td>0.92%</td><td>1.10%</td><td>-</td></tr>
-<tr><td class="metric-name">SG&A比例</td><td>1.43%</td><td>1.73%</td><td>1.30%</td><td>0.63%</td><td>1.03%</td><td>1.46%</td><td>1.70%</td><td>1.29%</td><td>1.61%</td><td>-</td></tr>
-<tr><td class="metric-name">研发费用率</td><td>2.99%</td><td>3.15%</td><td>2.68%</td><td>2.63%</td><td>2.90%</td><td>3.27%</td><td>3.52%</td><td>4.73%</td><td>4.10%</td><td>-</td></tr>
-<tr><td class="metric-name">其他费用率</td><td>1.35%</td><td>1.16%</td><td>0.96%</td><td>1.44%</td><td>2.05%</td><td>2.62%</td><td>0.69%</td><td>2.94%</td><td>3.05%</td><td>-</td></tr>
-<tr><td class="metric-name">折旧费用占比</td><td>-</td><td>2.77%</td><td>2.02%</td><td>1.03%</td><td>1.50%</td><td>2.39%</td><td>1.66%</td><td>0.85%</td><td>1.09%</td><td>-</td></tr>
-<tr><td class="metric-name">所得税率</td><td>9.29%</td><td>3.85%</td><td>5.21%</td><td>8.93%</td><td>7.30%</td><td>4.21%</td><td>-26.38%</td><td>2.76%</td><td>27.22%</td><td>-</td></tr>
-<tr class="section-row"><td class="section-label">资产占比</td><td class="section-spacer" colspan="10"></td></tr>
-<tr><td class="metric-name">现金占比</td><td>8.88%</td><td>11.09%</td><td>8.90%</td><td>6.61%</td><td>4.04%</td><td>4.47%</td><td>0.96%</td><td>2.96%</td><td>2.90%</td><td>-</td></tr>
-<tr><td class="metric-name">库存占比</td><td>26.24%</td><td>16.79%</td><td>14.57%</td><td>21.79%</td><td>26.58%</td><td>19.51%</td><td>16.08%</td><td>28.66%</td><td>32.68%</td><td>-</td></tr>
-<tr><td class="metric-name">应收款占比</td><td>18.81%</td><td>15.92%</td><td>25.95%</td><td>32.22%</td><td>28.09%</td><td>19.84%</td><td>12.33%</td><td>15.88%</td><td>13.23%</td><td>-</td></tr>
-<tr><td class="metric-name">预付款占比</td><td>0.75%</td><td>0.08%</td><td>0.07%</td><td>0.11%</td><td>0.16%</td><td>1.25%</td><td>0.39%</td><td>6.20%</td><td>3.26%</td><td>-</td></tr>
-<tr><td class="metric-name">固定资产占比</td><td>-</td><td>23.88%</td><td>25.96%</td><td>23.65%</td><td>26.59%</td><td>38.24%</td><td>37.34%</td><td>19.47%</td><td>15.24%</td><td>-</td></tr>
-<tr><td class="metric-name">长期投资占比</td><td>2.39%</td><td>2.83%</td><td>2.95%</td><td>0.65%</td><td>-</td><td>0.07%</td><td>0.07%</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">商誉占比</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr class="other-assets-row"><td class="metric-name">其他资产占比</td><td>42.94%</td><td>29.42%</td><td>21.60%</td><td>14.97%</td><td>14.54%</td><td>16.62%</td><td>32.82%</td><td>26.83%</td><td>32.70%</td><td>100.00%</td></tr>
-<tr><td class="metric-name">应付款占比</td><td>17.61%</td><td>14.64%</td><td>16.87%</td><td>13.63%</td><td>33.13%</td><td>19.56%</td><td>16.47%</td><td>12.18%</td><td>15.49%</td><td>-</td></tr>
-<tr><td class="metric-name">预收款占比</td><td>0.88%</td><td>0.02%</td><td>0.03%</td><td>0.04%</td><td>0.10%</td><td>0.03%</td><td>0.05%</td><td>0.14%</td><td>0.36%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">短期借款占比</td><td>1.06%</td><td>1.60%</td><td>0.25%</td><td>4.53%</td><td>2.48%</td><td>20.67%</td><td>33.82%</td><td>23.62%</td><td>10.33%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">长期借款占比</td><td>2.57%</td><td>3.16%</td><td>5.23%</td><td>5.66%</td><td>13.15%</td><td>18.25%</td><td>3.12%</td><td>1.87%</td><td>0.11%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">运营资产占比</td><td>27.30%</td><td>18.13%</td><td>23.69%</td><td>40.46%</td><td>21.59%</td><td>21.02%</td><td>12.29%</td><td>38.41%</td><td>33.31%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">有息负债率</td><td>3.63%</td><td>4.76%</td><td>5.47%</td><td>10.19%</td><td>15.64%</td><td>38.92%</td><td>36.94%</td><td>25.49%</td><td>10.44%</td><td>0.00%</td></tr>
-<tr class="section-row"><td class="section-label">周转能力</td><td class="section-spacer" colspan="10"></td></tr>
-<tr><td class="metric-name">存货周转天数</td><td>141</td><td>75</td><td>45</td><td>46</td><td>72</td><td>61</td><td>48</td><td>91</td><td>122</td><td>-</td></tr>
-<tr><td class="metric-name">应收款周转天数</td><td>91</td><td>64</td><td>73</td><td>62</td><td>69</td><td>56</td><td>34</td><td>45</td><td>43</td><td>-</td></tr>
-<tr><td class="metric-name">应付款周转天数</td><td>95</td><td>65</td><td>52</td><td>29</td><td>90</td><td>62</td><td>50</td><td>39</td><td>58</td><td>-</td></tr>
-<tr><td class="metric-name">固定资产周转率</td><td>-</td><td>3.78</td><td>4.95</td><td>7.91</td><td>5.57</td><td>3.36</td><td>3.49</td><td>6.50</td><td>7.32</td><td>-</td></tr>
-<tr><td class="metric-name">总资产周转率</td><td>0.75</td><td>0.90</td><td>1.28</td><td>1.87</td><td>1.48</td><td>1.28</td><td>1.30</td><td>1.27</td><td>1.12</td><td>0.00</td></tr>
-<tr class="section-row"><td class="section-label">人均效率</td><td class="section-spacer" colspan="10"></td></tr>
-<tr><td class="metric-name">员工人数</td><td>-</td><td>3,344</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均创收(万)</td><td>-</td><td>397.63</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均创利(万)</td><td>-</td><td>14.77</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均薪酬(万)</td><td>-</td><td>15.29</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr class="section-row"><td class="section-label">市场表现</td><td class="section-spacer" colspan="10"></td></tr>
-<tr><td class="metric-name">股价</td><td>77.37</td><td>45.64</td><td>39.64</td><td>77.68</td><td>103.35</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">市值(亿)</td><td>390.48</td><td>192.04</td><td>166.79</td><td>233.47</td><td>260.00</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">PE</td><td>79.03</td><td>36.41</td><td>14.88</td><td>42.04</td><td>103.77</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
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-<tr><td class="metric-name">股东户数</td><td>24,627</td><td>14,740</td><td>13,732</td><td>14,462</td><td>15,688</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
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data/CN/688778.SH/report.md

@@ -1,89 +0,0 @@
-# 厦钨新能 (688778.SH) - Financial Report
-*Report generated on: 2026-01-03*
-
-| 代码 | 简称 | 上市日期 | PE | PB | 股息率(%) |
-|:---|:---|:---|:---|:---|:---|
-| 688778.SH | 厦钨新能 | 2021-08-05 | 79.03 | 4.33 | 0.00% |
-
-
-## 主要指标
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| ROE | 6.13% | 5.65% | 6.15% | 13.67% | 14.87% | 14.33% | 9.93% | 10.73% | 24.90% | 0.00% |
-| ROA | 3.17% | 3.35% | 3.91% | 7.29% | 5.28% | 4.03% | 2.81% | 1.44% | 4.40% | 0.00% |
-| ROCE/ROIC | 6.71% | 5.83% | 6.83% | 13.99% | 13.65% | 9.48% | 6.43% | 9.16% | 29.07% | 0.00% |
-| 毛利率 | 10.00% | 9.76% | 7.99% | 8.60% | 9.55% | 10.48% | 8.06% | 10.09% | 12.70% | - |
-| 净利润率 | 4.23% | 3.72% | 3.05% | 3.90% | 3.57% | 3.14% | 2.15% | 1.14% | 3.94% | - |
-| 收入(亿) | 130.59 | 132.97 | 173.11 | 287.51 | 155.66 | 79.90 | 69.78 | 70.26 | 42.11 | 0.00 |
-| 收入增速 | 32.15% | -23.19% | -39.79% | 84.71% | 94.82% | 14.50% | -0.69% | 66.84% | - | - |
-| 净利润(亿) | 5.52 | 4.94 | 5.27 | 11.21 | 5.55 | 2.51 | 1.50 | 0.80 | 1.66 | 0.00 |
-| 净利润增速 | 50.26% | -6.33% | -52.93% | 101.77% | 121.66% | 66.94% | 87.35% | -51.76% | - | - |
-| 经营净现金流(亿) | 8.97 | 17.53 | 25.80 | -15.61 | 4.32 | 3.97 | 1.72 | 1.11 | -5.94 | - |
-| 资本开支(亿) | 4.89 | 9.66 | 11.45 | 6.51 | 5.56 | 3.33 | 5.86 | 6.13 | 3.79 | - |
-| 自由现金流(亿) | 4.08 | 7.87 | 14.35 | -22.12 | -1.24 | 0.64 | -4.14 | -5.01 | -9.73 | - |
-| 分红(亿) | - | 2.02 | 2.95 | 2.10 | 1.26 | - | - | - | - | - |
-| 回购(亿) | - | 1.71 | 0.50 | - | - | - | - | - | - | - |
-| 总资产(亿) | 174.13 | 147.47 | 134.81 | 153.80 | 105.16 | 62.22 | 53.48 | 55.49 | 37.74 | 1.00 |
-| 净资产(亿) | 90.09 | 87.37 | 85.82 | 82.00 | 37.36 | 17.48 | 15.12 | 7.47 | 6.67 | 1.00 |
-| 商誉(亿) | - | - | - | - | - | - | - | - | - | - |
-
-
-## 费用指标
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 销售费用率 | 0.25% | 0.31% | 0.19% | 0.09% | 0.19% | 0.33% | 0.58% | 0.37% | 0.51% | - |
-| 管理费用率 | 1.18% | 1.42% | 1.11% | 0.54% | 0.84% | 1.13% | 1.12% | 0.92% | 1.10% | - |
-| SG&A比例 | 1.43% | 1.73% | 1.30% | 0.63% | 1.03% | 1.46% | 1.70% | 1.29% | 1.61% | - |
-| 研发费用率 | 2.99% | 3.15% | 2.68% | 2.63% | 2.90% | 3.27% | 3.52% | 4.73% | 4.10% | - |
-| 其他费用率 | 1.35% | 1.16% | 0.96% | 1.44% | 2.05% | 2.62% | 0.69% | 2.94% | 3.05% | - |
-| 折旧费用占比 | - | 2.77% | 2.02% | 1.03% | 1.50% | 2.39% | 1.66% | 0.85% | 1.09% | - |
-| 所得税率 | 9.29% | 3.85% | 5.21% | 8.93% | 7.30% | 4.21% | -26.38% | 2.76% | 27.22% | - |
-
-
-## 资产占比
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 现金占比 | 8.88% | 11.09% | 8.90% | 6.61% | 4.04% | 4.47% | 0.96% | 2.96% | 2.90% | - |
-| 库存占比 | 26.24% | 16.79% | 14.57% | 21.79% | 26.58% | 19.51% | 16.08% | 28.66% | 32.68% | - |
-| 应收款占比 | 18.81% | 15.92% | 25.95% | 32.22% | 28.09% | 19.84% | 12.33% | 15.88% | 13.23% | - |
-| 预付款占比 | 0.75% | 0.08% | 0.07% | 0.11% | 0.16% | 1.25% | 0.39% | 6.20% | 3.26% | - |
-| 固定资产占比 | - | 23.88% | 25.96% | 23.65% | 26.59% | 38.24% | 37.34% | 19.47% | 15.24% | - |
-| 长期投资占比 | 2.39% | 2.83% | 2.95% | 0.65% | - | 0.07% | 0.07% | - | - | - |
-| 商誉占比 | - | - | - | - | - | - | - | - | - | - |
-| 其他资产占比 | 42.94% | 29.42% | 21.60% | 14.97% | 14.54% | 16.62% | 32.82% | 26.83% | 32.70% | 100.00% |
-| 应付款占比 | 17.61% | 14.64% | 16.87% | 13.63% | 33.13% | 19.56% | 16.47% | 12.18% | 15.49% | - |
-| 预收款占比 | 0.88% | 0.02% | 0.03% | 0.04% | 0.10% | 0.03% | 0.05% | 0.14% | 0.36% | 0.00% |
-| 短期借款占比 | 1.06% | 1.60% | 0.25% | 4.53% | 2.48% | 20.67% | 33.82% | 23.62% | 10.33% | 0.00% |
-| 长期借款占比 | 2.57% | 3.16% | 5.23% | 5.66% | 13.15% | 18.25% | 3.12% | 1.87% | 0.11% | 0.00% |
-| 运营资产占比 | 27.30% | 18.13% | 23.69% | 40.46% | 21.59% | 21.02% | 12.29% | 38.41% | 33.31% | 0.00% |
-| 有息负债率 | 3.63% | 4.76% | 5.47% | 10.19% | 15.64% | 38.92% | 36.94% | 25.49% | 10.44% | 0.00% |
-
-
-## 周转能力
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 存货周转天数 | 141 | 75 | 45 | 46 | 72 | 61 | 48 | 91 | 122 | - |
-| 应收款周转天数 | 91 | 64 | 73 | 62 | 69 | 56 | 34 | 45 | 43 | - |
-| 应付款周转天数 | 95 | 65 | 52 | 29 | 90 | 62 | 50 | 39 | 58 | - |
-| 固定资产周转率 | - | 3.78 | 4.95 | 7.91 | 5.57 | 3.36 | 3.49 | 6.50 | 7.32 | - |
-| 总资产周转率 | 0.75 | 0.90 | 1.28 | 1.87 | 1.48 | 1.28 | 1.30 | 1.27 | 1.12 | 0.00 |
-
-
-## 人均效率
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 员工人数 | - | 3,344 | - | - | - | - | - | - | - | - |
-| 人均创收(万) | - | 397.63 | - | - | - | - | - | - | - | - |
-| 人均创利(万) | - | 14.77 | - | - | - | - | - | - | - | - |
-| 人均薪酬(万) | - | 15.29 | - | - | - | - | - | - | - | - |
-
-
-## 市场表现
-| 指标 | 2025Q3 | 2024A | 2023A | 2022A | 2021A | 2020A | 2019A | 2018A | 2017A | 2016A |
-|:---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
-| 股价 | 77.37 | 45.64 | 39.64 | 77.68 | 103.35 | - | - | - | - | - |
-| 市值(亿) | 390.48 | 192.04 | 166.79 | 233.47 | 260.00 | - | - | - | - | - |
-| PE | 79.03 | 36.41 | 14.88 | 42.04 | 103.77 | - | - | - | - | - |
-| PB | 4.33 | 2.23 | 1.97 | 2.93 | 7.27 | - | - | - | - | - |
-| 股东户数 | 24,627 | 14,740 | 13,732 | 14,462 | 15,688 | - | - | - | - | - |
-

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data/JP/2503.T/raw_basic_info_raw.csv

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data/JP/2503.T/raw_cash_flow_raw.csv

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data/JP/2503.T/raw_employee_count_raw.csv

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data/JP/2503.T/raw_historical_metrics_raw.csv

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data/JP/2503.T/raw_income_statement_raw.csv

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-                    <h1>麒麟控股株式会社 (2503.T) - Financial Report</h1>
-<p><em>Report generated on: 2026-01-07</em></p>
-
-        <table class="company-table">
-            <thead>
-                <tr>
-                    <th>代码</th>
-                    <th>简称</th>
-                    <th>上市日期</th>
-                    <th>PE</th>
-                    <th>PB</th>
-                    <th>股息率(%)</th>
-                </tr>
-            </thead>
-            <tbody>
-                <tr>
-                    <td>2503.T</td>
-                    <td>麒麟控股株式会社</td>
-                    <td>1949-05-16</td>
-                    <td>0.00</td>
-                    <td>0.00</td>
-                    <td>0.00%</td>
-                </tr>
-            </tbody>
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-        
-<div class="table-gap"></div>
-
-            <table class="metrics-table" data-table="metrics" data-scrollable="true">
-                <thead>
-                    <tr>
-                        <th>指标</th>
-                        <th>2024A</th><th>2023A</th><th>2022A</th><th>2021A</th><th>2020A</th>
-                    </tr>
-                </thead>
-                <tbody>
-                    <tr class="section-row"><td class="section-label">主要指标</td><td class="section-spacer" colspan="5"></td></tr>
-<tr><td class="metric-name">ROE</td><td>4.93%</td><td>9.95%</td><td>11.33%</td><td>6.69%</td><td>8.58%</td></tr>
-<tr><td class="metric-name">ROA</td><td>1.74%</td><td>3.93%</td><td>4.37%</td><td>2.42%</td><td>2.92%</td></tr>
-<tr><td class="metric-name">ROCE/ROIC</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">毛利率</td><td>45.59%</td><td>45.14%</td><td>45.53%</td><td>45.17%</td><td>43.46%</td></tr>
-<tr><td class="metric-name">净利润率</td><td>2.49%</td><td>5.28%</td><td>5.58%</td><td>3.28%</td><td>3.89%</td></tr>
-<tr><td class="metric-name">收入(亿)</td><td>1,084.61</td><td>1,075.36</td><td>1,041.03</td><td>1,007.91</td><td>1,169.52</td></tr>
-<tr><td class="metric-name">收入增速</td><td>0.86%</td><td>3.30%</td><td>3.29%</td><td>-13.82%</td><td>-</td></tr>
-<tr><td class="metric-name">净利润(亿)</td><td>27.00</td><td>56.78</td><td>58.09</td><td>33.08</td><td>45.49</td></tr>
-<tr><td class="metric-name">净利润增速</td><td>-52.45%</td><td>-2.25%</td><td>75.58%</td><td>-27.27%</td><td>-</td></tr>
-<tr><td class="metric-name">经营净现金流(亿)</td><td>112.64</td><td>102.38</td><td>70.94</td><td>121.34</td><td>104.23</td></tr>
-<tr><td class="metric-name">资本开支(亿)</td><td>83.76</td><td>57.34</td><td>51.53</td><td>47.77</td><td>58.82</td></tr>
-<tr><td class="metric-name">自由现金流(亿)</td><td>28.88</td><td>45.04</td><td>19.40</td><td>73.57</td><td>45.41</td></tr>
-<tr><td class="metric-name">分红(亿)</td><td>27.05</td><td>28.97</td><td>28.14</td><td>29.98</td><td>34.98</td></tr>
-<tr><td class="metric-name">回购(亿)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">总资产(亿)</td><td>1,555.76</td><td>1,445.77</td><td>1,330.29</td><td>1,367.77</td><td>1,555.13</td></tr>
-<tr><td class="metric-name">净资产(亿)</td><td>548.03</td><td>570.62</td><td>512.81</td><td>494.77</td><td>530.26</td></tr>
-<tr><td class="metric-name">商誉(亿)</td><td>538.53</td><td>349.71</td><td>256.62</td><td>254.84</td><td>287.71</td></tr>
-<tr class="section-row"><td class="section-label">费用指标</td><td class="section-spacer" colspan="5"></td></tr>
-<tr><td class="metric-name">销售费用率</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">管理费用率</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">SG&A比例</td><td>31.60%</td><td>31.72%</td><td>32.18%</td><td>32.27%</td><td>31.21%</td></tr>
-<tr><td class="metric-name">研发费用率</td><td>4.96%</td><td>3.98%</td><td>3.73%</td><td>3.82%</td><td>3.49%</td></tr>
-<tr><td class="metric-name">其他费用率</td><td>6.53%</td><td>4.16%</td><td>4.03%</td><td>5.80%</td><td>4.88%</td></tr>
-<tr><td class="metric-name">折旧费用占比</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">所得税率</td><td>48.08%</td><td>29.26%</td><td>30.02%</td><td>34.28%</td><td>25.57%</td></tr>
-<tr class="section-row"><td class="section-label">资产占比</td><td class="section-spacer" colspan="5"></td></tr>
-<tr><td class="metric-name">现金占比</td><td>3.79%</td><td>4.89%</td><td>3.79%</td><td>6.37%</td><td>6.90%</td></tr>
-<tr><td class="metric-name">库存占比</td><td>10.70%</td><td>11.53%</td><td>11.41%</td><td>10.00%</td><td>8.83%</td></tr>
-<tr><td class="metric-name">应收款占比</td><td>14.35%</td><td>14.77%</td><td>15.31%</td><td>15.07%</td><td>14.53%</td></tr>
-<tr><td class="metric-name">预付款占比</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">固定资产占比</td><td>20.10%</td><td>20.66%</td><td>22.05%</td><td>21.60%</td><td>21.22%</td></tr>
-<tr><td class="metric-name">长期投资占比</td><td>3.08%</td><td>3.67%</td><td>4.07%</td><td>4.75%</td><td>4.30%</td></tr>
-<tr><td class="metric-name">商誉占比</td><td>34.61%</td><td>24.19%</td><td>19.29%</td><td>18.63%</td><td>18.50%</td></tr>
-<tr class="other-assets-row"><td class="metric-name">其他资产占比</td><td>13.36%</td><td>20.29%</td><td>24.07%</td><td>23.58%</td><td>25.72%</td></tr>
-<tr><td class="metric-name">应付款占比</td><td>4.83%</td><td>4.78%</td><td>4.74%</td><td>3.99%</td><td>3.91%</td></tr>
-<tr><td class="metric-name">预收款占比</td><td>0.00%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td></tr>
-<tr><td class="metric-name">短期借款占比</td><td>2.33%</td><td>3.51%</td><td>4.50%</td><td>4.11%</td><td>10.13%</td></tr>
-<tr><td class="metric-name">长期借款占比</td><td>46.48%</td><td>38.73%</td><td>32.15%</td><td>36.41%</td><td>32.01%</td></tr>
-<tr><td class="metric-name">运营资产占比</td><td>20.22%</td><td>21.52%</td><td>21.99%</td><td>21.08%</td><td>19.45%</td></tr>
-<tr><td class="metric-name">有息负债率</td><td>48.81%</td><td>42.24%</td><td>36.65%</td><td>40.51%</td><td>42.14%</td></tr>
-<tr class="section-row"><td class="section-label">周转能力</td><td class="section-spacer" colspan="5"></td></tr>
-<tr><td class="metric-name">存货周转天数</td><td>102</td><td>103</td><td>97</td><td>90</td><td>75</td></tr>
-<tr><td class="metric-name">应收款周转天数</td><td>75</td><td>72</td><td>71</td><td>74</td><td>70</td></tr>
-<tr><td class="metric-name">应付款周转天数</td><td>46</td><td>42</td><td>40</td><td>36</td><td>33</td></tr>
-<tr><td class="metric-name">固定资产周转率</td><td>3.47</td><td>3.60</td><td>3.55</td><td>3.41</td><td>3.54</td></tr>
-<tr><td class="metric-name">总资产周转率</td><td>0.70</td><td>0.74</td><td>0.78</td><td>0.74</td><td>0.75</td></tr>
-<tr class="section-row"><td class="section-label">人均效率</td><td class="section-spacer" colspan="5"></td></tr>
-<tr><td class="metric-name">员工人数</td><td>31,934</td><td>30,538</td><td>29,515</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均创收(万)</td><td>339.64</td><td>352.14</td><td>352.71</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均创利(万)</td><td>8.46</td><td>18.59</td><td>19.68</td><td>-</td><td>-</td></tr>
-<tr><td class="metric-name">人均薪酬(万)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-<tr class="section-row"><td class="section-label">市场表现</td><td class="section-spacer" colspan="5"></td></tr>
-<tr><td class="metric-name">股价</td><td>95.04</td><td>104.09</td><td>105.23</td><td>102.20</td><td>0.00</td></tr>
-<tr><td class="metric-name">市值(亿)</td><td>868.66</td><td>951.38</td><td>961.80</td><td>934.09</td><td>0.00</td></tr>
-<tr><td class="metric-name">PE</td><td>32.17</td><td>16.76</td><td>16.56</td><td>28.23</td><td>0.00</td></tr>
-<tr><td class="metric-name">PB</td><td>1.59</td><td>1.67</td><td>1.88</td><td>1.89</td><td>0.00</td></tr>
-<tr><td class="metric-name">股东户数</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr>
-                </tbody>
-            </table>
-            
-                </div>
-                <script>
-                document.addEventListener('DOMContentLoaded', function() {
-                    const scrollableTables = document.querySelectorAll('table[data-scrollable="true"]');
-                    scrollableTables.forEach(table => {
-                        const container = document.createElement('div');
-                        container.className = 'table-container';
-                        table.parentNode.insertBefore(container, table);
-                        container.appendChild(table);
-                    });
-
-                    const parseValue = (text) => {
-                        if (!text || text.trim() === '-') return null;
-                        return parseFloat(text.replace(/%|,/g, ''));
-                    };
-
-                    const highlightIfOverThirtyPercent = (cell) => {
-                        const value = parseValue(cell.textContent);
-                        if (value !== null && value > 30) {
-                            cell.classList.add('bg-red', 'font-red');
-                        }
-                    };
-
-                    const styleRules = {
-                        'ROE': (cell) => {
-                            const value = parseValue(cell.textContent);
-                            if (value !== null && value > 15) cell.classList.add('bg-green');
-                        },
-                        'ROA': (cell) => {
-                            const value = parseValue(cell.textContent);
-                            if (value !== null && value > 10) cell.classList.add('bg-green');
-                        },
-                        '毛利率': (cell) => {
-                            const value = parseValue(cell.textContent);
-                            if (value !== null && value > 50) cell.classList.add('bg-green');
-                        },
-                        '净利润率': (cell) => {
-                            const value = parseValue(cell.textContent);
-                            if (value !== null) {
-                                if (value > 20) {
-                                    cell.classList.add('bg-green');
-                                } else if (value < 0) {
-                                    cell.classList.add('bg-red', 'font-red');
-                                }
-                            }
-                        },
-                        '收入增速': (cell) => {
-                            cell.classList.add('italic');
-                            const value = parseValue(cell.textContent);
-                            if (value !== null) {
-                                if (value > 15) {
-                                    cell.classList.add('bg-green', 'font-green');
-                                } else if (value < 0) {
-                                    cell.classList.add('bg-red', 'font-red');
-                                } else {
-                                    cell.classList.add('font-blue');
-                                }
-                            }
-                        },
-                        '净利润增速': (cell) => {
-                            cell.classList.add('italic');
-                            const value = parseValue(cell.textContent);
-                            if (value !== null) {
-                                if (value > 15) {
-                                    cell.classList.add('bg-green', 'font-green');
-                                } else if (value < 0) {
-                                    cell.classList.add('bg-red', 'font-red');
-                                } else {
-                                    cell.classList.add('font-blue');
-                                }
-                            }
-                        },
-                        '经营净现金流(亿)': (cell) => {
-                            const value = parseValue(cell.textContent);
-                            if (value !== null && value < 0) cell.classList.add('bg-red', 'font-red');
-                        },
-                        '应收款周转天数': (cell) => {
-                            const value = parseValue(cell.textContent);
-                            if (value !== null && value > 90) {
-                                cell.classList.add('bg-red', 'font-red');
-                            }
-                        },
-                        '现金占比': highlightIfOverThirtyPercent,
-                        '库存占比': highlightIfOverThirtyPercent,
-                        '应收款占比': highlightIfOverThirtyPercent,
-                        '预付款占比': highlightIfOverThirtyPercent,
-                        '固定资产占比': highlightIfOverThirtyPercent,
-                        '长期投资占比': highlightIfOverThirtyPercent,
-                        '商誉占比': highlightIfOverThirtyPercent,
-                        '其他资产占比': highlightIfOverThirtyPercent
-                    };
-
-                    const metricsTables = document.querySelectorAll('table[data-table="metrics"]');
-                    metricsTables.forEach(table => {
-                        let netProfitValues = [];
-                        let fcfRow = null;
-                        const rows = table.querySelectorAll('tbody tr');
-                        rows.forEach(row => {
-                            if (row.classList.contains('section-row')) return;
-                            const metricCell = row.querySelector('td:first-child');
-                            if (!metricCell) return;
-                            const metricName = metricCell.textContent.trim();
-                            if (metricName === '净利润(亿)') {
-                                row.querySelectorAll('td:not(:first-child)').forEach(cell => {
-                                    netProfitValues.push(parseValue(cell.textContent));
-                                });
-                            } else if (metricName === '自由现金流(亿)') {
-                                fcfRow = row;
-                            }
-                        });
-
-                        rows.forEach(row => {
-                            if (row.classList.contains('section-row')) return;
-                            const metricCell = row.querySelector('td:first-child');
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-                            if (styleRules[metricName]) {
-                                cells.forEach(cell => {
-                                    styleRules[metricName](cell);
-                                });
-                            }
-                            if (row === fcfRow && netProfitValues.length > 0) {
-                                cells.forEach((cell, index) => {
-                                    const fcfValue = parseValue(cell.textContent);
-                                    const netProfitValue = netProfitValues[index];
-                                    if (fcfValue !== null) {
-                                        if (fcfValue < 0) {
-                                            cell.classList.add('bg-red', 'font-red');
-                                        } else if (netProfitValue !== null && fcfValue > netProfitValue) {
-                                            cell.classList.add('bg-green', 'font-green');
-                                        }
-                                    }
-                                });
-                            }
-                        });
-                    });
-                });
-                </script>
-            </body>
-        </html>
-        

data/JP/2503.T/report.md

@@ -1,89 +0,0 @@
-# 麒麟控股株式会社 (2503.T) - Financial Report
-*Report generated on: 2026-01-07*
-
-| 代码 | 简称 | 上市日期 | PE | PB | 股息率(%) |
-|:---|:---|:---|:---|:---|:---|
-| 2503.T | 麒麟控股株式会社 | 1949-05-16 | 0.00 | 0.00 | 0.00% |
-
-
-## 主要指标
-| 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
-|:---|--:|--:|--:|--:|--:|
-| ROE | 4.93% | 9.95% | 11.33% | 6.69% | 8.58% |
-| ROA | 1.74% | 3.93% | 4.37% | 2.42% | 2.92% |
-| ROCE/ROIC | - | - | - | - | - |
-| 毛利率 | 45.59% | 45.14% | 45.53% | 45.17% | 43.46% |
-| 净利润率 | 2.49% | 5.28% | 5.58% | 3.28% | 3.89% |
-| 收入(亿) | 1,084.61 | 1,075.36 | 1,041.03 | 1,007.91 | 1,169.52 |
-| 收入增速 | 0.86% | 3.30% | 3.29% | -13.82% | - |
-| 净利润(亿) | 27.00 | 56.78 | 58.09 | 33.08 | 45.49 |
-| 净利润增速 | -52.45% | -2.25% | 75.58% | -27.27% | - |
-| 经营净现金流(亿) | 112.64 | 102.38 | 70.94 | 121.34 | 104.23 |
-| 资本开支(亿) | 83.76 | 57.34 | 51.53 | 47.77 | 58.82 |
-| 自由现金流(亿) | 28.88 | 45.04 | 19.40 | 73.57 | 45.41 |
-| 分红(亿) | 27.05 | 28.97 | 28.14 | 29.98 | 34.98 |
-| 回购(亿) | - | - | - | - | - |
-| 总资产(亿) | 1,555.76 | 1,445.77 | 1,330.29 | 1,367.77 | 1,555.13 |
-| 净资产(亿) | 548.03 | 570.62 | 512.81 | 494.77 | 530.26 |
-| 商誉(亿) | 538.53 | 349.71 | 256.62 | 254.84 | 287.71 |
-
-
-## 费用指标
-| 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
-|:---|--:|--:|--:|--:|--:|
-| 销售费用率 | - | - | - | - | - |
-| 管理费用率 | - | - | - | - | - |
-| SG&A比例 | 31.60% | 31.72% | 32.18% | 32.27% | 31.21% |
-| 研发费用率 | 4.96% | 3.98% | 3.73% | 3.82% | 3.49% |
-| 其他费用率 | 6.53% | 4.16% | 4.03% | 5.80% | 4.88% |
-| 折旧费用占比 | - | - | - | - | - |
-| 所得税率 | 48.08% | 29.26% | 30.02% | 34.28% | 25.57% |
-
-
-## 资产占比
-| 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
-|:---|--:|--:|--:|--:|--:|
-| 现金占比 | 3.79% | 4.89% | 3.79% | 6.37% | 6.90% |
-| 库存占比 | 10.70% | 11.53% | 11.41% | 10.00% | 8.83% |
-| 应收款占比 | 14.35% | 14.77% | 15.31% | 15.07% | 14.53% |
-| 预付款占比 | - | - | - | - | - |
-| 固定资产占比 | 20.10% | 20.66% | 22.05% | 21.60% | 21.22% |
-| 长期投资占比 | 3.08% | 3.67% | 4.07% | 4.75% | 4.30% |
-| 商誉占比 | 34.61% | 24.19% | 19.29% | 18.63% | 18.50% |
-| 其他资产占比 | 13.36% | 20.29% | 24.07% | 23.58% | 25.72% |
-| 应付款占比 | 4.83% | 4.78% | 4.74% | 3.99% | 3.91% |
-| 预收款占比 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
-| 短期借款占比 | 2.33% | 3.51% | 4.50% | 4.11% | 10.13% |
-| 长期借款占比 | 46.48% | 38.73% | 32.15% | 36.41% | 32.01% |
-| 运营资产占比 | 20.22% | 21.52% | 21.99% | 21.08% | 19.45% |
-| 有息负债率 | 48.81% | 42.24% | 36.65% | 40.51% | 42.14% |
-
-
-## 周转能力
-| 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
-|:---|--:|--:|--:|--:|--:|
-| 存货周转天数 | 102 | 103 | 97 | 90 | 75 |
-| 应收款周转天数 | 75 | 72 | 71 | 74 | 70 |
-| 应付款周转天数 | 46 | 42 | 40 | 36 | 33 |
-| 固定资产周转率 | 3.47 | 3.60 | 3.55 | 3.41 | 3.54 |
-| 总资产周转率 | 0.70 | 0.74 | 0.78 | 0.74 | 0.75 |
-
-
-## 人均效率
-| 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
-|:---|--:|--:|--:|--:|--:|
-| 员工人数 | 31,934 | 30,538 | 29,515 | - | - |
-| 人均创收(万) | 339.64 | 352.14 | 352.71 | - | - |
-| 人均创利(万) | 8.46 | 18.59 | 19.68 | - | - |
-| 人均薪酬(万) | - | - | - | - | - |
-
-
-## 市场表现
-| 指标 | 2024A | 2023A | 2022A | 2021A | 2020A |
-|:---|--:|--:|--:|--:|--:|
-| 股价 | 95.04 | 104.09 | 105.23 | 102.20 | 0.00 |
-| 市值(亿) | 868.66 | 951.38 | 961.80 | 934.09 | 0.00 |
-| PE | 32.17 | 16.76 | 16.56 | 28.23 | 0.00 |
-| PB | 1.59 | 1.67 | 1.88 | 1.89 | 0.00 |
-| 股东户数 | - | - | - | - | - |
-

ecosystem.config.js.bak

@@ -1,22 +0,0 @@
-module.exports = {
-  apps: [
-    {
-      name: "datafetch-backend",
-      script: ".venv/bin/uvicorn",
-      args: "backend.app.main:app --host 0.0.0.0 --port 8000",
-      cwd: ".",
-      env: {
-        PYTHONPATH: "."
-      }
-    },
-    {
-      name: "datafetch-frontend",
-      script: "npm",
-      args: "run dev",
-      cwd: "./frontend",
-      env: {
-        NODE_ENV: "development"
-      }
-    }
-  ]
-};

frontend/package-lock.json

@@ -11,6 +11,7 @@
         "@hookform/resolvers": "^5.2.2",
         "@radix-ui/react-dialog": "^1.1.15",
         "@radix-ui/react-label": "^2.1.8",
+        "@radix-ui/react-progress": "^1.1.8",
         "@radix-ui/react-select": "^2.2.6",
         "@radix-ui/react-slot": "^1.2.4",
         "@radix-ui/react-tabs": "^1.1.13",
@@ -84,6 +85,7 @@
       "integrity": "sha512-e7jT4DxYvIDLk1ZHmU/m/mB19rex9sv0c2ftBtjSBv+kVM/902eh0fINUzD7UwLLNR+jU585GxUJ8/EBfAM5fw==",
       "dev": true,
       "license": "MIT",
+      "peer": true,
       "dependencies": {
         "@babel/code-frame": "^7.27.1",
         "@babel/generator": "^7.28.5",
@@ -1723,6 +1725,68 @@
         }
       }
     },
+    "node_modules/@radix-ui/react-progress": {
+      "version": "1.1.8",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-progress/-/react-progress-1.1.8.tgz",
+      "integrity": "sha512-+gISHcSPUJ7ktBy9RnTqbdKW78bcGke3t6taawyZ71pio1JewwGSJizycs7rLhGTvMJYCQB1DBK4KQsxs7U8dA==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/react-context": "1.1.3",
+        "@radix-ui/react-primitive": "2.1.4"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+        "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/@radix-ui/react-progress/node_modules/@radix-ui/react-context": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-context/-/react-context-1.1.3.tgz",
+      "integrity": "sha512-ieIFACdMpYfMEjF0rEf5KLvfVyIkOz6PDGyNnP+u+4xQ6jny3VCgA4OgXOwNx2aUkxn8zx9fiVcM8CfFYv9Lxw==",
+      "license": "MIT",
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/@radix-ui/react-progress/node_modules/@radix-ui/react-primitive": {
+      "version": "2.1.4",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-primitive/-/react-primitive-2.1.4.tgz",
+      "integrity": "sha512-9hQc4+GNVtJAIEPEqlYqW5RiYdrr8ea5XQ0ZOnD6fgru+83kqT15mq2OCcbe8KnjRZl5vF3ks69AKz3kh1jrhg==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/react-slot": "1.2.4"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+        "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/@radix-ui/react-roving-focus": {
       "version": "1.1.11",
       "resolved": "https://registry.npmjs.org/@radix-ui/react-roving-focus/-/react-roving-focus-1.1.11.tgz",
@@ -2422,6 +2486,7 @@
       "resolved": "https://registry.npmjs.org/@types/react/-/react-19.2.7.tgz",
       "integrity": "sha512-MWtvHrGZLFttgeEj28VXHxpmwYbor/ATPYbBfSFZEIRK0ecCFLl2Qo55z52Hss+UV9CRN7trSeq1zbgx7YDWWg==",
       "license": "MIT",
+      "peer": true,
       "dependencies": {
         "csstype": "^3.2.2"
       }
@@ -2432,6 +2497,7 @@
       "integrity": "sha512-jp2L/eY6fn+KgVVQAOqYItbF0VY/YApe5Mz2F0aykSO8gx31bYCZyvSeYxCHKvzHG5eZjc+zyaS5BrBWya2+kQ==",
       "devOptional": true,
       "license": "MIT",
+      "peer": true,
       "peerDependencies": {
         "@types/react": "^19.2.0"
       }
@@ -2487,6 +2553,7 @@
       "integrity": "sha512-3xP4XzzDNQOIqBMWogftkwxhg5oMKApqY0BAflmLZiFYHqyhSOxv/cd/zPQLTcCXr4AkaKb25joocY0BD1WC6A==",
       "dev": true,
       "license": "MIT",
+      "peer": true,
       "dependencies": {
         "@typescript-eslint/scope-manager": "8.51.0",
         "@typescript-eslint/types": "8.51.0",
@@ -2992,6 +3059,7 @@
       "integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==",
       "dev": true,
       "license": "MIT",
+      "peer": true,
       "bin": {
         "acorn": "bin/acorn"
       },
@@ -3354,6 +3422,7 @@
         }
       ],
       "license": "MIT",
+      "peer": true,
       "dependencies": {
         "baseline-browser-mapping": "^2.9.0",
         "caniuse-lite": "^1.0.30001759",
@@ -4054,6 +4123,7 @@
       "integrity": "sha512-LEyamqS7W5HB3ujJyvi0HQK/dtVINZvd5mAAp9eT5S/ujByGjiZLCzPcHVzuXbpJDJF/cxwHlfceVUDZ2lnSTw==",
       "dev": true,
       "license": "MIT",
+      "peer": true,
       "dependencies": {
         "@eslint-community/eslint-utils": "^4.8.0",
         "@eslint-community/regexpp": "^4.12.1",
@@ -4239,6 +4309,7 @@
       "integrity": "sha512-whOE1HFo/qJDyX4SnXzP4N6zOWn79WhnCUY/iDR0mPfQZO8wcYE4JClzI2oZrhBnnMUCBCHZhO6VQyoBU95mZA==",
       "dev": true,
       "license": "MIT",
+      "peer": true,
       "dependencies": {
         "@rtsao/scc": "^1.1.0",
         "array-includes": "^3.1.9",
@@ -7470,6 +7541,7 @@
       "resolved": "https://registry.npmjs.org/react/-/react-19.2.3.tgz",
       "integrity": "sha512-Ku/hhYbVjOQnXDZFv2+RibmLFGwFdeeKHFcOTlrt7xplBnya5OGn/hIRDsqDiSUcfORsDC7MPxwork8jBwsIWA==",
       "license": "MIT",
+      "peer": true,
       "engines": {
         "node": ">=0.10.0"
       }
@@ -7479,6 +7551,7 @@
       "resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.2.3.tgz",
       "integrity": "sha512-yELu4WmLPw5Mr/lmeEpox5rw3RETacE++JgHqQzd2dg+YbJuat3jH4ingc+WPZhxaoFzdv9y33G+F7Nl5O0GBg==",
       "license": "MIT",
+      "peer": true,
       "dependencies": {
         "scheduler": "^0.27.0"
       },
@@ -7491,6 +7564,7 @@
       "resolved": "https://registry.npmjs.org/react-hook-form/-/react-hook-form-7.69.0.tgz",
       "integrity": "sha512-yt6ZGME9f4F6WHwevrvpAjh42HMvocuSnSIHUGycBqXIJdhqGSPQzTpGF+1NLREk/58IdPxEMfPcFCjlMhclGw==",
       "license": "MIT",
+      "peer": true,
       "engines": {
         "node": ">=18.0.0"
       },
@@ -8339,7 +8413,8 @@
       "version": "4.1.18",
       "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-4.1.18.tgz",
       "integrity": "sha512-4+Z+0yiYyEtUVCScyfHCxOYP06L5Ne+JiHhY2IjR2KWMIWhJOYZKLSGZaP5HkZ8+bY0cxfzwDE5uOmzFXyIwxw==",
-      "license": "MIT"
+      "license": "MIT",
+      "peer": true
     },
     "node_modules/tailwindcss-animate": {
       "version": "1.0.7",
@@ -8405,6 +8480,7 @@
       "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==",
       "dev": true,
       "license": "MIT",
+      "peer": true,
       "engines": {
         "node": ">=12"
       },
@@ -8597,6 +8673,7 @@
       "integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==",
       "dev": true,
       "license": "Apache-2.0",
+      "peer": true,
       "bin": {
         "tsc": "bin/tsc",
         "tsserver": "bin/tsserver"
@@ -9036,6 +9113,7 @@
       "resolved": "https://registry.npmjs.org/zod/-/zod-4.3.4.tgz",
       "integrity": "sha512-Zw/uYiiyF6pUT1qmKbZziChgNPRu+ZRneAsMUDU6IwmXdWt5JwcUfy2bvLOCUtz5UniaN/Zx5aFttZYbYc7O/A==",
       "license": "MIT",
+      "peer": true,
       "funding": {
         "url": "https://github.com/sponsors/colinhacks"
       }

frontend/package.json

@@ -12,6 +12,7 @@
     "@hookform/resolvers": "^5.2.2",
     "@radix-ui/react-dialog": "^1.1.15",
     "@radix-ui/react-label": "^2.1.8",
+    "@radix-ui/react-progress": "^1.1.8",
     "@radix-ui/react-select": "^2.2.6",
     "@radix-ui/react-slot": "^1.2.4",
     "@radix-ui/react-tabs": "^1.1.13",

frontend/src/app/config/page.tsx

@@ -2,7 +2,8 @@
 
 import { useState, useEffect } from "react"
 import { getConfig, updateConfig } from "@/lib/api"
-import { Card, CardContent, CardHeader, CardTitle, CardDescription, CardFooter } from "@/components/ui/card"
+import { AI_MODELS } from "@/lib/constants"
+import { Card, CardContent, CardHeader, CardTitle, CardDescription } from "@/components/ui/card"
 import { Input } from "@/components/ui/input"
 import { Button } from "@/components/ui/button"
 import { Label } from "@/components/ui/label"
@@ -71,10 +72,11 @@ export default function ConfigPage() {
                                 value={config["AI_MODEL"] || "gemini-2.0-flash"}
                                 onChange={(e) => setConfig({ ...config, "AI_MODEL": e.target.value })}
                             >
-                                <option value="gemini-2.0-flash">Gemini 2.0 Flash</option>
+                                {AI_MODELS.map((model) => (
-                                <option value="gemini-2.5-flash">Gemini 2.5 Flash</option>
+                                    <option key={model.value} value={model.value}>
-                                <option value="gemini-3-flash-preview">Gemini 3 Flash Preview</option>
+                                        {model.label}
-                                <option value="gemini-3-pro-preview">Gemini 3 Pro Preview</option>
+                                    </option>
+                                ))}
                                 <option value="custom">自定义模型...</option>
                             </select>
                             <Button onClick={() => handleSave("AI_MODEL", config["AI_MODEL"])}>保存</Button>

frontend/src/app/page.tsx

@@ -1,22 +1,261 @@
+"use client"
+
+import { useState, useEffect } from "react"
+import { useSearchParams } from "next/navigation"
 import { SearchStock } from "@/components/search-stock"
 import { HistoryList } from "@/components/history-list"
+import { DataSourceSelector } from "@/components/data-source-selector"
+import { DataStatusCard } from "@/components/data-status-card"
+import { FinancialTables } from "@/components/financial-tables"
+import { AnalysisTrigger } from "@/components/analysis-trigger"
+import { AnalysisReport } from "@/components/analysis-report"
+import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"
+import { Badge } from "@/components/ui/badge"
+import { Button } from "@/components/ui/button"
+import { ArrowLeft, Building2, RefreshCw, Loader2, CheckCircle2 } from "lucide-react"
+import type { SearchResult } from "@/lib/types"
+import { useFinancialData } from "@/hooks/use-financial-data"
+import { Progress } from "@/components/ui/progress"
+import { formatTimestamp } from "@/lib/formatters"
+import { BloombergView } from "@/components/bloomberg-view"
+import { HeaderPortal } from "@/components/header-portal"
 
 export default function Home() {
+  const searchParams = useSearchParams()
+
+  // 状态管理
+  const [selectedCompany, setSelectedCompany] = useState<SearchResult | null>(null)
+  const [selectedDataSource, setSelectedDataSource] = useState<string>("iFinD")
+  const [companyId, setCompanyId] = useState<number | null>(null)
+  const [showFinancialData, setShowFinancialData] = useState(false)
+  const [analysisId, setAnalysisId] = useState<number | null>(null)
+  const [oneTimeModel, setOneTimeModel] = useState<string | undefined>(undefined)
+
+  // 处理公司选择
+  const handleCompanySelect = (company: SearchResult, dataSource?: string) => {
+    setSelectedCompany(company)
+    setCompanyId(null)
+    setShowFinancialData(false)
+    setAnalysisId(null)
+    setOneTimeModel(undefined)
+
+    // 如果没有传入数据源，则根据市场设置默认值
+    const targetDataSource = dataSource || (company.market === 'CN' ? 'Tushare' : 'iFinD')
+    setSelectedDataSource(targetDataSource)
+  }
+
+  // 监听 URL 参数变化实现快速导航
+  useEffect(() => {
+    const symbol = searchParams.get('symbol')
+    const market = searchParams.get('market')
+    const source = searchParams.get('source')
+    const name = searchParams.get('name')
+
+    if (symbol && market && source) {
+      const company: SearchResult = {
+        symbol,
+        market,
+        company_name: name || symbol
+      }
+      handleCompanySelect(company, source)
+    }
+  }, [searchParams])
+
+  // 数据准备就绪
+  const handleDataReady = (id: number) => {
+    setCompanyId(id)
+    setShowFinancialData(true)
+  }
+
+  // AI分析完成
+  const handleAnalysisComplete = (id: number) => {
+    setAnalysisId(id)
+  }
+
+  // 返回搜索
+  const handleBackToSearch = () => {
+    setSelectedCompany(null)
+    setCompanyId(null)
+    setShowFinancialData(false)
+    setAnalysisId(null)
+    setOneTimeModel(undefined)
+  }
+
   return (
-    <div className="flex flex-col gap-8 p-8 max-w-7xl mx-auto">
+    <div className="min-h-screen w-full p-8">
-      <div className="flex flex-col gap-4">
+      {/* 标题和描述 */}
-        <h1 className="text-3xl font-bold tracking-tight">股票分析</h1>
+      {!selectedCompany && (
-        <p className="text-muted-foreground">
+        <div className="max-w-7xl mx-auto flex flex-col gap-8">
-          输入公司名称或股票代码，开始全面的AI驱动的分析。支持中国、香港、美国、日本、越南的公司分析。
+          <div className="flex flex-col gap-4">
-        </p>
+            <h1 className="text-3xl font-bold tracking-tight">股票分析</h1>
-      </div>
+            <p className="text-muted-foreground">
+              输入公司名称或股票代码，开始全面的AI驱动的分析。支持中国、香港、美国、日本、越南的公司分析。
+            </p>
+          </div>
 
-      <SearchStock />
+          {/* 搜索组件 */}
+          <div className="grid grid-cols-1 gap-6">
+            <SearchStockWithSelection onSelect={handleCompanySelect} />
+          </div>
 
-      <div className="flex flex-col gap-4 mt-8">
+          {/* 最近的报告 */}
-        <h2 className="text-2xl font-bold tracking-tight">最近的报告</h2>
+          <div className="flex flex-col gap-4 mt-8">
-        <HistoryList />
+            <h2 className="text-2xl font-bold tracking-tight">最近的报告</h2>
-      </div>
+            <HistoryList />
+          </div>
+        </div>
+      )}
+
+      {selectedCompany && (
+        <div className="w-full">
+          <CompanyAnalysisView
+            company={selectedCompany}
+            dataSource={selectedDataSource}
+            onBack={handleBackToSearch}
+            onDataSourceChange={setSelectedDataSource}
+            oneTimeModel={oneTimeModel}
+          />
+        </div>
+      )}
     </div>
   )
 }
+
+// 拆分出单独的组件以使用Hooks
+function CompanyAnalysisView({
+  company,
+  dataSource,
+  onBack,
+  onDataSourceChange,
+  oneTimeModel
+}: {
+  company: SearchResult,
+  dataSource: string,
+  onBack: () => void,
+  onDataSourceChange: (ds: string) => void,
+  oneTimeModel?: string
+}) {
+  const {
+    status,
+    loading,
+    fetching,
+    updateStatus,
+    error,
+    fetchData,
+    checkStatus
+  } = useFinancialData(company, dataSource)
+
+  const [companyId, setCompanyId] = useState<number | null>(null)
+  const [analysisId, setAnalysisId] = useState<number | null>(null)
+
+  // 当数据就绪时设置companyId
+  useEffect(() => {
+    if (status?.has_data && status.company_id) {
+      setCompanyId(status.company_id)
+    } else {
+      setCompanyId(null)
+    }
+  }, [status])
+
+  const handleAnalysisComplete = (id: number) => {
+    setAnalysisId(id)
+  }
+
+  const isUpdating = fetching && updateStatus
+  const progress = updateStatus?.progress_percentage || 0
+  const progressStatus = updateStatus?.progress_message || ""
+
+  return (
+    <div className="space-y-6">
+      <HeaderPortal>
+        <div className="flex items-center justify-end w-full gap-4 text-sm h-full">
+          {/* 1. 状态显示 (进度或最后更新) */}
+          <div className="flex items-center text-muted-foreground mr-auto">
+            {isUpdating ? (
+              <div className="flex items-center gap-2 text-xs font-mono bg-muted/50 rounded-full px-3 py-1 animate-pulse">
+                <span className="font-bold text-primary">{Math.round(progress)}%</span>
+                <span className="truncate max-w-[200px]">{progressStatus || "Processing..."}</span>
+              </div>
+            ) : (
+              status?.has_data && status.last_update && (
+                <div className="flex items-center gap-1.5 text-xs opacity-80">
+                  <CheckCircle2 className="h-3.5 w-3.5 text-green-500" />
+                  <span>已更新: {formatTimestamp(status.last_update.date)}</span>
+                </div>
+              )
+            )}
+          </div>
+
+          {/* 2. 公司基础信息 */}
+          <div className="flex items-center gap-3 border-l pl-4 border-r pr-4 h-8">
+            <span className="font-bold truncate max-w-[200px]">{company.company_name}</span>
+            <Badge variant="secondary" className="font-mono h-6">{company.market} {company.symbol}</Badge>
+          </div>
+
+          {/* 3. 操作按钮 (刷新) */}
+          <Button
+            onClick={() => fetchData(true)}
+            disabled={fetching}
+            variant={!status?.has_data ? "default" : "outline"}
+            size="sm"
+            className="h-8"
+          >
+            {fetching ? (
+              <Loader2 className="h-3.5 w-3.5 animate-spin mr-2" />
+            ) : (
+              <RefreshCw className="h-3.5 w-3.5 mr-2" />
+            )}
+            {status?.has_data ? "更新数据" : "获取数据"}
+          </Button>
+        </div>
+      </HeaderPortal>
+
+      {/* 数据状态详情 (Card) */}
+
+
+      {/* 财务数据表格 */}
+      {status?.has_data && companyId && (
+        dataSource === 'Bloomberg' ? (
+          <BloombergView
+            companyId={companyId}
+          />
+        ) : (
+          <FinancialTables
+            companyId={companyId}
+            dataSource={dataSource}
+          />
+        )
+      )}
+
+      {/* AI 分析触发器 */}
+      {status?.has_data && companyId && !analysisId && (
+        <AnalysisTrigger
+          companyId={companyId}
+          dataSource={dataSource}
+          model={oneTimeModel}
+          onAnalysisComplete={handleAnalysisComplete}
+        />
+      )}
+
+      {/* AI 分析报告 */}
+      {analysisId && (
+        <AnalysisReport analysisId={analysisId} />
+      )}
+    </div>
+  )
+}
+
+
+// 搜索组件的包装器，添加选择功能
+function SearchStockWithSelection({ onSelect }: { onSelect: (company: SearchResult) => void }) {
+  return (
+    <Card>
+      <CardHeader>
+        <CardTitle>开始新的分析</CardTitle>
+      </CardHeader>
+      <CardContent>
+        <SearchStock onCompanySelect={onSelect} />
+      </CardContent>
+    </Card>
+  )
+}

frontend/src/components/analysis-report.tsx

@@ -0,0 +1,176 @@
+"use client"
+
+import { useEffect, useState } from "react"
+import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card"
+import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs"
+import { Badge } from "@/components/ui/badge"
+import { Loader2, FileText, TrendingUp, Users, ThumbsUp, ThumbsDown } from "lucide-react"
+import { getAnalysisResult } from "@/lib/api"
+import { MarkdownRenderer } from "@/components/markdown-renderer"
+import type { AnalysisResultResponse } from "@/lib/types"
+
+interface AnalysisReportProps {
+    analysisId: number
+}
+
+export function AnalysisReport({ analysisId }: AnalysisReportProps) {
+    const [data, setData] = useState<AnalysisResultResponse | null>(null)
+    const [loading, setLoading] = useState(true)
+    const [error, setError] = useState("")
+
+    useEffect(() => {
+        async function loadReport() {
+            setLoading(true)
+            setError("")
+            try {
+                const result = await getAnalysisResult(analysisId)
+                setData(result)
+            } catch (err: any) {
+                setError(err.message || "加载分析报告失败")
+            } finally {
+                setLoading(false)
+            }
+        }
+
+        loadReport()
+    }, [analysisId])
+
+    if (loading) {
+        return (
+            <Card>
+                <CardContent className="pt-6 flex items-center justify-center min-h-[400px]">
+                    <Loader2 className="h-8 w-8 animate-spin text-muted-foreground" />
+                </CardContent>
+            </Card>
+        )
+    }
+
+    if (error) {
+        return (
+            <Card>
+                <CardContent className="pt-6">
+                    <div className="text-center text-destructive">{error}</div>
+                </CardContent>
+            </Card>
+        )
+    }
+
+    if (!data) {
+        return null
+    }
+
+    return (
+        <Card>
+            <CardHeader>
+                <div className="flex items-center justify-between">
+                    <div className="flex items-center gap-2">
+                        <FileText className="h-5 w-5" />
+                        <CardTitle>AI 分析报告</CardTitle>
+                    </div>
+                    <div className="flex gap-2">
+                        <Badge variant="outline">{data.ai_model}</Badge>
+                        <Badge variant="secondary">{data.total_tokens} tokens</Badge>
+                    </div>
+                </div>
+                <CardDescription>
+                    使用 {data.data_source} 数据源
+                </CardDescription>
+            </CardHeader>
+            <CardContent>
+                <Tabs defaultValue="profile" className="w-full">
+                    <TabsList className="grid w-full grid-cols-5">
+                        <TabsTrigger value="profile">
+                            <FileText className="h-4 w-4 mr-1" />
+                            公司简介
+                        </TabsTrigger>
+                        <TabsTrigger value="fundamental">
+                            <TrendingUp className="h-4 w-4 mr-1" />
+                            基本面
+                        </TabsTrigger>
+                        <TabsTrigger value="insider">
+                            <Users className="h-4 w-4 mr-1" />
+                            内部人士
+                        </TabsTrigger>
+                        <TabsTrigger value="bullish">
+                            <ThumbsUp className="h-4 w-4 mr-1" />
+                            看涨分析
+                        </TabsTrigger>
+                        <TabsTrigger value="bearish">
+                            <ThumbsDown className="h-4 w-4 mr-1" />
+                            看跌分析
+                        </TabsTrigger>
+                    </TabsList>
+
+                    <TabsContent value="profile" className="mt-4">
+                        <ReportSection
+                            content={data.company_profile}
+                            title="公司简介"
+                            tokens={data.tokens_by_section?.company_profile}
+                        />
+                    </TabsContent>
+
+                    <TabsContent value="fundamental" className="mt-4">
+                        <ReportSection
+                            content={data.fundamental_analysis}
+                            title="基本面分析"
+                            tokens={data.tokens_by_section?.fundamental_analysis}
+                        />
+                    </TabsContent>
+
+                    <TabsContent value="insider" className="mt-4">
+                        <ReportSection
+                            content={data.insider_analysis}
+                            title="内部人士分析"
+                            tokens={data.tokens_by_section?.insider_analysis}
+                        />
+                    </TabsContent>
+
+                    <TabsContent value="bullish" className="mt-4">
+                        <ReportSection
+                            content={data.bullish_analysis}
+                            title="看涨分析"
+                            tokens={data.tokens_by_section?.bullish_analysis}
+                        />
+                    </TabsContent>
+
+                    <TabsContent value="bearish" className="mt-4">
+                        <ReportSection
+                            content={data.bearish_analysis}
+                            title="看跌分析"
+                            tokens={data.tokens_by_section?.bearish_analysis}
+                        />
+                    </TabsContent>
+                </Tabs>
+            </CardContent>
+        </Card>
+    )
+}
+
+interface ReportSectionProps {
+    content?: string | null
+    title: string
+    tokens?: number
+}
+
+function ReportSection({ content, title, tokens }: ReportSectionProps) {
+    if (!content) {
+        return (
+            <div className="text-center text-muted-foreground py-8">
+                {title}内容暂未生成
+            </div>
+        )
+    }
+
+    return (
+        <div className="space-y-2">
+            <div className="prose prose-sm max-w-none dark:prose-invert">
+                <MarkdownRenderer content={content} />
+            </div>
+            {tokens && (
+                <div className="text-xs text-muted-foreground text-right">
+                    本节使用 {tokens} tokens
+                </div>
+            )}
+        </div>
+    )
+}

frontend/src/components/analysis-trigger.tsx

@@ -0,0 +1,141 @@
+"use client"
+
+import { useEffect, useState } from "react"
+import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card"
+import { Button } from "@/components/ui/button"
+import { Badge } from "@/components/ui/badge"
+import { Loader2, Bot, CheckCircle2, AlertCircle } from "lucide-react"
+import { startAIAnalysis, getAnalysisStatus } from "@/lib/api"
+import type { AnalysisStatusResponse } from "@/lib/types"
+
+interface AnalysisTriggerProps {
+    companyId: number
+    dataSource: string
+    model?: string
+    onAnalysisComplete: (analysisId: number) => void
+}
+
+export function AnalysisTrigger({
+    companyId,
+    dataSource,
+    model = "gemini-2.0-flash",
+    onAnalysisComplete
+}: AnalysisTriggerProps) {
+    const [analysisId, setAnalysisId] = useState<number | null>(null)
+    const [status, setStatus] = useState<AnalysisStatusResponse | null>(null)
+    const [error, setError] = useState("")
+    const [polling, setPolling] = useState(false)
+
+    // 轮询分析状态
+    useEffect(() => {
+        if (!analysisId || !polling) return
+
+        const interval = setInterval(async () => {
+            try {
+                const data = await getAnalysisStatus(analysisId)
+                setStatus(data)
+
+                if (data.status === "completed") {
+                    setPolling(false)
+                    onAnalysisComplete(analysisId)
+                } else if (data.status === "failed") {
+                    setPolling(false)
+                    setError(data.error_message || "分析失败")
+                }
+            } catch (err: any) {
+                console.error("Failed to get analysis status:", err)
+            }
+        }, 3000)
+
+        return () => clearInterval(interval)
+    }, [analysisId, polling])
+
+    const handleStartAnalysis = async () => {
+        setError("")
+        setPolling(true)
+        try {
+            const response = await startAIAnalysis({
+                company_id: companyId,
+                data_source: dataSource,
+                model
+            })
+            setAnalysisId(response.analysis_id)
+        } catch (err: any) {
+            setPolling(false)
+            setError(err.message || "启动AI分析失败")
+        }
+    }
+
+    const getStatusBadge = () => {
+        if (!status) return null
+
+        const statusMap: Record<string, { variant: any; label: string }> = {
+            'pending': { variant: 'secondary', label: '等待中' },
+            'in_progress': { variant: 'default', label: '分析中' },
+            'completed': { variant: 'default', label: '已完成' },
+            'failed': { variant: 'destructive', label: '失败' }
+        }
+
+        const config = statusMap[status.status] || statusMap['pending']
+        return <Badge variant={config.variant}>{config.label}</Badge>
+    }
+
+    return (
+        <Card>
+            <CardHeader>
+                <div className="flex items-center justify-between">
+                    <div className="flex items-center gap-2">
+                        <Bot className="h-5 w-5" />
+                        <CardTitle>AI 分析</CardTitle>
+                    </div>
+                    {status && getStatusBadge()}
+                </div>
+                <CardDescription>
+                    使用 {model} 进行深度分析
+                </CardDescription>
+            </CardHeader>
+            <CardContent className="space-y-4">
+                {error && (
+                    <div className="flex items-center gap-2 text-sm text-destructive">
+                        <AlertCircle className="h-4 w-4" />
+                        <span>{error}</span>
+                    </div>
+                )}
+
+                {polling && status && (
+                    <div className="space-y-2">
+                        <div className="flex items-center gap-2">
+                            {status.status === "in_progress" ? (
+                                <Loader2 className="h-4 w-4 animate-spin" />
+                            ) : (
+                                <CheckCircle2 className="h-4 w-4 text-green-600" />
+                            )}
+                            <span className="text-sm font-medium">
+                                {status.status === "pending" && "等待分析开始..."}
+                                {status.status === "in_progress" && "AI正在分析财务数据..."}
+                                {status.status === "completed" && "分析已完成"}
+                            </span>
+                        </div>
+                        <div className="text-xs text-muted-foreground">
+                            这可能需要1-2分钟，请耐心等待
+                        </div>
+                    </div>
+                )}
+
+                {!polling && !status && (
+                    <Button onClick={handleStartAnalysis} className="w-full">
+                        <Bot className="mr-2 h-4 w-4" />
+                        开始 AI 分析
+                    </Button>
+                )}
+
+                {status && status.status === "completed" && !polling && (
+                    <div className="flex items-center gap-2 text-sm text-green-600">
+                        <CheckCircle2 className="h-4 w-4" />
+                        <span>分析报告已生成</span>
+                    </div>
+                )}
+            </CardContent>
+        </Card>
+    )
+}

frontend/src/components/bloomberg-view.tsx

@@ -0,0 +1,530 @@
+"use client"
+
+import { useEffect, useState } from "react"
+import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"
+import { Table, TableBody, TableCell, TableHead, TableHeader, TableRow } from "@/components/ui/table"
+import { Loader2, DollarSign, RefreshCw } from "lucide-react"
+import { Button } from "@/components/ui/button"
+import { getFinancialData } from "@/lib/api"
+import { formatNumber, formatLargeNumber, formatDate } from "@/lib/formatters"
+import type { FinancialDataResponse } from "@/lib/types"
+
+interface BloombergViewProps {
+    companyId: number
+    onBack?: () => void
+}
+
+export function BloombergView({ companyId, onBack }: BloombergViewProps) {
+    const [data, setData] = useState<FinancialDataResponse | null>(null)
+    const [loading, setLoading] = useState(true)
+    const [error, setError] = useState("")
+
+    const loadData = async () => {
+        setLoading(true)
+        setError("")
+        try {
+            console.log("Fetching Bloomberg data for company:", companyId)
+            const result = await getFinancialData(companyId, "Bloomberg")
+            console.log("Bloomberg data result:", result)
+            setData(result)
+        } catch (err: any) {
+            console.error("Bloomberg fetch error:", err)
+            setError(err.message || "加载 Bloomberg 数据失败")
+        } finally {
+            setLoading(false)
+        }
+    }
+
+    useEffect(() => {
+        loadData()
+    }, [companyId])
+
+    if (loading) {
+        return (
+            <div className="flex flex-col items-center justify-center min-h-[400px] space-y-4">
+                <Loader2 className="h-8 w-8 animate-spin text-primary" />
+                <p className="text-muted-foreground">正在加载 Bloomberg 原始数据...</p>
+            </div>
+        )
+    }
+
+    if (error) {
+        return (
+            <Card className="border-destructive">
+                <CardContent className="pt-6">
+                    <div className="text-center text-destructive space-y-4">
+                        <p>{error}</p>
+                        <Button variant="outline" onClick={loadData}>重试</Button>
+                    </div>
+                </CardContent>
+            </Card>
+        )
+    }
+
+    if (!data) return null
+
+    // 如果后端提供了统一数据字段，直接使用
+    const mergedData = data.unified_data || []
+
+    return (
+        <div className="space-y-8">
+            <div className="flex items-center justify-between">
+                <h2 className="text-2xl font-bold flex items-center gap-2">
+                    <DollarSign className="w-6 h-6" />
+                    Bloomberg 财务数据总览
+                </h2>
+                <Button variant="outline" size="sm" onClick={loadData}>
+                    <RefreshCw className="w-4 h-4 mr-2" />
+                    刷新数据
+                </Button>
+            </div>
+
+            <RawDataTable title="财务数据总表" data={mergedData} />
+        </div>
+    )
+}
+
+function RawDataTable({ data, title }: { data: any[], title: string }) {
+    if (!data || data.length === 0) {
+        return (
+            <Card>
+                <CardHeader>
+                    <CardTitle className="text-base">{title}</CardTitle>
+                </CardHeader>
+                <CardContent>
+                    <div className="text-center text-muted-foreground py-8">暂无数据</div>
+                </CardContent>
+            </Card>
+        )
+    }
+
+    // 提取所有指标键值 (排除元数据)
+    const excludeKeys = ['id', 'company_code', 'code', 'symbol', 'market', 'update_date', 'create_time', 'end_date', 'ts_code']
+
+    // 从合并后的数据中收集所有可能的指标
+    const allIndicators = new Set<string>()
+    data.forEach(row => {
+        Object.keys(row).forEach(k => {
+            if (!excludeKeys.includes(k)) {
+                allIndicators.add(k)
+            }
+        })
+    })
+
+    // 对指标进行逻辑排序/分组
+    const priorityOrder = [
+        'currency',
+        '__SECTION_MAIN__',
+        'roe', 'roce', 'roa',
+        'gross_margin', 'net_profit_margin', 'ebitda_margin',
+        'revenue', 'revenue_growth',
+        'net_income', 'netincome_growth',
+        'cash_from_operating', 'capital_expenditure', 'free_cash_flow',
+        'dividends_paid', 'dividend_payout_ratio', 'repurchase',
+        'total_assets', 'equity', 'goodwill',
+        '__SECTION_EXPENSE__',
+        'selling&marketing', 'general&admin', 'sg&a', 'r&d',
+        'depreciation', 'tax_rate',
+        '__SECTION_ASSET__',
+        'cash', 'inventory', 'accounts&notes_receivable', 'prepaid',
+        'property_plant&equipment', 'lt_investment',
+        'accounts_payable', 'st_defer_rev',
+        'st_debt', 'lt_debt', 'total_debt_ratio',
+        '__SECTION_TURNOVER__',
+        'inventory_days', 'days_sales_outstanding', 'payables_days',
+        'net_fixed_asset_turnover', 'asset_turnover',
+        '__SECTION_EFFICIENCY__',
+        'employee', 'num_of_employees',
+        '__SECTION_MARKET__',
+        'last_price', 'market_cap', 'pe', 'pb',
+        'dividend_yield', 'shareholders'
+    ]
+
+    // Ensure section header is in the list to be sorted
+    const sectionKeys = [
+        '__SECTION_MAIN__', '__SECTION_EXPENSE__', '__SECTION_ASSET__',
+        '__SECTION_TURNOVER__', '__SECTION_EFFICIENCY__', '__SECTION_MARKET__'
+    ]
+    sectionKeys.forEach(key => {
+        if (!allIndicators.has(key)) {
+            allIndicators.add(key)
+        }
+    })
+
+    const sortedIndicators = Array.from(allIndicators).sort((a, b) => {
+        const idxA = priorityOrder.indexOf(a)
+        const idxB = priorityOrder.indexOf(b)
+
+        // Items in priority list come first, sorted by their order in the list
+        if (idxA !== -1 && idxB !== -1) return idxA - idxB
+
+        // Items in priority list come before items not in list
+        if (idxA !== -1) return -1
+        if (idxB !== -1) return 1
+
+        // Remaining items sorted alphabetically
+        return a.localeCompare(b)
+    })
+
+    // 构建查找表: Map<DateString, RowData>
+    const dataMap = new Map()
+    data.forEach(row => {
+        dataMap.set(row.end_date, row)
+    })
+
+    // 过滤日期: 只保留有营业收入 (revenue) 的列
+    const dates = Array.from(new Set(data.map(row => row.end_date)))
+        .filter(date => {
+            const row = dataMap.get(date)
+            // Check if revenue exists and is not null/undefined
+            return row && row.revenue !== null && row.revenue !== undefined
+        })
+        .sort((a, b) => new Date(b).getTime() - new Date(a).getTime())
+
+
+    // Tooltip State
+    const [tooltip, setTooltip] = useState<{ show: boolean, x: number, y: number, text: string }>({
+        show: false, x: 0, y: 0, text: ''
+    })
+
+    const handleMouseMove = (e: React.MouseEvent, text: string) => {
+        setTooltip({
+            show: true,
+            x: e.clientX,
+            y: e.clientY,
+            text
+        })
+    }
+
+    const handleMouseLeave = () => {
+        setTooltip(prev => ({ ...prev, show: false }))
+    }
+
+    // 获取第一行数据的货币单位 (假设一致)
+    const currency = data[0]?.currency || 'CNY'
+
+    return (
+        <div className="relative">
+            {tooltip.show && (
+                <div
+                    className="fixed z-50 px-2 py-1 text-xs text-white bg-black/90 rounded pointer-events-none whitespace-nowrap"
+                    style={{
+                        left: tooltip.x + 10,
+                        top: tooltip.y + 10
+                    }}
+                >
+                    {tooltip.text}
+                </div>
+            )}
+
+            <div className="rounded-md border">
+                <table className="w-full caption-bottom text-sm">
+                    <TableHeader>
+                        <TableRow>
+                            <TableHead className="w-[200px] sticky left-0 top-0 bg-background z-50 border-r border-b shadow-[2px_0_5px_-2px_rgba(0,0,0,0.1)]">
+                                <div className="flex flex-col gap-1 py-1">
+                                    <span className="font-bold text-foreground">指标 / 报告期</span>
+                                    <span className="text-xs font-normal text-muted-foreground">(单位: 亿)</span>
+                                </div>
+                            </TableHead>
+                            {dates.map(date => (
+                                <TableHead key={date} className="text-right min-w-[120px] px-4 font-mono sticky top-0 bg-background z-40 border-b">
+                                    {formatDate(date)}
+                                </TableHead>
+                            ))}
+                        </TableRow>
+                    </TableHeader>
+                    <TableBody>
+                        {sortedIndicators.map(indicator => {
+                            if (indicator === '__SECTION_MAIN__') {
+                                return (
+                                    <TableRow key={indicator} className="bg-muted">
+                                        <TableCell
+                                            className="font-bold sticky left-0 bg-muted z-10 border-r py-1"
+                                            colSpan={1}
+                                        >
+                                            主要指标
+                                        </TableCell>
+                                        <TableCell colSpan={dates.length} className="bg-muted p-0" />
+                                    </TableRow>
+                                )
+                            }
+                            if (indicator === '__SECTION_EXPENSE__') {
+                                return (
+                                    <TableRow key={indicator} className="bg-muted">
+                                        <TableCell
+                                            className="font-bold sticky left-0 bg-muted z-10 border-r py-1"
+                                            colSpan={1}
+                                        >
+                                            费用指标
+                                        </TableCell>
+                                        <TableCell colSpan={dates.length} className="bg-muted p-0" />
+                                    </TableRow>
+                                )
+                            }
+                            if (indicator === '__SECTION_ASSET__') {
+                                return (
+                                    <TableRow key={indicator} className="bg-muted">
+                                        <TableCell
+                                            className="font-bold sticky left-0 bg-muted z-10 border-r py-1"
+                                            colSpan={1}
+                                        >
+                                            资产结构
+                                        </TableCell>
+                                        <TableCell colSpan={dates.length} className="bg-muted p-0" />
+                                    </TableRow>
+                                )
+                            }
+                            if (indicator === '__SECTION_TURNOVER__') {
+                                return (
+                                    <TableRow key={indicator} className="bg-muted">
+                                        <TableCell
+                                            className="font-bold sticky left-0 bg-muted z-10 border-r py-1"
+                                            colSpan={1}
+                                        >
+                                            周转能力
+                                        </TableCell>
+                                        <TableCell colSpan={dates.length} className="bg-muted p-0" />
+                                    </TableRow>
+                                )
+                            }
+                            if (indicator === '__SECTION_EFFICIENCY__') {
+                                return (
+                                    <TableRow key={indicator} className="bg-muted">
+                                        <TableCell
+                                            className="font-bold sticky left-0 bg-muted z-10 border-r py-1"
+                                            colSpan={1}
+                                        >
+                                            人均效率
+                                        </TableCell>
+                                        <TableCell colSpan={dates.length} className="bg-muted p-0" />
+                                    </TableRow>
+                                )
+                            }
+                            if (indicator === '__SECTION_MARKET__') {
+                                return (
+                                    <TableRow key={indicator} className="bg-muted">
+                                        <TableCell
+                                            className="font-bold sticky left-0 bg-muted z-10 border-r py-1"
+                                            colSpan={1}
+                                        >
+                                            市场表现
+                                        </TableCell>
+                                        <TableCell colSpan={dates.length} className="bg-muted p-0" />
+                                    </TableRow>
+                                )
+                            }
+                            return (
+                                <TableRow key={indicator} className="hover:bg-purple-100 dark:hover:bg-purple-900/40">
+                                    <TableCell
+                                        className={`font-medium sticky left-0 bg-background z-10 border-r shadow-[2px_0_5px_-2px_rgba(0,0,0,0.1)] cursor-help transition-colors hover:bg-muted ${['revenue_growth', 'netincome_growth'].includes(indicator) ? 'text-blue-600 italic dark:text-blue-400' : ''
+                                            }`}
+                                        onMouseMove={(e) => handleMouseMove(e, indicator)}
+                                        onMouseLeave={handleMouseLeave}
+                                    >
+                                        <span>{formatColumnName(indicator)}</span>
+                                    </TableCell>
+                                    {dates.map(date => {
+                                        const row = dataMap.get(date)
+                                        const value = row ? row[indicator] : null
+
+                                        let highlightClass = ''
+                                        let textStyle = ''
+
+                                        if (value !== null && value !== undefined) {
+                                            const numVal = typeof value === 'string' ? parseFloat(value) : value
+                                            if (typeof numVal === 'number' && !isNaN(numVal)) {
+                                                if (indicator === 'roe' && numVal > 15) highlightClass = 'bg-green-100 dark:bg-green-900/40'
+                                                else if (indicator === 'gross_margin' && numVal > 40) highlightClass = 'bg-green-100 dark:bg-green-900/40'
+                                                else if (indicator === 'net_profit_margin' && numVal > 15) highlightClass = 'bg-green-100 dark:bg-green-900/40'
+                                                else if (indicator === 'roce' && numVal > 15) highlightClass = 'bg-green-100 dark:bg-green-900/40'
+                                                else if (indicator === 'roa' && numVal > 10) highlightClass = 'bg-green-100 dark:bg-green-900/40'
+                                                else if (indicator === 'revenue_growth') {
+                                                    if (numVal > 15) highlightClass = 'bg-green-100 dark:bg-green-900/40'
+                                                    else if (numVal < 0) highlightClass = 'bg-red-100 dark:bg-red-900/40'
+                                                }
+                                                else if (indicator === 'netincome_growth') {
+                                                    if (numVal > 20) highlightClass = 'bg-green-100 dark:bg-green-900/40'
+                                                    else if (numVal < 0) highlightClass = 'bg-red-100 dark:bg-red-900/40'
+                                                }
+
+                                                // Dynamic text style based on value for growth rates
+                                                if (['revenue_growth', 'netincome_growth'].includes(indicator)) {
+                                                    if (numVal < 0) {
+                                                        textStyle = 'text-red-600 italic font-bold dark:text-red-400'
+                                                    } else {
+                                                        textStyle = 'text-blue-600 italic dark:text-blue-400'
+                                                    }
+                                                }
+                                            }
+                                        }
+
+                                        // Fallback for text style if value is missing/invalid but indicator is growth rate
+                                        if (textStyle === '' && ['revenue_growth', 'netincome_growth'].includes(indicator)) {
+                                            textStyle = 'text-blue-600 italic dark:text-blue-400'
+                                        }
+
+                                        return (
+                                            <TableCell key={date} className={`text-right px-4 font-mono ${highlightClass} ${textStyle}`}>
+                                                {formatCellValue(indicator, value)}
+                                            </TableCell>
+                                        )
+                                    })}
+                                </TableRow>
+                            )
+                        })}
+                    </TableBody>
+                </table>
+            </div>
+        </div>
+    )
+}
+
+// 复用之前的 formatting 逻辑
+function formatColumnName(column: string): string {
+    const nameMap: Record<string, string> = {
+        // --- 核心利润表 ---
+        'revenue': '营业收入',
+        'net_income': '净利润',
+        'operating_profit': '营业利润',
+        'gross_margin': '毛利率',
+        'net_profit_margin': '净利率',
+        'ebitda_margin': 'EBITDA利润率',
+        'sg&a': '销售管理费用(SG&A)',
+        'selling&marketing': '销售费用',
+        'general&admin': '管理费用',
+        'ga_exp': '管理费用', // Alias
+        'rd_exp': '研发费用',
+        'r&d': '研发费用',
+        'depreciation': '折旧',
+        'tax_rate': '有效税率',
+        'dividends': '分红',
+        'dividend_payout_ratio': '分红支付率',
+        'netincome_growth': '净利润增长率',
+        'revenue_growth': '营收增长率',
+
+        // --- 资产负债表 ---
+        'total_assets': '总资产',
+        'total_liabilities': '总负债',
+        'shareholders_equity': '净资产', // Alias
+        'total_equity': '净资产', // Alias
+        'equity': '净资产', // Real Bloomberg Key
+        'cash': '现金',
+        'inventory': '存货',
+        'accounts&notes_receivable': '应收账款及票据',
+        'receivables': '应收账款',
+        'accounts_payable': '应付账款',
+        'prepayment': '预付款项',
+        'prepaid': '预付款项',
+        'fixed_assets': '固定资产',
+        'property_plant&equipment': '固定资产(PP&E)',
+        'long_term_investments': '长期投资',
+        'lt_investment': '长期投资',
+        'short_term_debt': '短期借款',
+        'st_debt': '短期借款',
+        'long_term_debt': '长期借款',
+        'lt_debt': '长期借款',
+        'deferred_revenue': '递延收入',
+        'st_defer_rev': '短期递延收入',
+        'goodwill': '商誉',
+        'total_debt_ratio': '总债务比率',
+
+        // --- 现金流量表 ---
+        'cash_from_operating': '经营现金流',
+        'ocf': '经营现金流', // Alias
+        'capital_expenditure': '资本支出',
+        'capex': '资本支出', // Alias
+        'free_cash_flow': '自由现金流',
+        'fcf': '自由现金流', // Alias
+        'dividends_paid': '支付股息',
+        'repurchase': '股份回购',
+
+        // --- 运营效率/周转 ---
+        'asset_turnover': '总资产周转率',
+        'net_fixed_asset_turnover': '固定资产周转率', // Correct Bloomberg Key
+        'net_fixed_asset_turn': '固定资产周转率', // Alias just in case
+        'inventory_days': '存货周转天数',
+        'invent_days': '存货周转天数',
+        'days_sales_outstanding': '应收账款周转天数',
+        'payables_days': '应付账款周转天数',
+        'employee': '员工人数',
+        'num_of_employees': '员工人数',
+
+        // --- 估值与市场 ---
+        'pe': '市盈率(PE)',
+        'pe_ratio': '市盈率(PE)',
+        'pb': '市净率(PB)',
+        'pb_ratio': '市净率(PB)',
+        'market_cap': '市值',
+        'price': '最新股价',
+        'last_price': '最新股价',
+        'roe': 'ROE',
+        'roa': 'ROA',
+        'roce': 'ROCE',
+        'dividend_yield': '股息率',
+        'shareholders': '股东人数',
+        'company_name': '公司名称',
+        'ipo_date': '上市日期',
+        'rev_abroad': '海外收入占比',
+        'currency': '货币',
+    }
+    return nameMap[column.toLowerCase()] || column
+}
+
+function formatCellValue(column: string, value: any): string {
+    if (value === null || value === undefined) return '-'
+
+    // 尝试解析文本数字
+    let numVal = value
+    if (typeof value === 'string') {
+        const parsed = parseFloat(value)
+        if (!isNaN(parsed)) {
+            numVal = parsed
+        }
+    }
+
+    if (typeof numVal === 'number') {
+        const lowerCol = column.toLowerCase()
+
+        // Special scaling for tax_rate
+        if (lowerCol === 'tax_rate') {
+            numVal = numVal * 100
+        }
+
+        const isRatio = ['roe', 'roce', 'roa', 'gross_margin', 'net_profit_margin', 'ebitda_margin', 'dividend_yield', 'rev_abroad', 'tax_rate', 'revenue_growth', 'netincome_growth'].includes(lowerCol)
+
+        // Scale huge monetary values to "Yi" (100 Million)
+        // Bloomberg raw data is in Millions.
+        // Millions -> Yi (100 Millions) = divide by 100.
+        const isMoney = [
+            'revenue', 'net_income', 'operating_profit',
+            'total_assets', 'total_liabilities', 'equity', 'shareholders_equity', 'total_equity',
+            'cash', 'inventory', 'accounts&notes_receivable', 'receivables', 'accounts_payable', 'prepaid', 'prepayment',
+            'property_plant&equipment', 'fixed_assets', 'lt_investment', 'long_term_investments',
+            'st_debt', 'short_term_debt', 'lt_debt', 'long_term_debt', 'st_defer_rev', 'deferred_revenue', 'goodwill',
+            'cash_from_operating', 'capital_expenditure', 'capex', 'free_cash_flow', 'fcf',
+            'dividends_paid', 'repurchase',
+            'sg&a', 'selling&marketing', 'general&admin', 'r&d', 'depreciation',
+            'market_cap'
+        ].includes(lowerCol)
+
+        if (isMoney) {
+            numVal = numVal / 100
+        }
+
+        if (isRatio) {
+            return numVal.toLocaleString('en-US', {
+                minimumFractionDigits: 2,
+                maximumFractionDigits: 2
+            }) + '%'
+        }
+
+        // 如果大于 1000，不保留小数；否则保留两位小数
+        const isLarge = Math.abs(numVal) > 1000
+        return numVal.toLocaleString('en-US', {
+            minimumFractionDigits: isLarge ? 0 : 2,
+            maximumFractionDigits: isLarge ? 0 : 2
+        })
+    }
+    return String(value)
+}

frontend/src/components/data-source-selector.tsx

@@ -0,0 +1,95 @@
+"use client"
+
+import { useEffect, useState } from "react"
+import {
+    Select,
+    SelectContent,
+    SelectItem,
+    SelectTrigger,
+    SelectValue,
+} from "@/components/ui/select"
+import { Label } from "@/components/ui/label"
+import { Database } from "lucide-react"
+import { getAvailableSources } from "@/lib/api"
+import type { DataSourceInfo } from "@/lib/types"
+
+interface DataSourceSelectorProps {
+    market: string
+    selectedSource: string
+    onSourceChange: (source: string) => void
+    disabled?: boolean
+    showLabel?: boolean
+}
+
+export function DataSourceSelector({
+    market,
+    selectedSource,
+    onSourceChange,
+    disabled = false,
+    showLabel = true
+}: DataSourceSelectorProps) {
+    const [sources, setSources] = useState<DataSourceInfo[]>([])
+    const [loading, setLoading] = useState(false)
+
+    useEffect(() => {
+        async function loadSources() {
+            if (!market) return
+
+            setLoading(true)
+            try {
+                const data = await getAvailableSources(market)
+                setSources(data.sources.filter(s => s.available))
+
+                // 如果当前选中的源不可用，自动选择第一个可用的
+                const availableSources = data.sources.filter(s => s.available)
+                if (availableSources.length > 0 && !availableSources.find(s => s.source === selectedSource)) {
+                    onSourceChange(availableSources[0].source)
+                }
+            } catch (error) {
+                console.error("Failed to load data sources:", error)
+            } finally {
+                setLoading(false)
+            }
+        }
+
+        loadSources()
+    }, [market])
+
+    if (sources.length === 0) {
+        return null
+    }
+
+    return (
+        <div className="space-y-2">
+            {showLabel && (
+                <div className="flex items-center gap-2">
+                    <Database className="h-4 w-4 text-muted-foreground" />
+                    <Label>数据源</Label>
+                </div>
+            )}
+            <Select
+                value={selectedSource}
+                onValueChange={onSourceChange}
+                disabled={disabled || loading}
+            >
+                <SelectTrigger>
+                    <SelectValue placeholder="选择数据源" />
+                </SelectTrigger>
+                <SelectContent portal={false}>
+                    {sources.map((source) => (
+                        <SelectItem key={source.source} value={source.source}>
+                            {source.source}
+                        </SelectItem>
+                    ))}
+                </SelectContent>
+            </Select>
+            {
+                sources.length === 1 && (
+                    <p className="text-xs text-muted-foreground">
+                        该市场仅支持 {sources[0].source} 数据源
+                    </p>
+                )
+            }
+        </div >
+    )
+}

frontend/src/components/data-status-card.tsx

@@ -0,0 +1,215 @@
+"use client"
+
+import { useEffect, useState } from "react"
+import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card"
+import { Button } from "@/components/ui/button"
+import { Badge } from "@/components/ui/badge"
+import { Loader2, Database, CheckCircle2, AlertCircle, RefreshCw } from "lucide-react"
+import { checkDataStatus, fetchFinancialData, getFetchStatus } from "@/lib/api"
+import { formatTimestamp } from "@/lib/formatters"
+import type { DataCheckResponse, DataUpdateResponse, SearchResult } from "@/lib/types"
+
+interface DataStatusCardProps {
+    company: SearchResult
+    dataSource: string
+    onDataReady: (companyId: number) => void
+}
+
+export function DataStatusCard({ company, dataSource, onDataReady }: DataStatusCardProps) {
+    const [status, setStatus] = useState<DataCheckResponse | null>(null)
+    const [loading, setLoading] = useState(true)
+    const [fetching, setFetching] = useState(false)
+    const [updateId, setUpdateId] = useState<number | null>(null)
+    const [updateStatus, setUpdateStatus] = useState<DataUpdateResponse | null>(null)
+    const [error, setError] = useState<string>("")
+
+    // 检查数据状态
+    const checkStatus = async () => {
+        setLoading(true)
+        setError("")
+        try {
+            const data = await checkDataStatus(company.market, company.symbol, dataSource)
+            setStatus(data)
+        } catch (err: any) {
+            setError(err.message || "检查数据状态失败")
+        } finally {
+            setLoading(false)
+        }
+    }
+
+    useEffect(() => {
+        checkStatus()
+    }, [company, dataSource])
+
+    // 轮询获取状态
+    useEffect(() => {
+        if (!updateId || !fetching) return
+
+        const interval = setInterval(async () => {
+            try {
+                const data = await getFetchStatus(updateId)
+                setUpdateStatus(data)
+
+                if (data.status === "completed") {
+                    setFetching(false)
+                    setUpdateId(null)
+                    // 刷新数据状态
+                    await checkStatus()
+                    // 通知父组件数据已就绪
+                    if (data.company_id) {
+                        onDataReady(data.company_id)
+                    }
+                } else if (data.status === "failed") {
+                    setFetching(false)
+                    setUpdateId(null)
+                    setError(data.error_message || "数据获取失败")
+                }
+            } catch (err: any) {
+                console.error("Failed to get fetch status:", err)
+            }
+        }, 2000)
+
+        return () => clearInterval(interval)
+    }, [updateId, fetching])
+
+    // 触发数据获取
+    const handleFetchData = async (forceRefresh = false) => {
+        setFetching(true)
+        setError("")
+        try {
+            const response = await fetchFinancialData({
+                market: company.market,
+                symbol: company.symbol,
+                company_name: company.company_name,
+                data_source: dataSource,
+                force_refresh: forceRefresh
+            })
+            setUpdateId(response.update_id)
+        } catch (err: any) {
+            setFetching(false)
+            setError(err.message || "启动数据获取失败")
+        }
+    }
+
+    if (loading) {
+        return (
+            <Card>
+                <CardContent className="pt-6 flex items-center justify-center">
+                    <Loader2 className="h-6 w-6 animate-spin text-muted-foreground" />
+                </CardContent>
+            </Card>
+        )
+    }
+
+    return (
+        <Card>
+            <CardHeader>
+                <div className="flex items-center justify-between">
+                    <div className="flex items-center gap-2">
+                        <Database className="h-5 w-5" />
+                        <CardTitle>数据状态</CardTitle>
+                    </div>
+                    <Badge variant={status?.has_data ? "default" : "secondary"}>
+                        {dataSource}
+                    </Badge>
+                </div>
+                <CardDescription>
+                    {company.company_name} ({company.symbol})
+                </CardDescription>
+            </CardHeader>
+            <CardContent className="space-y-4">
+                {error && (
+                    <div className="flex items-center gap-2 text-sm text-destructive">
+                        <AlertCircle className="h-4 w-4" />
+                        <span>{error}</span>
+                    </div>
+                )}
+
+                {fetching && updateStatus && (
+                    <div className="space-y-2">
+                        <div className="flex items-center gap-2">
+                            <Loader2 className="h-4 w-4 animate-spin" />
+                            <span className="text-sm font-medium">
+                                {updateStatus.status === "in_progress" ? "正在获取数据..." : "等待中..."}
+                            </span>
+                        </div>
+                        {updateStatus.fetched_tables && updateStatus.fetched_tables.length > 0 && (
+                            <div className="text-xs text-muted-foreground">
+                                已获取: {updateStatus.fetched_tables.join(", ")}
+                            </div>
+                        )}
+                    </div>
+                )}
+
+                {!fetching && status?.has_data && status.last_update && (
+                    <div className="space-y-2">
+                        <div className="flex items-center gap-2 text-sm text-green-600">
+                            <CheckCircle2 className="h-4 w-4" />
+                            <span>数据已就绪</span>
+                        </div>
+                        <div className="grid grid-cols-2 gap-2 text-sm">
+                            <div>
+                                <span className="text-muted-foreground">最后更新:</span>
+                                <br />
+                                <span className="font-mono text-xs">
+                                    {formatTimestamp(status.last_update.date)}
+                                </span>
+                            </div>
+                            {status.last_update.data_start_date && status.last_update.data_end_date && (
+                                <div>
+                                    <span className="text-muted-foreground">数据范围:</span>
+                                    <br />
+                                    <span className="font-mono text-xs">
+                                        {status.last_update.data_start_date} ~ {status.last_update.data_end_date}
+                                    </span>
+                                </div>
+                            )}
+                        </div>
+                        {status.last_update.table_counts && (
+                            <div className="text-xs text-muted-foreground">
+                                表格数据: {Object.entries(status.last_update.table_counts).map(
+                                    ([table, count]) => `${table}(${count}行)`
+                                ).join(", ")}
+                            </div>
+                        )}
+                    </div>
+                )}
+
+                {!fetching && !status?.has_data && (
+                    <div className="text-sm text-muted-foreground">
+                        暂无该数据源的数据
+                    </div>
+                )}
+
+                <div className="flex gap-2">
+                    {!status?.has_data && (
+                        <Button
+                            onClick={() => handleFetchData(false)}
+                            disabled={fetching}
+                            className="w-full"
+                        >
+                            {fetching ? (
+                                <>
+                                    <Loader2 className="mr-2 h-4 w-4 animate-spin" />
+                                    获取中...
+                                </>
+                            ) : (
+                                "获取数据"
+                            )}
+                        </Button>
+                    )}
+                    {status?.has_data && !fetching && (
+                        <Button
+                            onClick={() => handleFetchData(true)}
+                            variant="outline"
+                            className="w-full"
+                        >
+                            <RefreshCw className="mr-2 h-4 w-4" />
+                            更新数据
+                        </Button>
+                    )}
+                </div>
+            </CardContent>
+        </Card>
+    )
+}

frontend/src/components/data-status-display.tsx

@@ -0,0 +1,135 @@
+"use client"
+
+import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card"
+import { Badge } from "@/components/ui/badge"
+import { Loader2, Database, CheckCircle2, AlertCircle } from "lucide-react"
+import { Progress } from "@/components/ui/progress"
+import type { DataCheckResponse, DataUpdateResponse, SearchResult } from "@/lib/types"
+import { formatTimestamp } from "@/lib/formatters"
+
+interface DataStatusDisplayProps {
+    company: SearchResult
+    dataSource: string
+    status: DataCheckResponse | null
+    updateStatus: DataUpdateResponse | null
+    loading: boolean
+    fetching: boolean
+    error: string
+}
+
+export function DataStatusDisplay({
+    company,
+    dataSource,
+    status,
+    updateStatus,
+    loading,
+    fetching,
+    error
+}: DataStatusDisplayProps) {
+
+    if (loading) {
+        return (
+            <Card>
+                <CardContent className="pt-6 flex items-center justify-center">
+                    <Loader2 className="h-6 w-6 animate-spin text-muted-foreground" />
+                </CardContent>
+            </Card>
+        )
+    }
+
+    // Determine if we should show fetching state
+    const isUpdating = fetching && updateStatus;
+    const progress = updateStatus?.progress_percentage || 0;
+    const message = updateStatus?.progress_message || (updateStatus?.status === "in_progress" ? "正在获取数据..." : "准备中...");
+
+    return (
+        <Card>
+            <CardHeader className="pb-3">
+                <div className="flex items-center justify-between">
+                    <div className="flex items-center gap-2">
+                        <Database className="h-5 w-5" />
+                        <CardTitle>数据状态</CardTitle>
+                    </div>
+                </div>
+            </CardHeader>
+            <CardContent className="space-y-4">
+                {error && (
+                    <div className="flex items-center gap-2 text-sm text-destructive bg-destructive/10 p-3 rounded-md">
+                        <AlertCircle className="h-4 w-4" />
+                        <span>{error}</span>
+                    </div>
+                )}
+
+                {/* 正在更新状态 */}
+                {isUpdating && (
+                    <div className="space-y-4 p-4 bg-muted/50 rounded-lg border">
+                        <div className="flex items-center justify-between">
+                            <div className="flex items-center gap-2">
+                                <Loader2 className="h-4 w-4 animate-spin text-primary" />
+                                <span className="font-medium">
+                                    {message}
+                                </span>
+                            </div>
+                            <span className="text-sm text-muted-foreground font-mono">{Math.round(progress)}%</span>
+                        </div>
+
+                        <Progress value={progress} className="h-2" />
+
+                        {updateStatus?.fetched_tables && updateStatus.fetched_tables.length > 0 && (
+                            <div className="text-xs text-muted-foreground mt-2">
+                                已完成: {updateStatus.fetched_tables.map(t => t.replace('tushare_', '').replace('cn_', '')).join(", ")}
+                            </div>
+                        )}
+                    </div>
+                )}
+
+                {/* 静态数据状态 (非更新时显示) */}
+                {!isUpdating && status?.has_data && status.last_update && (
+                    <div className="space-y-2">
+                        <div className="flex items-center gap-2 text-sm text-green-600 font-medium">
+                            <CheckCircle2 className="h-4 w-4" />
+                            <span>数据已就绪</span>
+                        </div>
+
+                        <div className="grid grid-cols-1 md:grid-cols-2 gap-4 mt-2 p-3 bg-muted/30 rounded-md">
+                            <div>
+                                <span className="text-muted-foreground text-xs uppercase tracking-wider">最后更新</span>
+                                <div className="font-mono text-sm mt-0.5">
+                                    {formatTimestamp(status.last_update.date)}
+                                </div>
+                            </div>
+
+                            {status.last_update.data_start_date && status.last_update.data_end_date && (
+                                <div>
+                                    <span className="text-muted-foreground text-xs uppercase tracking-wider">数据范围</span>
+                                    <div className="font-mono text-sm mt-0.5">
+                                        {status.last_update.data_start_date} <span className="text-muted-foreground">至</span> {status.last_update.data_end_date}
+                                    </div>
+                                </div>
+                            )}
+                        </div>
+
+                        {status.last_update.table_counts && (
+                            <div className="text-xs text-muted-foreground pt-2 flex flex-wrap gap-2">
+                                {Object.entries(status.last_update.table_counts).map(([table, count]) => (
+                                    <Badge key={table} variant="secondary" className="font-normal">
+                                        {table.replace('tushare_', '').replace('cn_', '')}: {count}
+                                    </Badge>
+                                ))}
+                            </div>
+                        )}
+                    </div>
+                )}
+
+                {/* 无数据状态 */}
+                {!isUpdating && !status?.has_data && !error && (
+                    <div className="flex flex-col items-center justify-center py-8 text-center text-muted-foreground space-y-2">
+                        <Database className="h-8 w-8 opacity-20" />
+                        <p>暂无该数据源的数据</p>
+                        <p className="text-xs">请点击右上角的"获取数据"按钮开始同步</p>
+                    </div>
+                )}
+            </CardContent>
+        </Card>
+    )
+}

frontend/src/components/financial-tables.tsx

@@ -0,0 +1,306 @@
+"use client"
+
+import { useEffect, useState } from "react"
+import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"
+import { Table, TableBody, TableCell, TableHead, TableHeader, TableRow } from "@/components/ui/table"
+import { Loader2, TrendingUp, TrendingDown, DollarSign } from "lucide-react"
+import { getFinancialData } from "@/lib/api"
+import { formatNumber, formatLargeNumber, formatDate } from "@/lib/formatters"
+import type { FinancialDataResponse } from "@/lib/types"
+
+interface FinancialTablesProps {
+    companyId: number
+    dataSource: string
+}
+
+export function FinancialTables({ companyId, dataSource }: FinancialTablesProps) {
+    const [data, setData] = useState<FinancialDataResponse | null>(null)
+    const [loading, setLoading] = useState(true)
+    const [error, setError] = useState("")
+
+    useEffect(() => {
+        async function loadData() {
+            setLoading(true)
+            setError("")
+            try {
+                const result = await getFinancialData(companyId, dataSource)
+                setData(result)
+            } catch (err: any) {
+                setError(err.message || "加载财务数据失败")
+            } finally {
+                setLoading(false)
+            }
+        }
+
+        loadData()
+    }, [companyId, dataSource])
+
+    if (loading) {
+        return (
+            <Card>
+                <CardContent className="pt-6 flex items-center justify-center min-h-[300px]">
+                    <Loader2 className="h-8 w-8 animate-spin text-muted-foreground" />
+                </CardContent>
+            </Card>
+        )
+    }
+
+    if (error) {
+        return (
+            <Card>
+                <CardContent className="pt-6">
+                    <div className="text-center text-destructive">{error}</div>
+                </CardContent>
+            </Card>
+        )
+    }
+
+    if (!data) {
+        return null
+    }
+
+    const isBloomberg = dataSource === 'Bloomberg'
+    const TableComponent = isBloomberg ? TransposedFinancialTable : FinancialTable
+
+    return (
+        <Card>
+            <CardHeader>
+                <CardTitle className="flex items-center gap-2">
+                    <DollarSign className="h-5 w-5" />
+                    财务数据
+                </CardTitle>
+            </CardHeader>
+            <CardContent className="space-y-8">
+                <div className="space-y-4">
+                    <h3 className="text-lg font-semibold flex items-center gap-2">
+                        <TrendingUp className="h-4 w-4" />
+                        利润表
+                    </h3>
+                    <TableComponent
+                        data={data.income_statement}
+                        title="利润表"
+                    />
+                </div>
+
+                <div className="space-y-4">
+                    <h3 className="text-lg font-semibold flex items-center gap-2">
+                        <TrendingDown className="h-4 w-4" />
+                        资产负债表
+                    </h3>
+                    <TableComponent
+                        data={data.balance_sheet}
+                        title="资产负债表"
+                    />
+                </div>
+
+                <div className="space-y-4">
+                    <h3 className="text-lg font-semibold flex items-center gap-2">
+                        <DollarSign className="h-4 w-4" />
+                        现金流量表
+                    </h3>
+                    <TableComponent
+                        data={data.cash_flow}
+                        title="现金流量表"
+                    />
+                </div>
+
+                <div className="space-y-4">
+                    <h3 className="text-lg font-semibold flex items-center gap-2">
+                        <TrendingUp className="h-4 w-4" />
+                        估值数据
+                    </h3>
+                    <TableComponent
+                        data={data.daily_basic}
+                        title="估值数据"
+                    />
+                </div>
+            </CardContent>
+        </Card>
+    )
+}
+
+interface FinancialTableProps {
+    data: Record<string, any>[]
+    title: string
+    icon?: React.ReactNode
+}
+
+function FinancialTable({ data, title }: FinancialTableProps) {
+    if (!data || data.length === 0) {
+        return (
+            <div className="text-center text-muted-foreground py-8">
+                暂无{title}数据
+            </div>
+        )
+    }
+
+    // 获取所有列名（排除 id, code, ticker, market, update_date 等元数据列）
+    const excludeColumns = ['id', 'code', 'ticker', 'market', 'update_date', 'ts_code']
+    const columns = Object.keys(data[0]).filter(key => !excludeColumns.includes(key))
+
+    return (
+        <div className="rounded-md border">
+            <div className="overflow-x-auto">
+                <Table>
+                    <TableHeader>
+                        <TableRow>
+                            {columns.map((column) => (
+                                <TableHead key={column} className="whitespace-nowrap">
+                                    {formatColumnName(column)}
+                                </TableHead>
+                            ))}
+                        </TableRow>
+                    </TableHeader>
+                    <TableBody>
+                        {data.map((row, idx) => (
+                            <TableRow key={idx}>
+                                {columns.map((column) => (
+                                    <TableCell key={column} className="font-mono text-right">
+                                        {formatCellValue(column, row[column])}
+                                    </TableCell>
+                                ))}
+                            </TableRow>
+                        ))}
+                    </TableBody>
+                </Table>
+            </div>
+            <div className="px-4 py-2 text-xs text-muted-foreground border-t">
+                共 {data.length} 条记录
+            </div>
+        </div>
+    )
+}
+
+function TransposedFinancialTable({ data, title }: FinancialTableProps) {
+    if (!data || data.length === 0) {
+        return (
+            <div className="text-center text-muted-foreground py-8">
+                暂无{title}数据
+            </div>
+        )
+    }
+
+    // 1. 提取所有日期 (Headers)
+    // 假设 data 是按时间倒序排列的 (API通常如此)
+    // 如果不是，建议在这里做个sort
+    const sortedData = [...data].sort((a, b) =>
+        new Date(b.end_date).getTime() - new Date(a.end_date).getTime()
+    )
+    const dates = sortedData.map(row => row['end_date']).filter(Boolean)
+
+    // 2. 提取所有指标 (Rows)
+    const excludeColumns = ['id', 'code', 'ticker', 'market', 'update_date', 'ts_code', 'end_date']
+    // 从第一行获取所有指标key
+    const indicators = Object.keys(sortedData[0]).filter(key => !excludeColumns.includes(key))
+
+    return (
+        <div className="rounded-md border">
+            <div className="overflow-x-auto">
+                <Table>
+                    <TableHeader>
+                        <TableRow>
+                            {/* 第一列：指标名称 */}
+                            <TableHead className="whitespace-nowrap w-[200px] min-w-[150px] sticky left-0 bg-background z-10 border-r shadow-[2px_0_5px_-2px_rgba(0,0,0,0.1)]">
+                                指标 / 报告期
+                            </TableHead>
+                            {/* 其他列：日期 */}
+                            {dates.map((date, idx) => (
+                                <TableHead key={idx} className="whitespace-nowrap text-right px-4 min-w-[120px]">
+                                    {formatDate(date)}
+                                </TableHead>
+                            ))}
+                        </TableRow>
+                    </TableHeader>
+                    <TableBody>
+                        {indicators.map((indicator) => (
+                            <TableRow key={indicator}>
+                                {/* 指标名 */}
+                                <TableCell className="font-medium sticky left-0 bg-background z-10 border-r shadow-[2px_0_5px_-2px_rgba(0,0,0,0.1)]">
+                                    {formatColumnName(indicator)}
+                                </TableCell>
+                                {/* 值 */}
+                                {sortedData.map((row, rowIdx) => (
+                                    <TableCell key={rowIdx} className="font-mono text-right px-4">
+                                        {formatCellValue(indicator, row[indicator])}
+                                    </TableCell>
+                                ))}
+                            </TableRow>
+                        ))}
+                    </TableBody>
+                </Table>
+            </div>
+            <div className="px-4 py-2 text-xs text-muted-foreground border-t">
+                共 {indicators.length} 个指标
+            </div>
+        </div>
+    )
+}
+
+// 格式化列名
+function formatColumnName(column: string): string {
+    const nameMap: Record<string, string> = {
+        'end_date': '报告期',
+        // Common
+        'revenue': '营业收入',
+        'net_income': '净利润',
+        // Balance Sheet
+        'total_assets': '总资产',
+        'total_liabilities': '总负债',
+        'total_equity': '股东权益',
+        'cash': '货币资金',
+        'inventory': '存货',
+        'receivables': '应收账款',
+        'prepayment': '预付款项',
+        'fixed_assets': '固定资产',
+        'long_term_investments': '长期投资',
+        'accounts_payable': '应付账款',
+        'short_term_debt': '短期借款',
+        'long_term_debt': '长期借款',
+        'deferred_revenue': '递延收入',
+        'goodwill': '商誉',
+        // Income
+        'sga_exp': '销售管理费用',
+        'selling_marketing_exp': '销售费用',
+        'ga_exp': '管理费用',
+        'rd_exp': '研发费用',
+        'depreciation': '折旧',
+        'operating_profit': '营业利润',
+        // Cash Flow
+        'ocf': '经营现金流',
+        'capex': '资本支出',
+        'dividends': '分红支付',
+        'fcf': '自由现金流',
+        // Metrics
+        'pe': '市盈率(PE)',
+        'pb': '市净率(PB)',
+        'market_cap': '市值',
+        'price': '最新股价',
+        'roe': 'ROE',
+        'roa': 'ROA',
+    }
+    return nameMap[column] || column
+}
+
+// 格式化单元格值
+function formatCellValue(column: string, value: any): string {
+    if (value === null || value === undefined) {
+        return '-'
+    }
+
+    // 日期列
+    if (column.includes('date')) {
+        return formatDate(value)
+    }
+
+    // 数值列
+    if (typeof value === 'number') {
+        // 比率和百分比（PE、PB等）
+        if (column.includes('ratio') || column.includes('pe') || column.includes('pb') || column.includes('margin') || column.includes('roe') || column.includes('roa') || column.includes('rate')) {
+            return formatNumber(value)
+        }
+        // 大数字（金额、市值等）
+        return formatLargeNumber(value)
+    }
+
+    return String(value)
+}

frontend/src/components/header-portal.tsx

@@ -0,0 +1,20 @@
+"use client"
+
+import { useEffect, useState } from "react"
+import { createPortal } from "react-dom"
+
+export const HeaderPortal = ({ children }: { children: React.ReactNode }) => {
+    const [mounted, setMounted] = useState(false)
+
+    useEffect(() => {
+        setMounted(true)
+        return () => setMounted(false)
+    }, [])
+
+    if (!mounted) return null
+
+    const target = document.getElementById("header-portal-target")
+    if (!target) return null
+
+    return createPortal(children, target)
+}

frontend/src/components/header-search.tsx

@@ -0,0 +1,121 @@
+"use client"
+
+import { useState, useRef, useEffect } from "react"
+import { searchStock } from "@/lib/api"
+import { Input } from "@/components/ui/input"
+import { Button } from "@/components/ui/button"
+import { Search, Loader2 } from "lucide-react"
+import { Badge } from "@/components/ui/badge"
+import type { SearchResult } from "@/lib/types"
+import { useRouter } from "next/navigation"
+
+export function HeaderSearch({ defaultDataSource }: { defaultDataSource?: string }) {
+    const router = useRouter()
+    const [query, setQuery] = useState("")
+    const [results, setResults] = useState<SearchResult[]>([])
+    const [loading, setLoading] = useState(false)
+    const [open, setOpen] = useState(false)
+    const containerRef = useRef<HTMLDivElement>(null)
+
+    // Handle clicks outside to close dropdown
+    useEffect(() => {
+        const handleClickOutside = (event: MouseEvent) => {
+            if (containerRef.current && !containerRef.current.contains(event.target as Node)) {
+                setOpen(false)
+            }
+        }
+        document.addEventListener("mousedown", handleClickOutside)
+        return () => document.removeEventListener("mousedown", handleClickOutside)
+    }, [])
+
+    const handleSearch = async () => {
+        if (!query.trim()) return
+        setLoading(true)
+        setOpen(true)
+        try {
+            // Using default model for light search
+            const data = await searchStock(query, "gemini-2.5-flash")
+            setResults(Array.isArray(data) ? data : [])
+        } catch (err) {
+            console.error("Search failed", err)
+            setResults([])
+        } finally {
+            setLoading(false)
+        }
+    }
+
+    const handleSelect = (company: SearchResult) => {
+        // Use default data source from props if available
+        let source = defaultDataSource || "Bloomberg"
+
+        // If no default provided, keep original fallback logic (though mostly covered by default)
+        if (!defaultDataSource) {
+            if (company.market === 'CN') source = "Tushare"
+            else if (company.market === 'HK') source = "iFinD"
+        }
+
+        // Push to URL
+        const params = new URLSearchParams()
+        params.set('symbol', company.symbol)
+        params.set('market', company.market)
+        params.set('source', source)
+        params.set('name', company.company_name)
+        params.set('t', Date.now().toString())
+
+        router.push(`/?${params.toString()}`)
+
+        // Close and clear
+        setOpen(false)
+        setQuery("")
+    }
+
+    const handleKeyDown = (e: React.KeyboardEvent) => {
+        if (e.key === "Enter") {
+            handleSearch()
+        }
+    }
+
+    return (
+        <div className="relative w-full max-w-md" ref={containerRef}>
+            <div className="relative flex items-center">
+                <Search className="absolute left-2.5 top-1/2 -translate-y-1/2 h-4 w-4 text-muted-foreground" />
+                <Input
+                    className="pl-9 h-9 w-full bg-background/50 border-input/60 focus:bg-background transition-colors"
+                    placeholder="搜索代码或名称..."
+                    value={query}
+                    onChange={(e) => setQuery(e.target.value)}
+                    onKeyDown={handleKeyDown}
+                    onFocus={() => { if (results.length > 0) setOpen(true) }}
+                />
+                {loading && (
+                    <Loader2 className="absolute right-2.5 top-1/2 -translate-y-1/2 h-4 w-4 animate-spin text-muted-foreground" />
+                )}
+            </div>
+
+            {open && results.length > 0 && (
+                <div className="absolute top-full mt-2 left-0 w-full bg-popover text-popover-foreground rounded-md border shadow-md z-[80] overflow-hidden max-h-[400px] overflow-y-auto">
+                    <div className="p-1">
+                        {results.map((result) => (
+                            <button
+                                key={`${result.market}-${result.symbol}`}
+                                className="w-full flex items-center justify-between p-2 hover:bg-accent hover:text-accent-foreground rounded-sm text-sm text-left transition-colors"
+                                onClick={() => handleSelect(result)}
+                            >
+                                <div className="flex flex-col overflow-hidden">
+                                    <span className="font-medium truncate">{result.company_name}</span>
+                                    <span className="text-xs text-muted-foreground font-mono">{result.symbol}</span>
+                                </div>
+                                <Badge variant="secondary" className="shrink-0 ml-2 text-[10px] h-5">{result.market}</Badge>
+                            </button>
+                        ))}
+                    </div>
+                </div>
+            )}
+            {open && !loading && results.length === 0 && query && (
+                <div className="absolute top-full mt-2 left-0 w-full bg-popover text-popover-foreground rounded-md border shadow-md z-[80] p-4 text-sm text-center text-muted-foreground">
+                    未找到结果，请检查输入或丰富内容
+                </div>
+            )}
+        </div>
+    )
+}

frontend/src/components/nav-header.tsx

@@ -2,16 +2,137 @@
 
 import Link from "next/link"
 import { MonitorPlay } from "lucide-react"
+import { useRouter, useSearchParams } from "next/navigation"
+import { HeaderSearch } from "@/components/header-search"
+import { useEffect, useState } from "react"
+import {
+    Select,
+    SelectContent,
+    SelectItem,
+    SelectTrigger,
+    SelectValue,
+} from "@/components/ui/select"
+
+type RecentCompany = {
+    market: string
+    symbol: string
+    company_name: string
+    last_update: string
+}
 
 export function NavHeader() {
+    const router = useRouter()
+    const searchParams = useSearchParams()
+
+    const [dataSource, setDataSource] = useState("Bloomberg")
+    const [companies, setCompanies] = useState<RecentCompany[]>([])
+
+    // Sync state with URL params
+    useEffect(() => {
+        const sourceParam = searchParams.get('source')
+        if (sourceParam && sourceParam !== dataSource) {
+            setDataSource(sourceParam)
+        }
+    }, [searchParams])
+
+    const handleDataSourceChange = (newSource: string) => {
+        setDataSource(newSource)
+
+        // If viewing a company, update URL to trigger reload with new source
+        const currentSymbol = searchParams.get('symbol')
+        if (currentSymbol) {
+            const params = new URLSearchParams(searchParams.toString())
+            params.set('source', newSource)
+            router.push(`/?${params.toString()}`)
+        }
+    }
+
+    // Fetch recent companies
+    useEffect(() => {
+        const fetchRecent = async () => {
+            try {
+                // Use relative path since we are in same domain mainly
+                const res = await fetch(`/api/data/recent?data_source=${dataSource}`)
+                if (res.ok) {
+                    const data = await res.json()
+                    setCompanies(Array.isArray(data) ? data : [])
+                }
+            } catch (err) {
+                console.error("Failed to fetch recent companies:", err)
+            }
+        }
+        fetchRecent()
+    }, [dataSource])
+
+    const handleCompanyChange = (value: string) => {
+        if (!value) return
+        const [symbol, market] = value.split(':')
+        const company = companies.find(c => c.symbol === symbol && c.market === market)
+
+        // Push URL params to trigger page state update
+        // We encode company name as well to display it nicely before full details load
+        const params = new URLSearchParams()
+        params.set('symbol', symbol)
+        params.set('market', market)
+        params.set('source', dataSource)
+        if (company) params.set('name', company.company_name)
+        params.set('t', Date.now().toString()) // Force update
+
+        router.push(`/?${params.toString()}`)
+    }
+
     return (
-        <header className="border-b bg-muted/40">
+        <header className="border-b bg-background/95 backdrop-blur supports-[backdrop-filter]:bg-background/60 sticky top-0 z-[60]">
-            <div className="flex h-14 items-center gap-4 px-6 lg:h-[60px] max-w-7xl mx-auto">
+            <div className="flex h-14 items-center gap-4 px-6 lg:h-[60px] w-full px-8"> {/* Adjusted constraint */}
-                <Link className="flex items-center gap-2 font-semibold" href="/">
+                <Link className="flex items-center gap-2 font-semibold min-w-[140px]" href="/">
                     <MonitorPlay className="h-6 w-6" />
                     <span className="">股票分析 AI</span>
                 </Link>
-                <nav className="ml-auto flex items-center gap-4 sm:gap-6">
+
+                {/* 快速选择栏 */}
+                <div className="hidden md:flex items-center gap-3 ml-6 px-4 border-l h-8">
+                    <span className="text-sm text-muted-foreground whitespace-nowrap font-medium">快速访问:</span>
+
+                    <Select value={dataSource} onValueChange={handleDataSourceChange}>
+                        <SelectTrigger className="w-[120px] h-8 text-xs font-medium">
+                            <SelectValue placeholder="数据源" />
+                        </SelectTrigger>
+                        <SelectContent className="z-[70]">
+                            <SelectItem value="Bloomberg">Bloomberg</SelectItem>
+                            <SelectItem value="iFinD">iFinD</SelectItem>
+                            <SelectItem value="Tushare">Tushare</SelectItem>
+                        </SelectContent>
+                    </Select>
+
+                    <Select onValueChange={handleCompanyChange}>
+                        <SelectTrigger className="w-[240px] h-8 text-xs font-mono">
+                            <SelectValue placeholder="选择最近浏览的代码..." />
+                        </SelectTrigger>
+                        <SelectContent className="z-[70]">
+                            {companies.map((c) => (
+                                <SelectItem key={`${c.symbol}:${c.market}`} value={`${c.symbol}:${c.market}`}>
+                                    <span className="flex items-center gap-2 w-full">
+                                        <span className="font-bold w-[60px] text-left">{c.symbol}</span>
+                                        <span className="truncate flex-1 text-left">{c.company_name}</span>
+
+                                    </span>
+                                </SelectItem>
+                            ))}
+                            {companies.length === 0 && (
+                                <div className="p-2 text-xs text-muted-foreground text-center">该数据源暂无历史记录</div>
+                            )}
+                        </SelectContent>
+                    </Select>
+                </div>
+
+                <div className="w-[300px] ml-6">
+                    <HeaderSearch defaultDataSource={dataSource} />
+                </div>
+
+                {/* Portal Target for Dynamic Content */}
+                <div id="header-portal-target" className="flex-1 flex items-center justify-end px-4 gap-4 min-w-0" />
+
+                <nav className="flex items-center gap-4 sm:gap-6 border-l pl-6">
                     <Link className="text-sm font-medium hover:underline underline-offset-4" href="/">
                         主页
                     </Link>

frontend/src/components/search-stock.tsx

@@ -1,67 +1,130 @@
 "use client"
 
 import { useState, useEffect } from "react"
-import { searchStock, startAnalysis, getConfig } from "@/lib/api"
+import { searchStock, getConfig } from "@/lib/api"
+import { AI_MODELS } from "@/lib/constants"
 import { Card, CardContent, CardHeader, CardTitle, CardDescription } from "@/components/ui/card"
 import { Input } from "@/components/ui/input"
 import { Button } from "@/components/ui/button"
-import { Search, Loader2, Database, Bot } from "lucide-react"
+import { Search, Loader2, Settings2, Database } from "lucide-react"
-import { useRouter } from "next/navigation"
-import {
-    Select,
-    SelectContent,
-    SelectItem,
-    SelectTrigger,
-    SelectValue,
-} from "@/components/ui/select"
-import { Label } from "@/components/ui/label"
 import { Badge } from "@/components/ui/badge"
+import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"
+import type { SearchResult } from "@/lib/types"
 
-export function SearchStock() {
+interface SearchStockProps {
+    onCompanySelect?: (company: SearchResult, dataSource: string) => void
+}
+
+const DATA_SOURCES = {
+    CN: [
+        { value: "Tushare", label: "Tushare (CN)" },
+        { value: "iFinD", label: "iFinD" },
+        { value: "Bloomberg", label: "Bloomberg" }
+    ],
+    GLOBAL: [
+        { value: "iFinD", label: "iFinD" },
+        { value: "Bloomberg", label: "Bloomberg" }
+    ]
+}
+
+function SearchResultItem({ result, onSelect }: { result: SearchResult, onSelect: (r: SearchResult, ds: string) => void }) {
+    // 默认数据源逻辑：CN -> Tushare, 其他 -> iFinD
+    const defaultSource = result.market === "CN" ? "Tushare" : "iFinD"
+    const [source, setSource] = useState(defaultSource)
+
+    // 根据市场获取可用数据源列表
+    const availableSources = result.market === "CN" ? DATA_SOURCES.CN : DATA_SOURCES.GLOBAL
+
+    return (
+        <Card className="hover:bg-accent/50 transition-colors h-full flex flex-col">
+            <CardContent className="p-4 flex flex-col gap-3 h-full">
+                {/* 顶部：公司信息 */}
+                <div className="flex-1">
+                    <div className="flex items-start justify-between gap-2">
+                        <div className="min-w-0">
+                            <h3 className="font-bold text-base truncate" title={result.company_name}>
+                                {result.company_name}
+                            </h3>
+                            <p className="text-sm text-muted-foreground font-mono mt-0.5">
+                                {result.symbol}
+                            </p>
+                        </div>
+                        <Badge variant="secondary" className="shrink-0 text-xs font-mono">
+                            {result.market}
+                        </Badge>
+                    </div>
+                </div>
+
+                {/* 底部：操作区 */}
+                <div className="flex items-center gap-2 pt-2 border-t mt-auto">
+                    <div className="flex-1 min-w-0">
+                        <Select value={source} onValueChange={setSource}>
+                            <SelectTrigger className="h-8 text-xs w-full">
+                                <Database className="w-3 h-3 mr-2 shrink-0" />
+                                <span className="truncate">{availableSources.find(s => s.value === source)?.label}</span>
+                            </SelectTrigger>
+                            <SelectContent portal={false}>
+                                {availableSources.map(ds => (
+                                    <SelectItem key={ds.value} value={ds.value} className="text-xs">
+                                        {ds.label}
+                                    </SelectItem>
+                                ))}
+                            </SelectContent>
+                        </Select>
+                    </div>
+                    <Button
+                        onClick={() => onSelect(result, source)}
+                        size="sm"
+                        className="h-8 px-3 shrink-0"
+                    >
+                        选择
+                    </Button>
+                </div>
+            </CardContent>
+        </Card>
+    )
+}
+
+export function SearchStock({ onCompanySelect }: SearchStockProps = {}) {
     const [query, setQuery] = useState("")
-    const [results, setResults] = useState<{ market: string; symbol: string; company_name: string }[]>([])
+    const [results, setResults] = useState<SearchResult[]>([])
     const [loading, setLoading] = useState(false)
     const [error, setError] = useState("")
-    const [activeIndex, setActiveIndex] = useState<number | null>(null)
+    const [selectedModel, setSelectedModel] = useState<string>("")
 
-    // Global Configuration State
+    // 加载默认模型配置
-    const [selectedModel, setSelectedModel] = useState("gemini-2.0-flash")
-    const [dataSourcePrefs, setDataSourcePrefs] = useState<Record<string, string>>({
-        'CN': 'Tushare',
-        'HK': 'iFinD',
-        'US': 'Alpha Vantage',
-        'JP': 'iFinD',
-        'VN': 'iFinD'
-    })
-
-    // Fetch initial config
     useEffect(() => {
-        const fetchConfig = async () => {
+        const loadConfig = async () => {
             try {
                 const config = await getConfig()
-                if (config.AI_MODEL) {
+                const modelConfig = Array.isArray(config)
-                    setSelectedModel(config.AI_MODEL)
+                    ? config.find((c: any) => c.key === 'AI_MODEL')
+                    : null
+
+                if (modelConfig && modelConfig.value) {
+                    setSelectedModel(modelConfig.value)
+                } else {
+                    // 如果后端没有配置，使用 fallback
+                    setSelectedModel("gemini-2.5-flash")
                 }
-            } catch (e) {
+            } catch (error) {
-                console.error("Failed to load config:", e)
+                console.error("Failed to load default model config:", error)
+                // 加载失败时使用 fallback
+                setSelectedModel("gemini-2.5-flash")
             }
         }
-        fetchConfig()
+        loadConfig()
     }, [])
 
-    const router = useRouter()
-
     const handleSearch = async () => {
         if (!query.trim()) return
+
         setLoading(true)
         setError("")
         setResults([])
-        setActiveIndex(null)
-
         try {
-            const data = await searchStock(query)
+            const data = await searchStock(query, selectedModel)
             setResults(data)
-            // Auto-select the first result if exists? Or keep null? User asked for "click to select". Keeping null is safer.
         } catch (err: any) {
             setError(err.message || "搜索失败")
         } finally {
@@ -69,185 +132,100 @@ export function SearchStock() {
         }
     }
 
-    const handleAnalyze = async (result: { market: string; symbol: string; company_name: string }) => {
+    const handleSelectCompany = (result: SearchResult, dataSource: string) => {
-        setLoading(true)
+        if (onCompanySelect) {
-        try {
+            // 这里传递数据源，而不是模型
-            // Use global model selection
+            onCompanySelect(result, dataSource)
-            const dataSource = dataSourcePrefs[result.market]
-            const report = await startAnalysis(result.market, result.symbol, result.company_name, selectedModel, dataSource)
-            router.push(`/analysis/${report.id}`)
-        } catch (err: any) {
-            setError(err.message || "启动分析失败")
-        } finally {
-            setLoading(false)
         }
     }
 
-    // Dynamic Data Source Options (Mocking availability)
+    // 使用共享常量
-    const dataSourceOptions: Record<string, string[]> = {
+    const availableModels = AI_MODELS
-        'CN': ['Tushare'],
-        'HK': ['iFinD'],
-        'US': ['Alpha Vantage', 'iFinD'],
-        'JP': ['iFinD'],
-        'VN': ['iFinD']
-    }
 
     return (
-        <div className="grid grid-cols-1 lg:grid-cols-3 gap-6 w-full max-w-6xl">
+        <Card className="w-full border-none shadow-none">
-            {/* Left Column: Search & Results */}
+            <CardHeader className="px-0 pt-0">
-            <div className="lg:col-span-2 space-y-6">
+                <CardTitle className="flex items-center gap-2">
-                <Card className="h-full flex flex-col">
+                    <Search className="h-5 w-5" />
-                    <CardHeader>
+                    股票搜索
-                        <CardTitle>开始新的分析</CardTitle>
+                </CardTitle>
-                        <CardDescription>输入公司名称或股票代码开始搜索</CardDescription>
+                <CardDescription>
-                    </CardHeader>
+                    输入公司名称、股票代码或关键词进行搜索
-                    <CardContent className="space-y-4 flex-grow">
+                </CardDescription>
-                        <div className="flex gap-2">
+            </CardHeader>
-                            <Input
+            <CardContent className="space-y-6 px-0">
-                                placeholder="输入公司名称（例如：腾讯）或代码（例如：700）"
+                {/* 搜索框和模型选择 */}
-                                value={query}
+                <div className="flex gap-4 items-start">
-                                onChange={(e) => setQuery(e.target.value)}
+                    <div className="flex-1 flex gap-2">
-                                onKeyDown={(e) => e.key === "Enter" && handleSearch()}
+                        <Input
-                            />
+                            placeholder="例如: 腾讯, AAPL, 00700.HK"
-                            <Button onClick={handleSearch} disabled={loading} size="default" className="px-4">
+                            value={query}
-                                {loading ? <Loader2 className="animate-spin" /> : <Search className="h-4 w-4" />}
+                            onChange={(e) => setQuery(e.target.value)}
-                            </Button>
+                            onKeyDown={(e) => e.key === "Enter" && handleSearch()}
+                            className="flex-1"
+                        />
+                        <Button
+                            onClick={handleSearch}
+                            disabled={loading || !query.trim()}
+                            className="min-w-[100px]">
+                            {loading ? (
+                                <>
+                                    <Loader2 className="mr-2 h-4 w-4 animate-spin" />
+                                    搜索中
+                                </>
+                            ) : (
+                                <>
+                                    <Search className="mr-2 h-4 w-4" />
+                                    搜索
+                                </>
+                            )}
+                        </Button>
+                    </div>
+
+                    {/* 模型选择 - 仍然保留在搜索栏旁边，如果用户确实想在这里覆盖全局设置 */}
+                    <div className="w-[200px]">
+                        <Select value={selectedModel} onValueChange={setSelectedModel}>
+                            <SelectTrigger className="w-full">
+                                <Settings2 className="w-4 h-4 mr-2 text-muted-foreground" />
+                                <SelectValue placeholder="选择AI模型" />
+                            </SelectTrigger>
+                            <SelectContent portal={false}>
+                                {availableModels.map((model) => (
+                                    <SelectItem key={model.value} value={model.value}>
+                                        {model.label}
+                                    </SelectItem>
+                                ))}
+                            </SelectContent>
+                        </Select>
+                        <p className="text-xs text-muted-foreground mt-1 ml-1">
+                            本次分析使用的模型
+                        </p>
+                    </div>
+                </div>
+
+                {error && (
+                    <div className="p-3 text-sm text-red-600 bg-red-50 rounded-md">
+                        {error}
+                    </div>
+                )}
+
+                {results.length > 0 && (
+                    <div className="space-y-2">
+                        <div className="text-sm text-muted-foreground">
+                            找到 {results.length} 个结果：
                         </div>
-
+                        <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 xl:grid-cols-4 gap-4">
-                        {error && <div className="text-red-500 text-sm">{error}</div>}
+                            {results.map((result) => (
-
+                                <SearchResultItem
-                        {results.length > 0 && (
+                                    key={`${result.market}-${result.symbol}`}
-                            <div className="mt-6 space-y-3">
+                                    result={result}
-                                <div className="text-sm font-medium text-muted-foreground flex items-center justify-between">
+                                    onSelect={handleSelectCompany}
-                                    <span>找到 {results.length} 个结果</span>
+                                />
-                                    <span className="text-xs">点击下方卡片运行分析</span>
+                            ))}
-                                </div>
-                                <div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-4">
-                                    {results.map((result, index) => (
-                                        <div
-                                            key={index}
-                                            className={`group relative flex flex-col justify-between p-4 rounded-lg border transition-all cursor-pointer shadow-sm hover:shadow active:scale-95 duration-200 ${activeIndex === index ? 'border-primary bg-primary/5 ring-1 ring-primary' : 'bg-card hover:bg-accent hover:border-primary/50'}`}
-                                            onClick={() => setActiveIndex(index)}
-                                        >
-                                            <div className="space-y-2 mb-4">
-                                                <div className="font-semibold text-base line-clamp-2 leading-tight" title={result.company_name}>
-                                                    {result.company_name}
-                                                </div>
-                                                <div className="flex items-center gap-2 text-xs text-muted-foreground">
-                                                    <Badge variant="secondary" className="px-1.5 py-0 text-[10px] h-5">{result.market}</Badge>
-                                                    <span className="font-mono">{result.symbol}</span>
-                                                </div>
-                                            </div>
-
-                                            <Button
-                                                onClick={(e) => {
-                                                    e.stopPropagation();
-                                                    handleAnalyze(result);
-                                                }}
-                                                disabled={loading}
-                                                size="sm"
-                                                variant={activeIndex === index ? "default" : "secondary"}
-                                                className="w-full mt-auto"
-                                            >
-                                                {loading ? <Loader2 className="animate-spin h-3 w-3 mr-2" /> : null}
-                                                运行分析
-                                            </Button>
-                                        </div>
-                                    ))}
-                                </div>
-                            </div>
-                        )}
-                    </CardContent>
-                </Card>
-            </div>
-
-            {/* Right Column: Configuration */}
-            <div className="lg:col-span-1 space-y-6">
-                <Card className="h-full border-dashed shadow-sm bg-muted/30 flex flex-col">
-                    <CardHeader>
-                        <CardTitle className="flex items-center gap-2 text-base">
-                            <Bot className="h-5 w-5 text-primary" />
-                            分析配置
-                        </CardTitle>
-                    </CardHeader>
-                    <CardContent className="space-y-6">
-                        {/* Section 1: AI Model */}
-                        <div className="space-y-3">
-                            <Label className="text-sm font-medium">选择 AI 模型</Label>
-                            <Select
-                                value={selectedModel}
-                                onValueChange={setSelectedModel}
-                            >
-                                <SelectTrigger className="w-full bg-background">
-                                    <SelectValue placeholder="Select model" />
-                                </SelectTrigger>
-                                <SelectContent>
-                                    <SelectItem value="gemini-2.0-flash">Gemini 2.0 Flash</SelectItem>
-                                    <SelectItem value="gemini-2.5-flash">Gemini 2.5 Flash</SelectItem>
-                                    <SelectItem value="gemini-3-flash-preview">Gemini 3 Flash Preview</SelectItem>
-                                    <SelectItem value="gemini-3-pro-preview">Gemini 3 Pro Preview</SelectItem>
-
-                                    {/* If the current selected model is custom and not in the list above, show it */}
-                                    {selectedModel &&
-                                        !["gemini-2.0-flash", "gemini-2.5-flash", "gemini-3-flash-preview", "gemini-3-pro-preview"].includes(selectedModel) && (
-                                            <SelectItem value={selectedModel}>{selectedModel} (Custom)</SelectItem>
-                                        )}
-                                </SelectContent>
-                            </Select>
-                            <p className="text-xs text-muted-foreground">
-                                选择用于分析财报和生成报告的大语言模型。
-                            </p>
                         </div>
-
+                    </div>
-                        {/* Section 2: Data Sources - Only show when results are available and for relevant markets */}
+                )}
-                        {results.length > 0 && (
+            </CardContent>
-                            <>
+        </Card>
-                                <div className="h-[1px] bg-border w-full my-4" />
-                                <div className="space-y-3">
-                                    <div className="flex items-center gap-2">
-                                        <Database className="h-4 w-4 text-primary" />
-                                        <Label className="text-sm font-medium">数据源偏好</Label>
-                                    </div>
-
-                                    <div className="space-y-4">
-                                        {(activeIndex !== null && results[activeIndex] ? [results[activeIndex].market] : Array.from(new Set(results.map(r => r.market)))).map((market) => (
-                                            <div key={market} className="space-y-2 animate-in fade-in slide-in-from-right-4 duration-300">
-                                                <div className="flex items-center gap-2">
-                                                    <Badge variant="outline" className="h-5 px-1.5 text-[10px] uppercase">{market}</Badge>
-                                                    <span className="text-xs text-muted-foreground">选择数据源:</span>
-                                                </div>
-                                                <div className="grid grid-cols-2 gap-2">
-                                                    {(dataSourceOptions[market] || ['Default']).map((opt) => {
-                                                        const isSelected = dataSourcePrefs[market] === opt;
-                                                        return (
-                                                            <div
-                                                                key={opt}
-                                                                onClick={() => setDataSourcePrefs(prev => ({ ...prev, [market]: opt }))}
-                                                                className={`
-                                                                    cursor-pointer relative flex flex-col items-center justify-center p-2 rounded-md border text-xs font-medium transition-all
-                                                                    ${isSelected
-                                                                        ? 'border-primary bg-primary/10 text-primary ring-1 ring-primary/20'
-                                                                        : 'bg-background hover:bg-accent hover:border-primary/30 text-muted-foreground'}
-                                                                `}
-                                                            >
-                                                                {opt}
-                                                                {isSelected && <div className="absolute top-1 right-1 w-1.5 h-1.5 rounded-full bg-primary" />}
-                                                            </div>
-                                                        )
-                                                    })}
-                                                </div>
-                                            </div>
-                                        ))}
-                                    </div>
-                                    <p className="text-xs text-muted-foreground mt-2">
-                                        根据当前搜索结果的市场，选择首选的数据提供商。
-                                    </p>
-                                </div>
-                            </>
-                        )}
-                    </CardContent>
-                </Card>
-            </div>
-        </div>
     )
 }

frontend/src/components/ui/progress.tsx

@@ -0,0 +1,28 @@
+"use client"
+
+import * as React from "react"
+import * as ProgressPrimitive from "@radix-ui/react-progress"
+
+import { cn } from "@/lib/utils"
+
+const Progress = React.forwardRef<
+    React.ElementRef<typeof ProgressPrimitive.Root>,
+    React.ComponentPropsWithoutRef<typeof ProgressPrimitive.Root>
+>(({ className, value, ...props }, ref) => (
+    <ProgressPrimitive.Root
+        ref={ref}
+        className={cn(
+            "relative h-4 w-full overflow-hidden rounded-full bg-secondary",
+            className
+        )}
+        {...props}
+    >
+        <ProgressPrimitive.Indicator
+            className="h-full w-full flex-1 bg-primary transition-all"
+            style={{ transform: `translateX(-${100 - (value || 0)}%)` }}
+        />
+    </ProgressPrimitive.Root>
+))
+Progress.displayName = ProgressPrimitive.Root.displayName
+
+export { Progress }