README.md

@@ -18,14 +18,14 @@
 
 **TradingAgentsX** 是一個先進的多代理 AI 交易分析系統，模擬真實世界的交易公司運作模式。透過 LangGraph 編排多個專業化的 AI 代理（分析師、研究員、交易員、風險管理者），系統能夠從不同角度分析股票市場，並通過結構化的辯論與協作流程產生高質量的交易決策。
 
-> 💡 **致敬原作**: 本專案基於 [TauricResearch/TradingAgents](https://github.com/TauricResearch/TradingAgents) 進行改進和擴展，加入了完整的 Web 前端介面、RESTful API、Docker 部署支援等功能。感謝原作者的卓越工作和開源貢獻！
+> 💡 **致敬原作**: 本專案基於 [TauricResearch/TradingAgents](https://github.com/TauricResearch/TradingAgents) 進行改進和擴展，加入了完整的 Web 前端介面、RESTful API、Docker 部署支援等功能。並且支援台股上市櫃資料，感謝原作者的卓越工作和開源貢獻！
 
 ### 🎯 核心特色
 
 - 🤖 **多代理協作架構** - 專業化的 AI 代理團隊協同工作
 - 🌐 **多模型靈活支援** - 支援 OpenAI、Anthropic、Gemini、Grok、DeepSeek、Qwen 等多家 LLM 提供商
 - 🔧 **自訂端點配置** - 完整支援自訂 API 端點，可連接任何 OpenAI 兼容的服務
-- 📊 **全方位市場分析** - 整合技術面、基本面、情緒面、新聞面分析
+- 📊 **全方位市場分析** - 完整支援**美股**與**台股**(FinMind)技術面、基本面、情緒面、新聞面分析
 - 🔄 **結構化決策流程** - 透過看漲/看跌辯論機制減少偏見
 - 🧠 **長期記憶系統** - 使用 ChromaDB 向量數據庫儲存歷史決策
 - 🎨 **現代化 Web 介面** - 基於 Next.js 16 的響應式 UI
@@ -228,8 +228,10 @@ TradingAgentsX/
   - 申請網址: https://platform.deepseek.com
 - **Qwen API Key** - Qwen 系列模型
   - 申請網址: https://www.alibabacloud.com
-- **Alpha Vantage API Key** (必需) - 股票基本面資料
+- **Alpha Vantage API Key** (必需) - 美股基本面資料
   - 申請網址: https://www.alphavantage.co/support/#api-key
+- **FinMind API Key** (選填) - 台股上市櫃資料
+  - 申請網址: https://finmindtrade.com/
 
 > 💡 **提示**: 本系統採用 BYOK (Bring Your Own Key) 模式，您可以在前端介面直接輸入 API 金鑰，無需設定環境變數（適合快速測試）。
 
@@ -299,9 +301,12 @@ DEEPSEEK_API_KEY=your-deepseek-key
 # Qwen / Alibaba Cloud (可選)
 DASHSCOPE_API_KEY=your-qwen-key
 
-# Alpha Vantage (強烈建議 - 用於基本面數據)
+# Alpha Vantage (強烈建議 - 用於美股基本面數據)
 ALPHA_VANTAGE_API_KEY=your-alpha-vantage-key
 
+# FinMind (選填 - 用於台股數據)
+FINMIND_API_KEY=your-finmind-key
+
 # ============ 後端服務配置 ============
 BACKEND_HOST=0.0.0.0
 BACKEND_PORT=8000
@@ -492,7 +497,8 @@ docker compose down -v
 
    #### 🔑 API 金鑰配置
 
-   - **Alpha Vantage API Key** (必填): 用於獲取股票基本面數據
+   - **Alpha Vantage API Key** (必填): 用於獲取美股基本面數據
+   - **FinMind API Key** (選填): 用於獲取台股數據
    - 如未在環境變數中配置 LLM API Key，需在此填入
 
 4. **執行分析**
@@ -678,7 +684,8 @@ TradingAgentsX 模擬真實交易公司的組織架構，每個代理都有其
 - yfinance: 即時股價與歷史資料
 - Reddit API: 社群情緒分析
 - RSS Feeds: 財經新聞抓取
-- Alpha Vantage: 詳細財務資料（必需）
+- Alpha Vantage: 美股詳細財務資料（必需）
+- FinMind: 台股上市櫃資料 integration (https://finmindtrade.com)
 
 ---
 

backend/app/api/routes.py

@@ -90,6 +90,7 @@ async def run_analysis(
                 analysis_date=request.analysis_date,
                 analysts=request.analysts,
                 research_depth=request.research_depth,
+                market_type=request.market_type or "us",  # 預設美股
                 deep_think_llm=request.deep_think_llm,
                 quick_think_llm=request.quick_think_llm,
                 openai_api_key=request.openai_api_key or "",  # Pass empty string if None, service handles it
@@ -100,7 +101,8 @@ async def run_analysis(
                 deep_think_api_key=request.deep_think_api_key or "",
                 embedding_base_url=request.embedding_base_url,
                 embedding_api_key=request.embedding_api_key or "",
-                alpha_vantage_api_key=request.alpha_vantage_api_key,
+                alpha_vantage_api_key=request.alpha_vantage_api_key or "",
+                finmind_api_key=request.finmind_api_key or "",
             ))
             
             # Check for errors in result

backend/app/models/schemas.py

@@ -21,6 +21,10 @@ class AnalysisRequest(BaseModel):
         description="List of analysts to include in analysis"
     )
     research_depth: Optional[int] = Field(default=1, ge=1, le=5, description="Research depth (1-5)")
+    market_type: Optional[Literal["us", "twse", "tpex"]] = Field(
+        default="us",
+        description="Market type: 'us' for US stocks, 'twse' for Taiwan TWSE (上市), 'tpex' for Taiwan TPEx/ROTC (上櫃/興櫃)"
+    )
     deep_think_llm: Optional[str] = Field(default="gpt-5-mini", description="Deep thinking LLM model")
     quick_think_llm: Optional[str] = Field(default="gpt-5-mini", description="Quick thinking LLM model")
     
@@ -45,10 +49,15 @@ class AnalysisRequest(BaseModel):
         description="Base URL for Embedding Model"
     )
     embedding_api_key: Optional[str] = Field(None, description="API Key for Embedding Model", min_length=0)
-    alpha_vantage_api_key: str = Field(
+    alpha_vantage_api_key: Optional[str] = Field(
-        ...,
+        None,
-        description="Alpha Vantage API Key (required for fundamental data)",
+        description="Alpha Vantage API Key (optional, for US stock fundamental data)",
-        min_length=1
+        min_length=0
+    )
+    finmind_api_key: Optional[str] = Field(
+        None,
+        description="FinMind API Token (optional, for Taiwan stock data)",
+        min_length=0
     )
 
 

backend/app/services/price_service.py

@@ -2,6 +2,7 @@
 Price data service for loading and processing stock price data
 """
 import polars as pl
+import pandas as pd
 from pathlib import Path
 from typing import List, Dict, Any, Optional
 import logging
@@ -101,13 +102,34 @@ class PriceService:
         end_date = datetime.now()
         start_date = end_date - timedelta(days=365 * 15)  # 15 years of data
         
+        # 處理台股代碼
+        yf_ticker = ticker
+        original_ticker = ticker
+        
+        # 檢查是否為台股代碼（4-6位數字）
+        clean_ticker = ticker.replace(".TW", "").replace(".TWO", "").strip()
+        if clean_ticker.isdigit() and 4 <= len(clean_ticker) <= 6:
+            # 嘗試從傳入的 market_type 判斷（如果有的話）
+            # 否則使用 FinMind API 判斷
+            try:
+                from tradingagents.dataflows.finmind_common import get_yfinance_ticker
+                yf_ticker = get_yfinance_ticker(clean_ticker)
+                logger.info(f"台股代碼 {ticker} 轉換為 Yahoo Finance 格式: {yf_ticker}")
+            except ImportError:
+                # 如果無法導入 FinMind，預設使用 .TW
+                yf_ticker = f"{clean_ticker}.TW"
+                logger.info(f"無法導入 FinMind，預設使用上市後綴: {yf_ticker}")
+            except Exception as e:
+                logger.warning(f"獲取市場類型失敗，嘗試 .TW: {e}")
+                yf_ticker = f"{clean_ticker}.TW"
+        
         for attempt in range(1, max_retries + 1):
             try:
-                logger.info(f"嘗試從 Yahoo Finance 獲取 {ticker} 數據（第 {attempt} 次嘗試）...")
+                logger.info(f"嘗試從 Yahoo Finance 獲取 {yf_ticker} 數據（第 {attempt} 次嘗試）...")
                 
                 # Download data with timeout
                 data = yf.download(
-                    ticker,
+                    yf_ticker,
                     start=start_date.strftime("%Y-%m-%d"),
                     end=end_date.strftime("%Y-%m-%d"),
                     progress=False,
@@ -115,24 +137,83 @@ class PriceService:
                 )
                 
                 if data.empty:
-                    logger.error(f"{ticker} 無可用數據")
+                    # 如果是台股，嘗試另一個後綴
-                    return None
+                    if ".TW" in yf_ticker:
+                        alt_ticker = yf_ticker.replace(".TW", ".TWO")
+                        logger.info(f"嘗試上櫃代碼: {alt_ticker}")
+                        data = yf.download(
+                            alt_ticker,
+                            start=start_date.strftime("%Y-%m-%d"),
+                            end=end_date.strftime("%Y-%m-%d"),
+                            progress=False,
+                            timeout=30
+                        )
+                        if not data.empty:
+                            yf_ticker = alt_ticker
+                    elif ".TWO" in yf_ticker:
+                        alt_ticker = yf_ticker.replace(".TWO", ".TW")
+                        logger.info(f"嘗試上市代碼: {alt_ticker}")
+                        data = yf.download(
+                            alt_ticker,
+                            start=start_date.strftime("%Y-%m-%d"),
+                            end=end_date.strftime("%Y-%m-%d"),
+                            progress=False,
+                            timeout=30
+                        )
+                        if not data.empty:
+                            yf_ticker = alt_ticker
+                    
+                    if data.empty:
+                        logger.error(f"{yf_ticker} 無可用數據")
+                        return None
+                
+                # 處理 yfinance 多索引 DataFrame
+                # yfinance 可能返回多層索引的 DataFrame
+                if isinstance(data.columns, pd.MultiIndex):
+                    # 移除多層索引，只保留第一層
+                    data.columns = data.columns.get_level_values(0)
+                    logger.info("已處理 yfinance 多索引 DataFrame")
                 
                 # Reset index to make Date a column
                 data = data.reset_index()
                 
+                # 確保 Date 欄位名稱正確
+                if 'Date' not in data.columns and 'date' in data.columns:
+                    data = data.rename(columns={'date': 'Date'})
+                elif 'Date' not in data.columns:
+                    # 如果第一個欄位是日期，重命名它
+                    first_col = data.columns[0]
+                    data = data.rename(columns={first_col: 'Date'})
+                
+                # 標準化欄位名稱
+                column_mapping = {
+                    'open': 'Open', 'high': 'High', 'low': 'Low', 
+                    'close': 'Close', 'volume': 'Volume', 'adj close': 'Adj Close'
+                }
+                data = data.rename(columns={k: v for k, v in column_mapping.items() if k in data.columns})
+                
                 # Ensure cache directory exists
                 Path(data_cache_dir).mkdir(parents=True, exist_ok=True)
                 
-                # Save to cache
+                # Save to cache - 使用原始代碼作為檔名（不含後綴）
-                cache_file = Path(data_cache_dir) / f"{ticker}-YFin-data-{start_date.strftime('%Y-%m-%d')}-{end_date.strftime('%Y-%m-%d')}.csv"
+                cache_file = Path(data_cache_dir) / f"{original_ticker}-YFin-data-{start_date.strftime('%Y-%m-%d')}-{end_date.strftime('%Y-%m-%d')}.csv"
                 data.to_csv(cache_file, index=False)
                 
-                logger.info(f"成功獲取並緩存 {ticker} 數據到 {cache_file}")
+                logger.info(f"成功獲取並緩存 {yf_ticker} 數據到 {cache_file}")
                 
                 # Prepare and return DataFrame - convert to polars
                 df = pl.read_csv(str(cache_file))
-                df = df.with_columns(pl.col("Date").str.to_datetime())
+                
+                # 嘗試轉換 Date 欄位
+                try:
+                    df = df.with_columns(pl.col("Date").str.to_datetime())
+                except Exception as date_err:
+                    logger.warning(f"日期轉換失敗: {date_err}，嘗試其他格式")
+                    try:
+                        df = df.with_columns(pl.col("Date").cast(pl.Datetime))
+                    except:
+                        pass
+                
                 return df.sort("Date")
                 
             except Exception as e:
@@ -142,7 +223,7 @@ class PriceService:
                     logger.info(f"將在 {wait_time} 秒後重試...")
                     time_module.sleep(wait_time)
                 else:
-                    logger.error(f"在 {max_retries} 次嘗試後仍無法獲取 {ticker} 數據")
+                    logger.error(f"在 {max_retries} 次嘗試後仍無法獲取 {yf_ticker} 數據")
                     return None
         
         return None

backend/app/services/trading_service.py

@@ -51,6 +51,8 @@ class TradingService:
         embedding_base_url: str = "https://api.openai.com/v1",
         embedding_api_key: Optional[str] = None,
         alpha_vantage_api_key: Optional[str] = None,
+        finmind_api_key: Optional[str] = None,  # 台灣股市資料 API
+        market_type: str = "us",  # 市場類型：us (美股) 或 tw (台股)
         analysts: Optional[List[str]] = None,
         research_depth: int = 1,
         deep_think_llm: str = "gpt-5-mini",
@@ -66,7 +68,9 @@ class TradingService:
             openai_base_url: OpenAI API Base URL (optional, deprecated)
             quick_think_base_url: Base URL for Quick Thinking Model
             deep_think_base_url: Base URL for Deep Thinking Model
-            alpha_vantage_api_key: Alpha Vantage API Key (optional)
+            alpha_vantage_api_key: Alpha Vantage API Key (optional, for US stocks)
+            finmind_api_key: FinMind API Token (optional, for Taiwan stocks)
+            market_type: Market type - 'us' for US stocks, 'tw' for Taiwan stocks
             analysts: List of analyst types to include
             research_depth: Research depth (1-5)
             deep_think_llm: Deep thinking LLM model
@@ -84,12 +88,17 @@ class TradingService:
             import os
             original_openai_key = os.environ.get("OPENAI_API_KEY")
             original_alpha_key = os.environ.get("ALPHA_VANTAGE_API_KEY")
+            original_finmind_key = os.environ.get("FINMIND_API_TOKEN")
             
             try:
                 # Set Alpha Vantage API key if provided
                 if alpha_vantage_api_key:
                     os.environ["ALPHA_VANTAGE_API_KEY"] = alpha_vantage_api_key
                 
+                # Set FinMind API token if provided
+                if finmind_api_key:
+                    os.environ["FINMIND_API_TOKEN"] = finmind_api_key
+                
                 # Set OpenAI API key for dataflows (openai.py reads from env var)
                 if openai_api_key:
                     os.environ["OPENAI_API_KEY"] = openai_api_key
@@ -126,6 +135,36 @@ class TradingService:
                 config["embedding_base_url"] = normalize_base_url(embedding_base_url)
                 config["embedding_api_key"] = embedding_api_key if embedding_api_key else openai_api_key
                 
+                # 根據 market_type 設定資料供應商
+                if market_type in ["twse", "tpex"]:
+                    # 台股（上市/上櫃/興櫃）：使用 FinMind 作為所有資料來源
+                    market_label = "上市" if market_type == "twse" else "上櫃/興櫃"
+                    logger.info(f"Market type: Taiwan stocks ({market_label}) - using FinMind data provider")
+                    config["data_vendors"] = {
+                        "core_stock_apis": "finmind",
+                        "technical_indicators": "finmind",
+                        "fundamental_data": "finmind",
+                        "news_data": "finmind",
+                    }
+                    # 所有工具也使用 finmind
+                    config["tool_vendors"] = {
+                        "get_stock_data": "finmind",
+                        "get_indicators": "finmind",
+                        "get_fundamentals": "finmind",
+                        "get_balance_sheet": "finmind",
+                        "get_cashflow": "finmind",
+                        "get_income_statement": "finmind",
+                        "get_news": "finmind",
+                        "get_global_news": "finmind",
+                        "get_insider_sentiment": "finmind",
+                        "get_insider_transactions": "finmind",
+                    }
+                    # 儲存市場類型供 price_service 使用
+                    config["market_type"] = market_type
+                else:
+                    # 美股：維持原有邏輯（不修改 data_vendors 和 tool_vendors）
+                    logger.info(f"Market type: US stocks - using default data providers")
+                
                 # Initialize TradingAgentsX graph
                 graph = TradingAgentsXGraph(analysts, config=config, debug=True)
                 
@@ -171,7 +210,6 @@ class TradingService:
                 }
                 
             finally:
-                # Clean up environment variables after request
                 # Clean up environment variables after request
                 if original_openai_key is not None:
                     os.environ["OPENAI_API_KEY"] = original_openai_key
@@ -181,8 +219,13 @@ class TradingService:
                     
                 if original_alpha_key is not None:
                     os.environ["ALPHA_VANTAGE_API_KEY"] = original_alpha_key
-                elif "ALPHA_VANTAGE_API_KEY" in os.environ:
+                elif alpha_vantage_api_key and "ALPHA_VANTAGE_API_KEY" in os.environ:
                     del os.environ["ALPHA_VANTAGE_API_KEY"]
+                
+                if original_finmind_key is not None:
+                    os.environ["FINMIND_API_TOKEN"] = original_finmind_key
+                elif finmind_api_key and "FINMIND_API_TOKEN" in os.environ:
+                    del os.environ["FINMIND_API_TOKEN"]
             
         except Exception as e:
             logger.error(f"Analysis failed for {ticker}: {str(e)}", exc_info=True)

frontend/components/AgentFlowDiagram.tsx

@@ -50,7 +50,7 @@ export function AgentFlowDiagram() {
           />
           <DataSourceCard
             icon={<DollarSign className="w-5 h-5" />}
-            name="Alpha Vantage"
+            name="Alpha Vantage / FinMind"
             description="財務數據"
             color="purple"
           />

frontend/components/analysis/AnalysisForm.tsx

@@ -53,6 +53,9 @@ const formSchema = z.object({
   quick_think_llm: z.string().min(1, "請選擇快速思維模型"),
   deep_think_llm: z.string().min(1, "請選擇深層思維模型"),
   
+  // Market type selection: us=美股, twse=上市, tpex=上櫃/興櫃
+  market_type: z.enum(["us", "twse", "tpex"]).default("us"),
+  
   // Custom model names (when "custom" is selected)
   custom_quick_think_model: z.string().optional(),
   custom_deep_think_model: z.string().optional(),
@@ -76,7 +79,8 @@ const formSchema = z.object({
     .optional()
     .or(z.literal("")),
   embedding_api_key: z.string().min(1, "請輸入嵌入模型 API Key"),
-  alpha_vantage_api_key: z.string().min(1, "請輸入 Alpha Vantage API Key"),
+  alpha_vantage_api_key: z.string().optional().or(z.literal("")),  // 選填
+  finmind_api_key: z.string().optional().or(z.literal("")),  // 選填
 });
 
 interface AnalysisFormProps {
@@ -99,6 +103,7 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
       analysis_date: format(new Date(), "yyyy-MM-dd"),
       analysts: ["market", "social", "news", "fundamentals"], // 預設全選
       research_depth: 3, // 預設中等層級
+      market_type: "us", // 預設美股
       quick_think_llm: "gpt-5-mini",
       deep_think_llm: "gpt-5-mini",
       custom_quick_think_model: "",
@@ -110,12 +115,14 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
       embedding_base_url: "https://api.openai.com/v1",
       embedding_api_key: "",
       alpha_vantage_api_key: "",
+      finmind_api_key: "",
     },
   });
 
   // Load API settings from localStorage and update when models change
   const quickThinkLlm = form.watch("quick_think_llm");
   const deepThinkLlm = form.watch("deep_think_llm");
+  const marketType = form.watch("market_type");
   const isQuickThinkCustom = quickThinkLlm === "custom";
   const isDeepThinkCustom = deepThinkLlm === "custom";
   
@@ -141,10 +148,27 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
 
     form.setValue("embedding_base_url", savedSettings.custom_base_url || "https://api.openai.com/v1");
     form.setValue("embedding_api_key", savedSettings.custom_api_key || savedSettings.openai_api_key);
-    form.setValue("alpha_vantage_api_key", savedSettings.alpha_vantage_api_key);
+    form.setValue("alpha_vantage_api_key", savedSettings.alpha_vantage_api_key || "");
+    form.setValue("finmind_api_key", savedSettings.finmind_api_key || "");
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, [quickThinkLlm, deepThinkLlm, isQuickThinkCustom, isDeepThinkCustom]);
 
+  // 當市場類型改變時，更新預設股票代碼和提示
+  useEffect(() => {
+    const currentTicker = form.getValues("ticker");
+    // 只在用戶未修改預設值時才自動切換
+    const isTwStock = marketType === "twse" || marketType === "tpex";
+    const isDefaultUsTicker = currentTicker === "NVDA" || currentTicker === "AAPL";
+    const isDefaultTwTicker = currentTicker === "2330" || currentTicker === "2317" || currentTicker === "6488";
+    
+    if (isTwStock && isDefaultUsTicker) {
+      form.setValue("ticker", marketType === "twse" ? "2330" : "6488");
+    } else if (marketType === "us" && isDefaultTwTicker) {
+      form.setValue("ticker", "NVDA");
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [marketType]);
+
   // 全選/取消全選
   const toggleSelectAll = () => {
     const currentAnalysts = form.getValues("analysts");
@@ -270,8 +294,45 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
                 />
               </div>
 
-              {/* 第一行：股票代碼、分析日期（2列） */}
+              {/* 第一行：市場類型、股票代碼、分析日期（3列） */}
-              <div className="md:col-span-2 grid grid-cols-1 md:grid-cols-2 gap-6">
+              <div className="md:col-span-2 grid grid-cols-1 md:grid-cols-3 gap-6">
+                {/* 市場類型選擇 */}
+                <FormField
+                  control={form.control}
+                  name="market_type"
+                  render={({ field }) => (
+                    <FormItem>
+                      <FormLabel>市場類型</FormLabel>
+                      <Select
+                        onValueChange={field.onChange}
+                        defaultValue={field.value}
+                      >
+                        <FormControl>
+                          <SelectTrigger>
+                            <SelectValue placeholder="選擇市場" />
+                          </SelectTrigger>
+                        </FormControl>
+                        <SelectContent>
+                          <SelectItem value="us" className="py-3 cursor-pointer">
+                            🇺🇸 美股
+                          </SelectItem>
+                          <SelectItem value="twse" className="py-3 cursor-pointer">
+                            🇹🇼 台股上市
+                          </SelectItem>
+                          <SelectItem value="tpex" className="py-3 cursor-pointer">
+                            🇹🇼 台股上櫃/興櫃
+                          </SelectItem>
+                        </SelectContent>
+                      </Select>
+                      <FormDescription>
+                        選擇分析的股票市場
+                      </FormDescription>
+                      <FormMessage />
+                    </FormItem>
+                  )}
+                />
+
+                {/* 股票代碼 */}
                 <FormField
                   control={form.control}
                   name="ticker"
@@ -279,10 +340,21 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
                     <FormItem>
                       <FormLabel>股票代碼</FormLabel>
                       <FormControl>
-                        <Input placeholder="NVDA" {...field} />
+                        <Input 
+                          placeholder={
+                            marketType === "us" ? "NVDA" : 
+                            marketType === "twse" ? "2330" : "6488"
+                          } 
+                          {...field} 
+                        />
                       </FormControl>
                       <FormDescription>
-                        輸入股票代碼（例如：NVDA、AAPL）
+                        {marketType === "us" 
+                          ? "輸入美股代碼（例如：NVDA、AAPL）" 
+                          : marketType === "twse"
+                          ? "輸入上市股票代碼（例如：2330、2317）"
+                          : "輸入上櫃/興櫃股票代碼（例如：6488、5765）"
+                        }
                       </FormDescription>
                       <FormMessage />
                     </FormItem>

frontend/components/settings/ApiSettingsDialog.tsx

@@ -39,9 +39,10 @@ import {
 const formSchema = z.object({
   // Required
   openai_api_key: z.string().min(1, "OpenAI API Key 為必填"),
-  alpha_vantage_api_key: z.string().min(1, "Alpha Vantage API Key 為必填"),
   
   // Optional
+  alpha_vantage_api_key: z.string().optional().or(z.literal("")),  // 美股基本面資料
+  finmind_api_key: z.string().optional().or(z.literal("")),  // 台灣股市資料
   anthropic_api_key: z.string().optional().or(z.literal("")),
   google_api_key: z.string().optional().or(z.literal("")),
   grok_api_key: z.string().optional().or(z.literal("")),
@@ -136,14 +137,43 @@ export function ApiSettingsDialog() {
                   </FormItem>
                 )}
               />
+            </div>
 
-              {/* Alpha Vantage API Key */}
+            {/* Stock Market Data APIs Section */}
+            <div className="space-y-4 border-t pt-4">
+              <h3 className="text-lg font-semibold text-muted-foreground">
+                股市資料 API（依分析市場選擇填寫）
+              </h3>
+
+              {/* FinMind API Key - Taiwan Stocks */}
+              <FormField
+                control={form.control}
+                name="finmind_api_key"
+                render={({ field }) => (
+                  <FormItem>
+                    <FormLabel>FinMind API Token（台股）</FormLabel>
+                    <FormControl>
+                      <Input
+                        type="password"
+                        placeholder="輸入 FinMind Token"
+                        {...field}
+                      />
+                    </FormControl>
+                    <FormDescription>
+                      用於獲取台灣股市資料（在 finmindtrade.com 註冊取得）
+                    </FormDescription>
+                    <FormMessage />
+                  </FormItem>
+                )}
+              />
+
+              {/* Alpha Vantage API Key - US Stocks */}
               <FormField
                 control={form.control}
                 name="alpha_vantage_api_key"
                 render={({ field }) => (
                   <FormItem>
-                    <FormLabel>Alpha Vantage API Key *</FormLabel>
+                    <FormLabel>Alpha Vantage API Key（美股）</FormLabel>
                     <FormControl>
                       <Input
                         type="password"
@@ -152,7 +182,7 @@ export function ApiSettingsDialog() {
                       />
                     </FormControl>
                     <FormDescription>
-                      用於獲取市場基本面數據（必填）
+                      用於獲取美股基本面數據（分析美股時建議填寫）
                     </FormDescription>
                     <FormMessage />
                   </FormItem>
@@ -160,10 +190,10 @@ export function ApiSettingsDialog() {
               />
             </div>
 
-            {/* Optional Section */}
+            {/* Optional LLM Providers Section */}
             <div className="space-y-4 border-t pt-4">
               <h3 className="text-lg font-semibold text-muted-foreground">
-                選填項目（依需求填寫）
+                選填項目（其他 LLM 供應商）
               </h3>
 
               {/* Anthropic API Key */}

frontend/lib/storage.ts

@@ -13,6 +13,7 @@ export interface ApiSettings {
   grok_api_key: string;
   deepseek_api_key: string;
   qwen_api_key: string;
+  finmind_api_key: string;  // 台灣股市資料 API
   
   // Custom endpoint
   custom_base_url: string;
@@ -29,6 +30,7 @@ export const DEFAULT_API_SETTINGS: ApiSettings = {
   grok_api_key: "",
   deepseek_api_key: "",
   qwen_api_key: "",
+  finmind_api_key: "",  // 台灣股市資料 API
   custom_base_url: "",
   custom_api_key: "",
 };

frontend/lib/types.ts

@@ -7,6 +7,7 @@ export interface AnalysisRequest {
   analysis_date: string;
   analysts?: string[];
   research_depth?: number;
+  market_type?: "us" | "twse" | "tpex";  // 市場類型：美股、上市、上櫃/興櫃
   quick_think_llm?: string;
   deep_think_llm?: string;
 
@@ -20,6 +21,7 @@ export interface AnalysisRequest {
   embedding_base_url?: string;
   embedding_api_key?: string;
   alpha_vantage_api_key?: string;
+  finmind_api_key?: string;  // 台灣股市資料 API
 }
 
 export interface PriceData {

frontend/pnpm-lock.yaml

@@ -61,7 +61,7 @@ importers:
         version: 19.2.0
       react-day-picker:
         specifier: ^9.11.3
-        version: 9.11.3(react@19.2.0)
+        version: 9.12.0(react@19.2.0)
       react-dom:
         specifier: 19.2.0
         version: 19.2.0(react@19.2.0)
@@ -89,10 +89,10 @@ importers:
     devDependencies:
       '@tailwindcss/postcss':
         specifier: ^4
-        version: 4.1.17
+        version: 4.1.18
       '@types/node':
         specifier: ^20
-        version: 20.19.25
+        version: 20.19.26
       '@types/react':
         specifier: ^19
         version: 19.2.7
@@ -104,16 +104,16 @@ importers:
         version: 1.0.0
       baseline-browser-mapping:
         specifier: ^2.9.2
-        version: 2.9.2
+        version: 2.9.6
       eslint:
         specifier: ^9
         version: 9.39.1(jiti@2.6.1)
       eslint-config-next:
         specifier: 16.0.3
-        version: 16.0.3(@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+        version: 16.0.3(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
       tailwindcss:
         specifier: ^4
-        version: 4.1.17
+        version: 4.1.18
       tw-animate-css:
         specifier: ^1.4.0
         version: 1.4.0
@@ -897,8 +897,8 @@ packages:
   '@radix-ui/rect@1.1.1':
     resolution: {integrity: sha512-HPwpGIzkl28mWyZqG52jiqDJ12waP11Pa1lGoiyUkIEuMLBP0oeK/C89esbXrxsky5we7dfd8U58nm0SgAWpVw==}
 
-  '@reduxjs/toolkit@2.11.0':
+  '@reduxjs/toolkit@2.11.1':
-    resolution: {integrity: sha512-hBjYg0aaRL1O2Z0IqWhnTLytnjDIxekmRxm1snsHjHaKVmIF1HiImWqsq+PuEbn6zdMlkIj9WofK1vR8jjx+Xw==}
+    resolution: {integrity: sha512-HjhlEREguAyBTGNzRlGNiDHGQ2EjLSPWwdhhpoEqHYy8hWak3Dp6/fU72OfqVsiMb8S6rbfPsWUF24fxpilrVA==}
     peerDependencies:
       react: ^16.9.0 || ^17.0.0 || ^18 || ^19
       react-redux: ^7.2.1 || ^8.1.3 || ^9.0.0
@@ -920,65 +920,65 @@ packages:
   '@swc/helpers@0.5.15':
     resolution: {integrity: sha512-JQ5TuMi45Owi4/BIMAJBoSQoOJu12oOk/gADqlcUL9JEdHB8vyjUSsxqeNXnmXHjYKMi2WcYtezGEEhqUI/E2g==}
 
-  '@tailwindcss/node@4.1.17':
+  '@tailwindcss/node@4.1.18':
-    resolution: {integrity: sha512-csIkHIgLb3JisEFQ0vxr2Y57GUNYh447C8xzwj89U/8fdW8LhProdxvnVH6U8M2Y73QKiTIH+LWbK3V2BBZsAg==}
+    resolution: {integrity: sha512-DoR7U1P7iYhw16qJ49fgXUlry1t4CpXeErJHnQ44JgTSKMaZUdf17cfn5mHchfJ4KRBZRFA/Coo+MUF5+gOaCQ==}
 
-  '@tailwindcss/oxide-android-arm64@4.1.17':
+  '@tailwindcss/oxide-android-arm64@4.1.18':
-    resolution: {integrity: sha512-BMqpkJHgOZ5z78qqiGE6ZIRExyaHyuxjgrJ6eBO5+hfrfGkuya0lYfw8fRHG77gdTjWkNWEEm+qeG2cDMxArLQ==}
+    resolution: {integrity: sha512-dJHz7+Ugr9U/diKJA0W6N/6/cjI+ZTAoxPf9Iz9BFRF2GzEX8IvXxFIi/dZBloVJX/MZGvRuFA9rqwdiIEZQ0Q==}
     engines: {node: '>= 10'}
     cpu: [arm64]
     os: [android]
 
-  '@tailwindcss/oxide-darwin-arm64@4.1.17':
+  '@tailwindcss/oxide-darwin-arm64@4.1.18':
-    resolution: {integrity: sha512-EquyumkQweUBNk1zGEU/wfZo2qkp/nQKRZM8bUYO0J+Lums5+wl2CcG1f9BgAjn/u9pJzdYddHWBiFXJTcxmOg==}
+    resolution: {integrity: sha512-Gc2q4Qhs660bhjyBSKgq6BYvwDz4G+BuyJ5H1xfhmDR3D8HnHCmT/BSkvSL0vQLy/nkMLY20PQ2OoYMO15Jd0A==}
     engines: {node: '>= 10'}
     cpu: [arm64]
     os: [darwin]
 
-  '@tailwindcss/oxide-darwin-x64@4.1.17':
+  '@tailwindcss/oxide-darwin-x64@4.1.18':
-    resolution: {integrity: sha512-gdhEPLzke2Pog8s12oADwYu0IAw04Y2tlmgVzIN0+046ytcgx8uZmCzEg4VcQh+AHKiS7xaL8kGo/QTiNEGRog==}
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     engines: {node: '>= 10'}
     cpu: [x64]
     os: [darwin]
 
-  '@tailwindcss/oxide-freebsd-x64@4.1.17':
+  '@tailwindcss/oxide-freebsd-x64@4.1.18':
-    resolution: {integrity: sha512-hxGS81KskMxML9DXsaXT1H0DyA+ZBIbyG/sSAjWNe2EDl7TkPOBI42GBV3u38itzGUOmFfCzk1iAjDXds8Oh0g==}
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     engines: {node: '>= 10'}
     cpu: [x64]
     os: [freebsd]
 
-  '@tailwindcss/oxide-linux-arm-gnueabihf@4.1.17':
+  '@tailwindcss/oxide-linux-arm-gnueabihf@4.1.18':
-    resolution: {integrity: sha512-k7jWk5E3ldAdw0cNglhjSgv501u7yrMf8oeZ0cElhxU6Y2o7f8yqelOp3fhf7evjIS6ujTI3U8pKUXV2I4iXHQ==}
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     engines: {node: '>= 10'}
     cpu: [arm]
     os: [linux]
 
-  '@tailwindcss/oxide-linux-arm64-gnu@4.1.17':
+  '@tailwindcss/oxide-linux-arm64-gnu@4.1.18':
-    resolution: {integrity: sha512-HVDOm/mxK6+TbARwdW17WrgDYEGzmoYayrCgmLEw7FxTPLcp/glBisuyWkFz/jb7ZfiAXAXUACfyItn+nTgsdQ==}
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     engines: {node: '>= 10'}
     cpu: [arm64]
     os: [linux]
 
-  '@tailwindcss/oxide-linux-arm64-musl@4.1.17':
+  '@tailwindcss/oxide-linux-arm64-musl@4.1.18':
-    resolution: {integrity: sha512-HvZLfGr42i5anKtIeQzxdkw/wPqIbpeZqe7vd3V9vI3RQxe3xU1fLjss0TjyhxWcBaipk7NYwSrwTwK1hJARMg==}
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     engines: {node: '>= 10'}
     cpu: [arm64]
     os: [linux]
 
-  '@tailwindcss/oxide-linux-x64-gnu@4.1.17':
+  '@tailwindcss/oxide-linux-x64-gnu@4.1.18':
-    resolution: {integrity: sha512-M3XZuORCGB7VPOEDH+nzpJ21XPvK5PyjlkSFkFziNHGLc5d6g3di2McAAblmaSUNl8IOmzYwLx9NsE7bplNkwQ==}
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     engines: {node: '>= 10'}
     cpu: [x64]
     os: [linux]
 
-  '@tailwindcss/oxide-linux-x64-musl@4.1.17':
+  '@tailwindcss/oxide-linux-x64-musl@4.1.18':
-    resolution: {integrity: sha512-k7f+pf9eXLEey4pBlw+8dgfJHY4PZ5qOUFDyNf7SI6lHjQ9Zt7+NcscjpwdCEbYi6FI5c2KDTDWyf2iHcCSyyQ==}
+    resolution: {integrity: sha512-bhJ2y2OQNlcRwwgOAGMY0xTFStt4/wyU6pvI6LSuZpRgKQwxTec0/3Scu91O8ir7qCR3AuepQKLU/kX99FouqQ==}
     engines: {node: '>= 10'}
     cpu: [x64]
     os: [linux]
 
-  '@tailwindcss/oxide-wasm32-wasi@4.1.17':
+  '@tailwindcss/oxide-wasm32-wasi@4.1.18':
-    resolution: {integrity: sha512-cEytGqSSoy7zK4JRWiTCx43FsKP/zGr0CsuMawhH67ONlH+T79VteQeJQRO/X7L0juEUA8ZyuYikcRBf0vsxhg==}
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     engines: {node: '>=14.0.0'}
     cpu: [wasm32]
     bundledDependencies:
@@ -989,24 +989,24 @@ packages:
       - '@emnapi/wasi-threads'
       - tslib
 
-  '@tailwindcss/oxide-win32-arm64-msvc@4.1.17':
+  '@tailwindcss/oxide-win32-arm64-msvc@4.1.18':
-    resolution: {integrity: sha512-JU5AHr7gKbZlOGvMdb4722/0aYbU+tN6lv1kONx0JK2cGsh7g148zVWLM0IKR3NeKLv+L90chBVYcJ8uJWbC9A==}
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     engines: {node: '>= 10'}
     cpu: [arm64]
     os: [win32]
 
-  '@tailwindcss/oxide-win32-x64-msvc@4.1.17':
+  '@tailwindcss/oxide-win32-x64-msvc@4.1.18':
-    resolution: {integrity: sha512-SKWM4waLuqx0IH+FMDUw6R66Hu4OuTALFgnleKbqhgGU30DY20NORZMZUKgLRjQXNN2TLzKvh48QXTig4h4bGw==}
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     engines: {node: '>= 10'}
     cpu: [x64]
     os: [win32]
 
-  '@tailwindcss/oxide@4.1.17':
+  '@tailwindcss/oxide@4.1.18':
-    resolution: {integrity: sha512-F0F7d01fmkQhsTjXezGBLdrl1KresJTcI3DB8EkScCldyKp3Msz4hub4uyYaVnk88BAS1g5DQjjF6F5qczheLA==}
+    resolution: {integrity: sha512-EgCR5tTS5bUSKQgzeMClT6iCY3ToqE1y+ZB0AKldj809QXk1Y+3jB0upOYZrn9aGIzPtUsP7sX4QQ4XtjBB95A==}
     engines: {node: '>= 10'}
 
-  '@tailwindcss/postcss@4.1.17':
+  '@tailwindcss/postcss@4.1.18':
-    resolution: {integrity: sha512-+nKl9N9mN5uJ+M7dBOOCzINw94MPstNR/GtIhz1fpZysxL/4a+No64jCBD6CPN+bIHWFx3KWuu8XJRrj/572Dw==}
+    resolution: {integrity: sha512-Ce0GFnzAOuPyfV5SxjXGn0CubwGcuDB0zcdaPuCSzAa/2vII24JTkH+I6jcbXLb1ctjZMZZI6OjDaLPJQL1S0g==}
 
   '@tybys/wasm-util@0.10.1':
     resolution: {integrity: sha512-9tTaPJLSiejZKx+Bmog4uSubteqTvFrVrURwkmHixBo0G4seD0zUxp98E1DzUBJxLQ3NPwXrGKDiVjwx/DpPsg==}
@@ -1062,8 +1062,8 @@ packages:
   '@types/ms@2.1.0':
     resolution: {integrity: sha512-GsCCIZDE/p3i96vtEqx+7dBUGXrc7zeSK3wwPHIaRThS+9OhWIXRqzs4d6k1SVU8g91DrNRWxWUGhp5KXQb2VA==}
 
-  '@types/node@20.19.25':
+  '@types/node@20.19.26':
-    resolution: {integrity: sha512-ZsJzA5thDQMSQO788d7IocwwQbI8B5OPzmqNvpf3NY/+MHDAS759Wo0gd2WQeXYt5AAAQjzcrTVC6SKCuYgoCQ==}
+    resolution: {integrity: sha512-0l6cjgF0XnihUpndDhk+nyD3exio3iKaYROSgvh/qSevPXax3L8p5DBRFjbvalnwatGgHEQn2R88y2fA3g4irg==}
 
   '@types/react-dom@19.2.3':
     resolution: {integrity: sha512-jp2L/eY6fn+KgVVQAOqYItbF0VY/YApe5Mz2F0aykSO8gx31bYCZyvSeYxCHKvzHG5eZjc+zyaS5BrBWya2+kQ==}
@@ -1082,63 +1082,63 @@ packages:
   '@types/use-sync-external-store@0.0.6':
     resolution: {integrity: sha512-zFDAD+tlpf2r4asuHEj0XH6pY6i0g5NeAHPn+15wk3BV6JA69eERFXC1gyGThDkVa1zCyKr5jox1+2LbV/AMLg==}
 
-  '@typescript-eslint/eslint-plugin@8.48.1':
+  '@typescript-eslint/eslint-plugin@8.49.0':
-    resolution: {integrity: sha512-X63hI1bxl5ohelzr0LY5coufyl0LJNthld+abwxpCoo6Gq+hSqhKwci7MUWkXo67mzgUK6YFByhmaHmUcuBJmA==}
+    resolution: {integrity: sha512-JXij0vzIaTtCwu6SxTh8qBc66kmf1xs7pI4UOiMDFVct6q86G0Zs7KRcEoJgY3Cav3x5Tq0MF5jwgpgLqgKG3A==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
-      '@typescript-eslint/parser': ^8.48.1
+      '@typescript-eslint/parser': ^8.49.0
       eslint: ^8.57.0 || ^9.0.0
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/parser@8.48.1':
+  '@typescript-eslint/parser@8.49.0':
-    resolution: {integrity: sha512-PC0PDZfJg8sP7cmKe6L3QIL8GZwU5aRvUFedqSIpw3B+QjRSUZeeITC2M5XKeMXEzL6wccN196iy3JLwKNvDVA==}
+    resolution: {integrity: sha512-N9lBGA9o9aqb1hVMc9hzySbhKibHmB+N3IpoShyV6HyQYRGIhlrO5rQgttypi+yEeKsKI4idxC8Jw6gXKD4THA==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
       eslint: ^8.57.0 || ^9.0.0
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/project-service@8.48.1':
+  '@typescript-eslint/project-service@8.49.0':
-    resolution: {integrity: sha512-HQWSicah4s9z2/HifRPQ6b6R7G+SBx64JlFQpgSSHWPKdvCZX57XCbszg/bapbRsOEv42q5tayTYcEFpACcX1w==}
+    resolution: {integrity: sha512-/wJN0/DKkmRUMXjZUXYZpD1NEQzQAAn9QWfGwo+Ai8gnzqH7tvqS7oNVdTjKqOcPyVIdZdyCMoqN66Ia789e7g==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/scope-manager@8.48.1':
+  '@typescript-eslint/scope-manager@8.49.0':
-    resolution: {integrity: sha512-rj4vWQsytQbLxC5Bf4XwZ0/CKd362DkWMUkviT7DCS057SK64D5lH74sSGzhI6PDD2HCEq02xAP9cX68dYyg1w==}
+    resolution: {integrity: sha512-npgS3zi+/30KSOkXNs0LQXtsg9ekZ8OISAOLGWA/ZOEn0ZH74Ginfl7foziV8DT+D98WfQ5Kopwqb/PZOaIJGg==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
 
-  '@typescript-eslint/tsconfig-utils@8.48.1':
+  '@typescript-eslint/tsconfig-utils@8.49.0':
-    resolution: {integrity: sha512-k0Jhs4CpEffIBm6wPaCXBAD7jxBtrHjrSgtfCjUvPp9AZ78lXKdTR8fxyZO5y4vWNlOvYXRtngSZNSn+H53Jkw==}
+    resolution: {integrity: sha512-8prixNi1/6nawsRYxet4YOhnbW+W9FK/bQPxsGB1D3ZrDzbJ5FXw5XmzxZv82X3B+ZccuSxo/X8q9nQ+mFecWA==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/type-utils@8.48.1':
+  '@typescript-eslint/type-utils@8.49.0':
-    resolution: {integrity: sha512-1jEop81a3LrJQLTf/1VfPQdhIY4PlGDBc/i67EVWObrtvcziysbLN3oReexHOM6N3jyXgCrkBsZpqwH0hiDOQg==}
+    resolution: {integrity: sha512-KTExJfQ+svY8I10P4HdxKzWsvtVnsuCifU5MvXrRwoP2KOlNZ9ADNEWWsQTJgMxLzS5VLQKDjkCT/YzgsnqmZg==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
       eslint: ^8.57.0 || ^9.0.0
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/types@8.48.1':
+  '@typescript-eslint/types@8.49.0':
-    resolution: {integrity: sha512-+fZ3LZNeiELGmimrujsDCT4CRIbq5oXdHe7chLiW8qzqyPMnn1puNstCrMNVAqwcl2FdIxkuJ4tOs/RFDBVc/Q==}
+    resolution: {integrity: sha512-e9k/fneezorUo6WShlQpMxXh8/8wfyc+biu6tnAqA81oWrEic0k21RHzP9uqqpyBBeBKu4T+Bsjy9/b8u7obXQ==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
 
-  '@typescript-eslint/typescript-estree@8.48.1':
+  '@typescript-eslint/typescript-estree@8.49.0':
-    resolution: {integrity: sha512-/9wQ4PqaefTK6POVTjJaYS0bynCgzh6ClJHGSBj06XEHjkfylzB+A3qvyaXnErEZSaxhIo4YdyBgq6j4RysxDg==}
+    resolution: {integrity: sha512-jrLdRuAbPfPIdYNppHJ/D0wN+wwNfJ32YTAm10eJVsFmrVpXQnDWBn8niCSMlWjvml8jsce5E/O+86IQtTbJWA==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/utils@8.48.1':
+  '@typescript-eslint/utils@8.49.0':
-    resolution: {integrity: sha512-fAnhLrDjiVfey5wwFRwrweyRlCmdz5ZxXz2G/4cLn0YDLjTapmN4gcCsTBR1N2rWnZSDeWpYtgLDsJt+FpmcwA==}
+    resolution: {integrity: sha512-N3W7rJw7Rw+z1tRsHZbK395TWSYvufBXumYtEGzypgMUthlg0/hmCImeA8hgO2d2G4pd7ftpxxul2J8OdtdaFA==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
       eslint: ^8.57.0 || ^9.0.0
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/visitor-keys@8.48.1':
+  '@typescript-eslint/visitor-keys@8.49.0':
-    resolution: {integrity: sha512-BmxxndzEWhE4TIEEMBs8lP3MBWN3jFPs/p6gPm/wkv02o41hI6cq9AuSmGAaTTHPtA1FTi2jBre4A9rm5ZmX+Q==}
+    resolution: {integrity: sha512-LlKaciDe3GmZFphXIc79THF/YYBugZ7FS1pO581E/edlVVNbZKDy93evqmrfQ9/Y4uN0vVhX4iuchq26mK/iiA==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
 
   '@ungap/structured-clone@1.3.0':
@@ -1333,8 +1333,8 @@ packages:
   balanced-match@1.0.2:
     resolution: {integrity: sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==}
 
-  baseline-browser-mapping@2.9.2:
+  baseline-browser-mapping@2.9.6:
-    resolution: {integrity: sha512-PxSsosKQjI38iXkmb3d0Y32efqyA0uW4s41u4IVBsLlWLhCiYNpH/AfNOVWRqCQBlD8TFJTz6OUWNd4DFJCnmw==}
+    resolution: {integrity: sha512-v9BVVpOTLB59C9E7aSnmIF8h7qRsFpx+A2nugVMTszEOMcfjlZMsXRm4LF23I3Z9AJxc8ANpIvzbzONoX9VJlg==}
     hasBin: true
 
   brace-expansion@1.1.12:
@@ -1368,8 +1368,8 @@ packages:
     resolution: {integrity: sha512-P8BjAsXvZS+VIDUI11hHCQEv74YT67YUi5JJFNWIqL235sBmjX4+qx9Muvls5ivyNENctx46xQLQ3aTuE7ssaQ==}
     engines: {node: '>=6'}
 
-  caniuse-lite@1.0.30001759:
+  caniuse-lite@1.0.30001760:
-    resolution: {integrity: sha512-Pzfx9fOKoKvevQf8oCXoyNRQ5QyxJj+3O0Rqx2V5oxT61KGx8+n6hV/IUyJeifUci2clnmmKVpvtiqRzgiWjSw==}
+    resolution: {integrity: sha512-7AAMPcueWELt1p3mi13HR/LHH0TJLT11cnwDJEs3xA4+CK/PLKeO9Kl1oru24htkyUKtkGCvAx4ohB0Ttry8Dw==}
 
   ccount@2.0.1:
     resolution: {integrity: sha512-eyrF0jiFpY+3drT6383f1qhkbGsLSifNAjA61IUjZjmLCWjItY6LB9ft9YhoDgwfmclB2zhu51Lc7+95b8NRAg==}
@@ -1552,8 +1552,8 @@ packages:
     resolution: {integrity: sha512-KIN/nDJBQRcXw0MLVhZE9iQHmG68qAVIBg9CqmUYjmQIhgij9U5MFvrqkUL5FbtyyzZuOeOt0zdeRe4UY7ct+A==}
     engines: {node: '>= 0.4'}
 
-  electron-to-chromium@1.5.266:
+  electron-to-chromium@1.5.267:
-    resolution: {integrity: sha512-kgWEglXvkEfMH7rxP5OSZZwnaDWT7J9EoZCujhnpLbfi0bbNtRkgdX2E3gt0Uer11c61qCYktB3hwkAS325sJg==}
+    resolution: {integrity: sha512-0Drusm6MVRXSOJpGbaSVgcQsuB4hEkMpHXaVstcPmhu5LIedxs1xNK/nIxmQIU/RPC0+1/o0AVZfBTkTNJOdUw==}
 
   emoji-regex@9.2.2:
     resolution: {integrity: sha512-L18DaJsXSUk2+42pv8mLs5jJT2hqFkFE4j21wOmgbUqsZ2hL72NsUU785g9RXgo3s0ZNgVl42TiHp3ZtOv/Vyg==}
@@ -1863,9 +1863,6 @@ packages:
   graceful-fs@4.2.11:
     resolution: {integrity: sha512-RbJ5/jmFcNNCcDV5o9eTnBLJ/HszWV0P73bc+Ff4nS/rJj+YaS6IGyiOL0VoBYX+l1Wrl3k63h/KrH+nhJ0XvQ==}
 
-  graphemer@1.4.0:
-    resolution: {integrity: sha512-EtKwoO6kxCL9WO5xipiHTZlSzBm7WLT627TqC/uVRd0HKmq8NXyebnNYxDoBi7wt8eTWrUrKXCOVaFq9x1kgag==}
-
   has-bigints@1.1.0:
     resolution: {integrity: sha512-R3pbpkcIqv2Pm3dUwgjclDRVmWpTJW2DcMzcIhEXEx1oh/CEMObMm3KLmRJOdvhM7o4uQBnwr8pzRK2sJWIqfg==}
     engines: {node: '>= 0.4'}
@@ -2555,8 +2552,8 @@ packages:
   queue-microtask@1.2.3:
     resolution: {integrity: sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A==}
 
-  react-day-picker@9.11.3:
+  react-day-picker@9.12.0:
-    resolution: {integrity: sha512-7lD12UvGbkyXqgzbYIGQTbl+x29B9bAf+k0pP5Dcs1evfpKk6zv4EdH/edNc8NxcmCiTNXr2HIYPrSZ3XvmVBg==}
+    resolution: {integrity: sha512-t8OvG/Zrciso5CQJu5b1A7yzEmebvST+S3pOVQJWxwjjVngyG/CA2htN/D15dLI4uTEuLLkbZyS4YYt480FAtA==}
     engines: {node: '>=18'}
     peerDependencies:
       react: '>=16.8.0'
@@ -2834,8 +2831,8 @@ packages:
   tailwind-merge@3.4.0:
     resolution: {integrity: sha512-uSaO4gnW+b3Y2aWoWfFpX62vn2sR3skfhbjsEnaBI81WD1wBLlHZe5sWf0AqjksNdYTbGBEd0UasQMT3SNV15g==}
 
-  tailwindcss@4.1.17:
+  tailwindcss@4.1.18:
-    resolution: {integrity: sha512-j9Ee2YjuQqYT9bbRTfTZht9W/ytp5H+jJpZKiYdP/bpnXARAuELt9ofP0lPnmHjbga7SNQIxdTAXCmtKVYjN+Q==}
+    resolution: {integrity: sha512-4+Z+0yiYyEtUVCScyfHCxOYP06L5Ne+JiHhY2IjR2KWMIWhJOYZKLSGZaP5HkZ8+bY0cxfzwDE5uOmzFXyIwxw==}
 
   tapable@2.3.0:
     resolution: {integrity: sha512-g9ljZiwki/LfxmQADO3dEY1CbpmXT5Hm2fJ+QaGKwSXUylMybePR7/67YW7jOrrvjEgL1Fmz5kzyAjWVWLlucg==}
@@ -2893,8 +2890,8 @@ packages:
     resolution: {integrity: sha512-3KS2b+kL7fsuk/eJZ7EQdnEmQoaho/r6KUef7hxvltNA5DR8NAUM+8wJMbJyZ4G9/7i3v5zPBIMN5aybAh2/Jg==}
     engines: {node: '>= 0.4'}
 
-  typescript-eslint@8.48.1:
+  typescript-eslint@8.49.0:
-    resolution: {integrity: sha512-FbOKN1fqNoXp1hIl5KYpObVrp0mCn+CLgn479nmu2IsRMrx2vyv74MmsBLVlhg8qVwNFGbXSp8fh1zp8pEoC2A==}
+    resolution: {integrity: sha512-zRSVH1WXD0uXczCXw+nsdjGPUdx4dfrs5VQoHnUWmv1U3oNlAKv4FUNdLDhVUg+gYn+a5hUESqch//Rv5wVhrg==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
       eslint: ^8.57.0 || ^9.0.0
@@ -3782,7 +3779,7 @@ snapshots:
 
   '@radix-ui/rect@1.1.1': {}
 
-  '@reduxjs/toolkit@2.11.0(react-redux@9.2.0(@types/react@19.2.7)(react@19.2.0)(redux@5.0.1))(react@19.2.0)':
+  '@reduxjs/toolkit@2.11.1(react-redux@9.2.0(@types/react@19.2.7)(react@19.2.0)(redux@5.0.1))(react@19.2.0)':
     dependencies:
       '@standard-schema/spec': 1.0.0
       '@standard-schema/utils': 0.3.0
@@ -3804,7 +3801,7 @@ snapshots:
     dependencies:
       tslib: 2.8.1
 
-  '@tailwindcss/node@4.1.17':
+  '@tailwindcss/node@4.1.18':
     dependencies:
       '@jridgewell/remapping': 2.3.5
       enhanced-resolve: 5.18.3
@@ -3812,66 +3809,66 @@ snapshots:
       lightningcss: 1.30.2
       magic-string: 0.30.21
       source-map-js: 1.2.1
-      tailwindcss: 4.1.17
+      tailwindcss: 4.1.18
 
-  '@tailwindcss/oxide-android-arm64@4.1.17':
+  '@tailwindcss/oxide-android-arm64@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-darwin-arm64@4.1.17':
+  '@tailwindcss/oxide-darwin-arm64@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-darwin-x64@4.1.17':
+  '@tailwindcss/oxide-darwin-x64@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-freebsd-x64@4.1.17':
+  '@tailwindcss/oxide-freebsd-x64@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-linux-arm-gnueabihf@4.1.17':
+  '@tailwindcss/oxide-linux-arm-gnueabihf@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-linux-arm64-gnu@4.1.17':
+  '@tailwindcss/oxide-linux-arm64-gnu@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-linux-arm64-musl@4.1.17':
+  '@tailwindcss/oxide-linux-arm64-musl@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-linux-x64-gnu@4.1.17':
+  '@tailwindcss/oxide-linux-x64-gnu@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-linux-x64-musl@4.1.17':
+  '@tailwindcss/oxide-linux-x64-musl@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-wasm32-wasi@4.1.17':
+  '@tailwindcss/oxide-wasm32-wasi@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-win32-arm64-msvc@4.1.17':
+  '@tailwindcss/oxide-win32-arm64-msvc@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide-win32-x64-msvc@4.1.17':
+  '@tailwindcss/oxide-win32-x64-msvc@4.1.18':
     optional: true
 
-  '@tailwindcss/oxide@4.1.17':
+  '@tailwindcss/oxide@4.1.18':
     optionalDependencies:
-      '@tailwindcss/oxide-android-arm64': 4.1.17
+      '@tailwindcss/oxide-android-arm64': 4.1.18
-      '@tailwindcss/oxide-darwin-arm64': 4.1.17
+      '@tailwindcss/oxide-darwin-arm64': 4.1.18
-      '@tailwindcss/oxide-darwin-x64': 4.1.17
+      '@tailwindcss/oxide-darwin-x64': 4.1.18
-      '@tailwindcss/oxide-freebsd-x64': 4.1.17
+      '@tailwindcss/oxide-freebsd-x64': 4.1.18
-      '@tailwindcss/oxide-linux-arm-gnueabihf': 4.1.17
+      '@tailwindcss/oxide-linux-arm-gnueabihf': 4.1.18
-      '@tailwindcss/oxide-linux-arm64-gnu': 4.1.17
+      '@tailwindcss/oxide-linux-arm64-gnu': 4.1.18
-      '@tailwindcss/oxide-linux-arm64-musl': 4.1.17
+      '@tailwindcss/oxide-linux-arm64-musl': 4.1.18
-      '@tailwindcss/oxide-linux-x64-gnu': 4.1.17
+      '@tailwindcss/oxide-linux-x64-gnu': 4.1.18
-      '@tailwindcss/oxide-linux-x64-musl': 4.1.17
+      '@tailwindcss/oxide-linux-x64-musl': 4.1.18
-      '@tailwindcss/oxide-wasm32-wasi': 4.1.17
+      '@tailwindcss/oxide-wasm32-wasi': 4.1.18
-      '@tailwindcss/oxide-win32-arm64-msvc': 4.1.17
+      '@tailwindcss/oxide-win32-arm64-msvc': 4.1.18
-      '@tailwindcss/oxide-win32-x64-msvc': 4.1.17
+      '@tailwindcss/oxide-win32-x64-msvc': 4.1.18
 
-  '@tailwindcss/postcss@4.1.17':
+  '@tailwindcss/postcss@4.1.18':
     dependencies:
       '@alloc/quick-lru': 5.2.0
-      '@tailwindcss/node': 4.1.17
+      '@tailwindcss/node': 4.1.18
-      '@tailwindcss/oxide': 4.1.17
+      '@tailwindcss/oxide': 4.1.18
       postcss: 8.5.6
-      tailwindcss: 4.1.17
+      tailwindcss: 4.1.18
 
   '@tybys/wasm-util@0.10.1':
     dependencies:
@@ -3926,7 +3923,7 @@ snapshots:
 
   '@types/ms@2.1.0': {}
 
-  '@types/node@20.19.25':
+  '@types/node@20.19.26':
     dependencies:
       undici-types: 6.21.0
 
@@ -3944,16 +3941,15 @@ snapshots:
 
   '@types/use-sync-external-store@0.0.6': {}
 
-  '@typescript-eslint/eslint-plugin@8.48.1(@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)':
+  '@typescript-eslint/eslint-plugin@8.49.0(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)':
     dependencies:
       '@eslint-community/regexpp': 4.12.2
-      '@typescript-eslint/parser': 8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      '@typescript-eslint/parser': 8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
-      '@typescript-eslint/scope-manager': 8.48.1
+      '@typescript-eslint/scope-manager': 8.49.0
-      '@typescript-eslint/type-utils': 8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      '@typescript-eslint/type-utils': 8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
-      '@typescript-eslint/utils': 8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      '@typescript-eslint/utils': 8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
-      '@typescript-eslint/visitor-keys': 8.48.1
+      '@typescript-eslint/visitor-keys': 8.49.0
       eslint: 9.39.1(jiti@2.6.1)
-      graphemer: 1.4.0
       ignore: 7.0.5
       natural-compare: 1.4.0
       ts-api-utils: 2.1.0(typescript@5.9.3)
@@ -3961,41 +3957,41 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
-  '@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)':
+  '@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)':
     dependencies:
-      '@typescript-eslint/scope-manager': 8.48.1
+      '@typescript-eslint/scope-manager': 8.49.0
-      '@typescript-eslint/types': 8.48.1
+      '@typescript-eslint/types': 8.49.0
-      '@typescript-eslint/typescript-estree': 8.48.1(typescript@5.9.3)
+      '@typescript-eslint/typescript-estree': 8.49.0(typescript@5.9.3)
-      '@typescript-eslint/visitor-keys': 8.48.1
+      '@typescript-eslint/visitor-keys': 8.49.0
       debug: 4.4.3
       eslint: 9.39.1(jiti@2.6.1)
       typescript: 5.9.3
     transitivePeerDependencies:
       - supports-color
 
-  '@typescript-eslint/project-service@8.48.1(typescript@5.9.3)':
+  '@typescript-eslint/project-service@8.49.0(typescript@5.9.3)':
     dependencies:
-      '@typescript-eslint/tsconfig-utils': 8.48.1(typescript@5.9.3)
+      '@typescript-eslint/tsconfig-utils': 8.49.0(typescript@5.9.3)
-      '@typescript-eslint/types': 8.48.1
+      '@typescript-eslint/types': 8.49.0
       debug: 4.4.3
       typescript: 5.9.3
     transitivePeerDependencies:
       - supports-color
 
-  '@typescript-eslint/scope-manager@8.48.1':
+  '@typescript-eslint/scope-manager@8.49.0':
     dependencies:
-      '@typescript-eslint/types': 8.48.1
+      '@typescript-eslint/types': 8.49.0
-      '@typescript-eslint/visitor-keys': 8.48.1
+      '@typescript-eslint/visitor-keys': 8.49.0
 
-  '@typescript-eslint/tsconfig-utils@8.48.1(typescript@5.9.3)':
+  '@typescript-eslint/tsconfig-utils@8.49.0(typescript@5.9.3)':
     dependencies:
       typescript: 5.9.3
 
-  '@typescript-eslint/type-utils@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)':
+  '@typescript-eslint/type-utils@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)':
     dependencies:
-      '@typescript-eslint/types': 8.48.1
+      '@typescript-eslint/types': 8.49.0
-      '@typescript-eslint/typescript-estree': 8.48.1(typescript@5.9.3)
+      '@typescript-eslint/typescript-estree': 8.49.0(typescript@5.9.3)
-      '@typescript-eslint/utils': 8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      '@typescript-eslint/utils': 8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
       debug: 4.4.3
       eslint: 9.39.1(jiti@2.6.1)
       ts-api-utils: 2.1.0(typescript@5.9.3)
@@ -4003,14 +3999,14 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
-  '@typescript-eslint/types@8.48.1': {}
+  '@typescript-eslint/types@8.49.0': {}
 
-  '@typescript-eslint/typescript-estree@8.48.1(typescript@5.9.3)':
+  '@typescript-eslint/typescript-estree@8.49.0(typescript@5.9.3)':
     dependencies:
-      '@typescript-eslint/project-service': 8.48.1(typescript@5.9.3)
+      '@typescript-eslint/project-service': 8.49.0(typescript@5.9.3)
-      '@typescript-eslint/tsconfig-utils': 8.48.1(typescript@5.9.3)
+      '@typescript-eslint/tsconfig-utils': 8.49.0(typescript@5.9.3)
-      '@typescript-eslint/types': 8.48.1
+      '@typescript-eslint/types': 8.49.0
-      '@typescript-eslint/visitor-keys': 8.48.1
+      '@typescript-eslint/visitor-keys': 8.49.0
       debug: 4.4.3
       minimatch: 9.0.5
       semver: 7.7.3
@@ -4020,20 +4016,20 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
-  '@typescript-eslint/utils@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)':
+  '@typescript-eslint/utils@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)':
     dependencies:
       '@eslint-community/eslint-utils': 4.9.0(eslint@9.39.1(jiti@2.6.1))
-      '@typescript-eslint/scope-manager': 8.48.1
+      '@typescript-eslint/scope-manager': 8.49.0
-      '@typescript-eslint/types': 8.48.1
+      '@typescript-eslint/types': 8.49.0
-      '@typescript-eslint/typescript-estree': 8.48.1(typescript@5.9.3)
+      '@typescript-eslint/typescript-estree': 8.49.0(typescript@5.9.3)
       eslint: 9.39.1(jiti@2.6.1)
       typescript: 5.9.3
     transitivePeerDependencies:
       - supports-color
 
-  '@typescript-eslint/visitor-keys@8.48.1':
+  '@typescript-eslint/visitor-keys@8.49.0':
     dependencies:
-      '@typescript-eslint/types': 8.48.1
+      '@typescript-eslint/types': 8.49.0
       eslint-visitor-keys: 4.2.1
 
   '@ungap/structured-clone@1.3.0': {}
@@ -4219,7 +4215,7 @@ snapshots:
 
   balanced-match@1.0.2: {}
 
-  baseline-browser-mapping@2.9.2: {}
+  baseline-browser-mapping@2.9.6: {}
 
   brace-expansion@1.1.12:
     dependencies:
@@ -4236,9 +4232,9 @@ snapshots:
 
   browserslist@4.28.1:
     dependencies:
-      baseline-browser-mapping: 2.9.2
+      baseline-browser-mapping: 2.9.6
-      caniuse-lite: 1.0.30001759
+      caniuse-lite: 1.0.30001760
-      electron-to-chromium: 1.5.266
+      electron-to-chromium: 1.5.267
       node-releases: 2.0.27
       update-browserslist-db: 1.2.2(browserslist@4.28.1)
 
@@ -4261,7 +4257,7 @@ snapshots:
 
   callsites@3.1.0: {}
 
-  caniuse-lite@1.0.30001759: {}
+  caniuse-lite@1.0.30001760: {}
 
   ccount@2.0.1: {}
 
@@ -4422,7 +4418,7 @@ snapshots:
       es-errors: 1.3.0
       gopd: 1.2.0
 
-  electron-to-chromium@1.5.266: {}
+  electron-to-chromium@1.5.267: {}
 
   emoji-regex@9.2.2: {}
 
@@ -4542,18 +4538,18 @@ snapshots:
 
   escape-string-regexp@5.0.0: {}
 
-  eslint-config-next@16.0.3(@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3):
+  eslint-config-next@16.0.3(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3):
     dependencies:
       '@next/eslint-plugin-next': 16.0.3
       eslint: 9.39.1(jiti@2.6.1)
       eslint-import-resolver-node: 0.3.9
       eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@9.39.1(jiti@2.6.1))
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1))
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1))
       eslint-plugin-jsx-a11y: 6.10.2(eslint@9.39.1(jiti@2.6.1))
       eslint-plugin-react: 7.37.5(eslint@9.39.1(jiti@2.6.1))
       eslint-plugin-react-hooks: 7.0.1(eslint@9.39.1(jiti@2.6.1))
       globals: 16.4.0
-      typescript-eslint: 8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      typescript-eslint: 8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
     optionalDependencies:
       typescript: 5.9.3
     transitivePeerDependencies:
@@ -4581,22 +4577,22 @@ snapshots:
       tinyglobby: 0.2.15
       unrs-resolver: 1.11.1
     optionalDependencies:
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1))
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1))
     transitivePeerDependencies:
       - supports-color
 
-  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1)):
+  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1)):
     dependencies:
       debug: 3.2.7
     optionalDependencies:
-      '@typescript-eslint/parser': 8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      '@typescript-eslint/parser': 8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
       eslint: 9.39.1(jiti@2.6.1)
       eslint-import-resolver-node: 0.3.9
       eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@9.39.1(jiti@2.6.1))
     transitivePeerDependencies:
       - supports-color
 
-  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1)):
+  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1)):
     dependencies:
       '@rtsao/scc': 1.1.0
       array-includes: 3.1.9
@@ -4607,7 +4603,7 @@ snapshots:
       doctrine: 2.1.0
       eslint: 9.39.1(jiti@2.6.1)
       eslint-import-resolver-node: 0.3.9
-      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1))
+      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@9.39.1(jiti@2.6.1))
       hasown: 2.0.2
       is-core-module: 2.16.1
       is-glob: 4.0.3
@@ -4619,7 +4615,7 @@ snapshots:
       string.prototype.trimend: 1.0.9
       tsconfig-paths: 3.15.0
     optionalDependencies:
-      '@typescript-eslint/parser': 8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      '@typescript-eslint/parser': 8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
     transitivePeerDependencies:
       - eslint-import-resolver-typescript
       - eslint-import-resolver-webpack
@@ -4875,8 +4871,6 @@ snapshots:
 
   graceful-fs@4.2.11: {}
 
-  graphemer@1.4.0: {}
-
   has-bigints@1.1.0: {}
 
   has-flag@4.0.0: {}
@@ -5650,7 +5644,7 @@ snapshots:
     dependencies:
       '@next/env': 16.0.7
       '@swc/helpers': 0.5.15
-      caniuse-lite: 1.0.30001759
+      caniuse-lite: 1.0.30001760
       postcss: 8.4.31
       react: 19.2.0
       react-dom: 19.2.0(react@19.2.0)
@@ -5797,7 +5791,7 @@ snapshots:
 
   queue-microtask@1.2.3: {}
 
-  react-day-picker@9.11.3(react@19.2.0):
+  react-day-picker@9.12.0(react@19.2.0):
     dependencies:
       '@date-fns/tz': 1.4.1
       date-fns: 4.1.0
@@ -5873,7 +5867,7 @@ snapshots:
 
   recharts@3.5.1(@types/react@19.2.7)(react-dom@19.2.0(react@19.2.0))(react-is@16.13.1)(react@19.2.0)(redux@5.0.1):
     dependencies:
-      '@reduxjs/toolkit': 2.11.0(react-redux@9.2.0(@types/react@19.2.7)(react@19.2.0)(redux@5.0.1))(react@19.2.0)
+      '@reduxjs/toolkit': 2.11.1(react-redux@9.2.0(@types/react@19.2.7)(react@19.2.0)(redux@5.0.1))(react@19.2.0)
       clsx: 2.1.1
       decimal.js-light: 2.5.1
       es-toolkit: 1.42.0
@@ -6187,7 +6181,7 @@ snapshots:
 
   tailwind-merge@3.4.0: {}
 
-  tailwindcss@4.1.17: {}
+  tailwindcss@4.1.18: {}
 
   tapable@2.3.0: {}
 
@@ -6258,12 +6252,12 @@ snapshots:
       possible-typed-array-names: 1.1.0
       reflect.getprototypeof: 1.0.10
 
-  typescript-eslint@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3):
+  typescript-eslint@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3):
     dependencies:
-      '@typescript-eslint/eslint-plugin': 8.48.1(@typescript-eslint/parser@8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      '@typescript-eslint/eslint-plugin': 8.49.0(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
-      '@typescript-eslint/parser': 8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      '@typescript-eslint/parser': 8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
-      '@typescript-eslint/typescript-estree': 8.48.1(typescript@5.9.3)
+      '@typescript-eslint/typescript-estree': 8.49.0(typescript@5.9.3)
-      '@typescript-eslint/utils': 8.48.1(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
+      '@typescript-eslint/utils': 8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
       eslint: 9.39.1(jiti@2.6.1)
       typescript: 5.9.3
     transitivePeerDependencies:

report/2330_2025-12-12/2330_Aggressive_Debator_2025-12-12.pdf

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tradingagents/dataflows/alpha_vantage_fundamentals.py

@@ -3,21 +3,18 @@ import json
 import os
 
 
-def get_fundamentals(ticker: str, curr_date: str = None, use_toon: bool = None) -> str:
+def get_fundamentals(ticker: str, curr_date: str = None, use_toon: bool = True) -> str:
     """
     使用 Alpha Vantage 檢索給定股票代碼的綜合基本面數據。
 
     Args:
         ticker (str): 公司的股票代碼
         curr_date (str): 您正在交易的當前日期，格式為 yyyy-mm-dd (Alpha Vantage 未使用)
-        use_toon (bool): 是否使用toon格式（減少token消耗）。默認從環境變量讀取
+        use_toon (bool): 是否使用toon格式（減少token消耗）。默認為 True
 
     Returns:
         str: 公司概覽數據，包括財務比率和關鍵指標（JSON或toon格式）
     """
-    # 從環境變量或參數決定是否使用toon
-    if use_toon is None:
-        use_toon = os.getenv("USE_TOON_FORMAT", "true").lower() == "true"
     
     params = {
         "symbol": ticker,
@@ -88,7 +85,7 @@ def get_fundamentals(ticker: str, curr_date: str = None, use_toon: bool = None)
         return response
 
 
-def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None, use_toon: bool = None) -> str:
+def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None, use_toon: bool = True) -> str:
     """
     使用 Alpha Vantage 檢索給定股票代碼的資產負債表數據。
 
@@ -96,14 +93,11 @@ def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = Non
         ticker (str): 公司的股票代碼
         freq (str): 報告頻率：年度/季度 (預設為季度) - Alpha Vantage 未使用
         curr_date (str): 您正在交易的當前日期，格式為 yyyy-mm-dd (Alpha Vantage 未使用)
-        use_toon (bool): 是否使用toon格式（減少token消耗）。默認從環境變量讀取
+        use_toon (bool): 是否使用toon格式。默認為 True
 
     Returns:
         str: 具有標準化欄位的資產負債表數據（JSON或toon格式）
     """
-    # 從環境變量或參數決定是否使用toon
-    if use_toon is None:
-        use_toon = os.getenv("USE_TOON_FORMAT", "true").lower() == "true"
 
     params = {
         "symbol": ticker,
@@ -141,7 +135,7 @@ def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = Non
         return response
 
 
-def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None, use_toon: bool = None) -> str:
+def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None, use_toon: bool = True) -> str:
     """
     使用 Alpha Vantage 檢索給定股票代碼的現金流量表數據。
 
@@ -149,14 +143,11 @@ def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None, us
         ticker (str): 公司的股票代碼
         freq (str): 報告頻率：年度/季度 (預設為季度) - Alpha Vantage 未使用
         curr_date (str): 您正在交易的當前日期，格式為 yyyy-mm-dd (Alpha Vantage 未使用)
-        use_toon (bool): 是否使用toon格式（減少token消耗）。默認從環境變量讀取
+        use_toon (bool): 是否使用toon格式。默認為 True
 
     Returns:
         str: 具有標準化欄位的現金流量表數據（JSON或toon格式）
     """
-    # 從環境變量或參數決定是否使用toon
-    if use_toon is None:
-        use_toon = os.getenv("USE_TOON_FORMAT", "true").lower() == "true"
 
     params = {
         "symbol": ticker,
@@ -194,7 +185,7 @@ def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None, us
         return response
 
 
-def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None, use_toon: bool = None) -> str:
+def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None, use_toon: bool = True) -> str:
     """
     使用 Alpha Vantage 檢索給定股票代碼的損益表數據。
 
@@ -202,14 +193,11 @@ def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str =
         ticker (str): 公司的股票代碼
         freq (str): 報告頻率：年度/季度 (預設為季度) - Alpha Vantage 未使用
         curr_date (str): 您正在交易的當前日期，格式為 yyyy-mm-dd (Alpha Vantage 未使用)
-        use_toon (bool): 是否使用toon格式（減少token消耗）。默認從環境變量讀取
+        use_toon (bool): 是否使用toon格式。默認為 True
 
     Returns:
         str: 具有標準化欄位的損益表數據（JSON或toon格式）
     """
-    # 從環境變量或參數決定是否使用toon
-    if use_toon is None:
-        use_toon = os.getenv("USE_TOON_FORMAT", "true").lower() == "true"
 
     params = {
         "symbol": ticker,

tradingagents/dataflows/alpha_vantage_news.py

@@ -4,7 +4,7 @@ import json
 import os
 
 
-def get_news(ticker, start_date, end_date, use_toon: bool = None) -> dict[str, str] | str:
+def get_news(ticker, start_date, end_date, use_toon: bool = True) -> dict[str, str] | str:
     """
     返回全球主要新聞機構的即時和歷史市場新聞與情緒數據。
 
@@ -14,14 +14,11 @@ def get_news(ticker, start_date, end_date, use_toon: bool = None) -> dict[str, s
         ticker: 新聞文章的股票代碼。
         start_date: 新聞搜索的開始日期。
         end_date: 新聞搜索的結束日期。
-        use_toon (bool): 是否使用toon格式（減少token消耗）。默認從環境變量讀取
+        use_toon (bool): 是否使用toon格式。默認為 True
 
     Returns:
         包含新聞情緒數據的字典或 JSON/Toon 字串。
     """
-    # 從環境變量或參數決定是否使用toon
-    if use_toon is None:
-        use_toon = os.getenv("USE_TOON_FORMAT", "true").lower() == "true"
 
     params = {
         "tickers": ticker,
@@ -97,7 +94,7 @@ def get_news(ticker, start_date, end_date, use_toon: bool = None) -> dict[str, s
         print(f"警告：無法總結新聞數據：{e}")
         return response
 
-def get_insider_transactions(symbol: str) -> dict[str, str] | str:
+def get_insider_transactions(symbol: str, use_toon: bool = True) -> dict[str, str] | str:
     """
     返回主要利益相關者的最新和歷史內部交易。
 
@@ -105,9 +102,10 @@ def get_insider_transactions(symbol: str) -> dict[str, str] | str:
 
     Args:
         symbol: 股票代碼。範例："IBM"。
+        use_toon (bool): 是否使用toon格式。默認為 True
 
     Returns:
-        包含內部交易數據的字典或 JSON 字串。
+        包含內部交易數據的字典或 JSON/Toon 字串。
     """
 
     params = {
@@ -125,7 +123,16 @@ def get_insider_transactions(symbol: str) -> dict[str, str] | str:
             if isinstance(data["data"], list):
                 data["data"] = data["data"][:15]
             
-            return json.dumps(data, ensure_ascii=False, indent=2)
+            # 使用toon格式或JSON格式返回
+            if use_toon:
+                try:
+                    from tradingagents.utils.toon_converter import convert_json_to_toon
+                    return convert_json_to_toon(data)
+                except Exception as e:
+                    print(f"警告：toon轉換失敗：{e}，使用JSON格式")
+                    return json.dumps(data, ensure_ascii=False, indent=2)
+            else:
+                return json.dumps(data, ensure_ascii=False, indent=2)
         
         return response
         

tradingagents/dataflows/findmind.py

@@ -0,0 +1,19 @@
+# -*- coding: utf-8 -*-
+"""
+此檔案為向後相容的別名模組。
+
+正確的模組名稱為 finmind（不是 findmind）。
+請改用：from tradingagents.dataflows import finmind
+"""
+
+# 重新匯出所有 finmind 模組的內容
+from .finmind import *
+
+# 發出棄用警告
+import warnings
+warnings.warn(
+    "模組名稱 'findmind' 已棄用，請改用 'finmind'。"
+    "例如：from tradingagents.dataflows import finmind",
+    DeprecationWarning,
+    stacklevel=2
+)

tradingagents/dataflows/finmind.py

@@ -0,0 +1,214 @@
+# -*- coding: utf-8 -*-
+"""
+FinMind 台灣股市資料整合模組
+
+整合所有 FinMind API 功能的統一入口點。
+
+API 文檔：
+- 基本面：https://finmind.github.io/tutor/TaiwanMarket/Fundamental/
+- 技術面：https://finmind.github.io/tutor/TaiwanMarket/Technical/
+- 籌碼面：https://finmind.github.io/tutor/TaiwanMarket/Chip/
+
+使用方式：
+    from tradingagents.dataflows import finmind
+    
+    # 獲取股價
+    stock_data = finmind.get_stock("2330", "2024-01-01", "2024-12-01")
+    
+    # 獲取基本面
+    fundamentals = finmind.get_fundamentals("2330")
+    
+    # 獲取技術指標
+    indicator = finmind.get_indicator("2330", "per", "2024-12-01", 30)
+
+環境變數設定：
+    export FINMIND_API_TOKEN='your_token_here'
+    
+    可在 https://finmindtrade.com/ 註冊獲取 Token
+"""
+
+# 從各子模組匯入函式
+from .finmind_common import (
+    # 例外類別
+    FinMindError,
+    FinMindRateLimitError,
+    FinMindAuthenticationError,
+    FinMindDataNotFoundError,
+    # 工具函式
+    get_api_token,
+    format_date,
+    get_default_start_date,
+    normalize_stock_id,
+    validate_taiwan_stock_id,
+    # 市場類型判斷
+    get_stock_market_type,
+    get_yfinance_ticker,
+    is_taiwan_stock,
+    # 內部函式（供進階使用）
+    _make_api_request,
+    _filter_by_date_range,
+    # 資料集定義
+    FUNDAMENTAL_DATASETS,
+    TECHNICAL_DATASETS,
+    CHIP_DATASETS,
+    RESTRICTED_DATASETS,
+)
+
+from .finmind_stock import (
+    get_stock,
+    get_stock_info,
+    get_stock_per,
+    get_day_trading,
+)
+
+from .finmind_fundamentals import (
+    get_fundamentals,
+    get_income_statement,
+    get_balance_sheet,
+    get_cashflow,
+    get_month_revenue,
+    get_dividend,
+    get_financial_statements,
+)
+
+from .finmind_indicator import (
+    get_indicator,
+    get_margin_data,
+    get_institutional_data,
+    INDICATOR_DESCRIPTIONS,
+)
+
+from .finmind_news import (
+    get_news,
+    get_global_news,
+    get_insider_sentiment,
+    get_insider_transactions,
+)
+
+
+# 版本資訊
+__version__ = "1.0.0"
+__author__ = "TradingAgentsX"
+
+
+# 便利函式：快速獲取股票完整資訊
+def get_stock_overview(
+    ticker: str,
+    curr_date: str = None
+) -> dict:
+    """
+    獲取股票的完整概覽，包含股價、基本面和籌碼面資訊。
+    
+    Args:
+        ticker: 股票代碼（例如 "2330"）
+        curr_date: 當前日期（預設為今天）
+        
+    Returns:
+        dict: 包含各類資訊的字典
+    """
+    import json
+    from datetime import datetime, timedelta
+    
+    if not curr_date:
+        curr_date = datetime.now().strftime("%Y-%m-%d")
+    
+    start_date = (datetime.strptime(curr_date, "%Y-%m-%d") - timedelta(days=30)).strftime("%Y-%m-%d")
+    
+    overview = {
+        "ticker": ticker,
+        "date": curr_date,
+        "stock_price": None,
+        "fundamentals": None,
+        "per_pbr": None,
+        "margin": None,
+        "institutional": None,
+    }
+    
+    try:
+        # 股價
+        stock_data = get_stock(ticker, start_date, curr_date)
+        if stock_data and "error" not in stock_data.lower():
+            overview["stock_price"] = "已獲取"
+    except Exception:
+        pass
+    
+    try:
+        # 基本面
+        fundamentals_data = get_fundamentals(ticker, curr_date)
+        if fundamentals_data:
+            overview["fundamentals"] = json.loads(fundamentals_data) if isinstance(fundamentals_data, str) else fundamentals_data
+    except Exception:
+        pass
+    
+    try:
+        # PER/PBR
+        per_data = get_stock_per(ticker, start_date, curr_date)
+        if per_data and "error" not in per_data.lower():
+            overview["per_pbr"] = "已獲取"
+    except Exception:
+        pass
+    
+    try:
+        # 融資融券
+        margin_data = get_margin_data(ticker, start_date, curr_date)
+        if margin_data and "error" not in margin_data.lower():
+            overview["margin"] = "已獲取"
+    except Exception:
+        pass
+    
+    try:
+        # 法人買賣超
+        inst_data = get_institutional_data(ticker, start_date, curr_date)
+        if inst_data and "error" not in inst_data.lower():
+            overview["institutional"] = "已獲取"
+    except Exception:
+        pass
+    
+    return overview
+
+
+# 模組說明
+__all__ = [
+    # 例外類別
+    "FinMindError",
+    "FinMindRateLimitError",
+    "FinMindAuthenticationError",
+    "FinMindDataNotFoundError",
+    
+    # 股價相關
+    "get_stock",
+    "get_stock_info",
+    "get_stock_per",
+    "get_day_trading",
+    
+    # 基本面相關
+    "get_fundamentals",
+    "get_income_statement",
+    "get_balance_sheet",
+    "get_cashflow",
+    "get_month_revenue",
+    "get_dividend",
+    "get_financial_statements",
+    
+    # 技術指標/籌碼面
+    "get_indicator",
+    "get_margin_data",
+    "get_institutional_data",
+    
+    # 新聞相關
+    "get_news",
+    "get_global_news",
+    "get_insider_sentiment",
+    "get_insider_transactions",
+    
+    # 工具函式
+    "get_stock_overview",
+    "format_date",
+    "get_default_start_date",
+    "normalize_stock_id",
+    "validate_taiwan_stock_id",
+    # 市場類型判斷
+    "get_stock_market_type",
+    "get_yfinance_ticker",
+    "is_taiwan_stock",
+]

tradingagents/dataflows/finmind_common.py

@@ -0,0 +1,585 @@
+# -*- coding: utf-8 -*-
+"""
+FinMind API 共用工具模組
+用於與 FinMind 台灣股市資料 API 進行互動
+
+API 文檔：https://finmind.github.io/
+
+注意：本模組僅使用公開可用的 API 端點，
+不使用需要 backer/sponsor 會員資格的功能。
+"""
+
+import os
+import requests
+import json
+from datetime import datetime, timedelta
+from typing import Optional, Dict, Any, Union
+from io import StringIO
+
+# ============================================================================
+# 常數定義
+# ============================================================================
+
+API_BASE_URL = "https://api.finmindtrade.com/api/v4/data"
+
+# 可用的公開資料集（不需要 backer/sponsor 會員）
+# 基本面
+FUNDAMENTAL_DATASETS = {
+    "financial_statements": "TaiwanStockFinancialStatements",  # 綜合損益表
+    "balance_sheet": "TaiwanStockBalanceSheet",                # 資產負債表
+    "cashflow": "TaiwanStockCashFlowsStatement",               # 現金流量表
+    "dividend": "TaiwanStockDividend",                         # 股利政策表
+    "dividend_result": "TaiwanStockDividendResult",            # 除權除息結果表
+    "month_revenue": "TaiwanStockMonthRevenue",                # 月營收表
+    "capital_reduction": "TaiwanStockCapitalReductionReferencePrice",  # 減資恢復買賣參考價格
+    "delisting": "TaiwanStockDelisting",                       # 台灣股票下市櫃表
+    "split_price": "TaiwanStockSplitPrice",                    # 台股分割後參考價
+    "par_value_change": "TaiwanStockParValueChange",           # 變更面額恢復買賣參考價格
+}
+
+# 技術面
+TECHNICAL_DATASETS = {
+    "stock_info": "TaiwanStockInfo",                           # 台股總覽
+    "stock_info_warrant": "TaiwanStockInfoWithWarrant",        # 台股總覽（含權證）
+    "trading_date": "TaiwanStockTradingDate",                  # 台股交易日
+    "stock_price": "TaiwanStockPrice",                         # 股價日成交資訊
+    "stock_per": "TaiwanStockPER",                             # PER、PBR 資料
+    "order_book_trade": "TaiwanStockStatisticsOfOrderBookAndTrade",  # 每5秒委託成交統計
+    "indicators_5sec": "TaiwanVariousIndicators5Seconds",      # 台股加權指數
+    "day_trading": "TaiwanStockDayTrading",                    # 當日沖銷交易
+    "total_return_index": "TaiwanStockTotalReturnIndex",       # 加權、櫃買報酬指數
+}
+
+# 籌碼面
+CHIP_DATASETS = {
+    "margin_purchase": "TaiwanStockMarginPurchaseShortSale",   # 個股融資融劵表
+    "margin_total": "TaiwanStockTotalMarginPurchaseShortSale", # 整體市場融資融劵表
+    "institutional": "TaiwanStockInstitutionalInvestorsBuySell",  # 法人買賣表
+    "institutional_total": "TaiwanStockTotalInstitutionalInvestors",  # 市場三大法人買賣表
+    "shareholding": "TaiwanStockShareholding",                 # 外資持股表
+    "securities_lending": "TaiwanStockSecuritiesLending",      # 借券成交明細
+    "short_sale_suspension": "TaiwanStockMarginShortSaleSuspension",  # 暫停融券賣出表
+    "short_sale_balances": "TaiwanDailyShortSaleBalances",     # 信用額度總量管制餘額表
+    "securities_trader_info": "TaiwanSecuritiesTraderInfo",    # 證券商資訊表
+}
+
+# 需要 backer/sponsor 會員的資料集（不使用）
+RESTRICTED_DATASETS = [
+    "TaiwanStockMarketValue",               # 台灣股價市值表
+    "TaiwanStockMarketValueWeight",         # 台股市值比重表
+    "TaiwanStockWeekPrice",                 # 台股週 K 資料表
+    "TaiwanStockMonthPrice",                # 台股月 K 資料表
+    "TaiwanStockPriceAdj",                  # 台灣還原股價資料表
+    "TaiwanStockPriceTick",                 # 台灣股價歷史逐筆資料表
+    "TaiwanStock10Year",                    # 台灣個股十年線資料表
+    "TaiwanStockKBar",                      # 台股分 K 資料表
+    "TaiwanStockEvery5SecondsIndex",        # 每 5 秒指數統計
+    "TaiwanStockHoldingSharesPer",          # 股權持股分級表
+    "TaiwanStockTradingDailyReport",        # 台股分點資料表
+    "TaiwanStockWarrantTradingDailyReport", # 台股權證分點資料表
+    "TaiwanstockGovernmentBankBuySell",     # 台股八大行庫賣賣表
+    "TaiwanTotalExchangeMarginMaintenance", # 台灣大盤融資維持率
+    "TaiwanStockTradingDailyReportSecIdAgg",# 當日卷商分點統計表
+    "TaiwanStockDispositionSecuritiesPeriod",# 公布處置有價證券表
+    "TaiwanStockInfoWithWarrantSummary",    # 台股權證標的對照表
+]
+
+
+# ============================================================================
+# 自定義例外
+# ============================================================================
+
+class FinMindError(Exception):
+    """FinMind API 通用錯誤"""
+    pass
+
+
+class FinMindRateLimitError(FinMindError):
+    """當超過 FinMind API 速率限制時引發的例外"""
+    pass
+
+
+class FinMindAuthenticationError(FinMindError):
+    """當 API Token 無效或缺失時引發的例外"""
+    pass
+
+
+class FinMindDataNotFoundError(FinMindError):
+    """當查詢的資料不存在時引發的例外"""
+    pass
+
+
+# ============================================================================
+# API Token 管理
+# ============================================================================
+
+def get_api_token() -> str:
+    """
+    從環境變數中檢索 FinMind 的 API Token。
+    
+    FinMind 使用 Bearer Token 進行身份驗證。
+    您可以在 https://finmindtrade.com/ 註冊後獲取 Token。
+    
+    Returns:
+        str: API Token
+        
+    Raises:
+        FinMindAuthenticationError: 當環境變數未設定時
+    """
+    token = os.getenv("FINMIND_API_TOKEN")
+    if not token:
+        # 也支援舊的環境變數名稱
+        token = os.getenv("FINMIND_API_KEY")
+    
+    if not token:
+        raise FinMindAuthenticationError(
+            "未設定 FINMIND_API_TOKEN 環境變數。"
+            "請在 https://finmindtrade.com/ 註冊並獲取 Token，"
+            "然後設定環境變數：export FINMIND_API_TOKEN='your_token'"
+        )
+    return token
+
+
+# ============================================================================
+# 日期格式處理
+# ============================================================================
+
+def format_date(date_input: Union[str, datetime]) -> str:
+    """
+    將各種日期格式轉換為 FinMind API 所需的 YYYY-MM-DD 格式。
+    
+    Args:
+        date_input: 日期字串或 datetime 物件
+        
+    Returns:
+        str: 格式化後的日期字串 (YYYY-MM-DD)
+        
+    Raises:
+        ValueError: 當日期格式不支援時
+    """
+    if isinstance(date_input, datetime):
+        return date_input.strftime("%Y-%m-%d")
+    
+    if isinstance(date_input, str):
+        # 如果已經是正確格式，直接返回
+        if len(date_input) == 10 and date_input[4] == '-' and date_input[7] == '-':
+            return date_input
+        
+        # 嘗試解析常見的日期格式
+        formats_to_try = [
+            "%Y-%m-%d",
+            "%Y/%m/%d",
+            "%Y%m%d",
+            "%Y-%m-%d %H:%M:%S",
+            "%Y-%m-%d %H:%M",
+        ]
+        
+        for fmt in formats_to_try:
+            try:
+                dt = datetime.strptime(date_input, fmt)
+                return dt.strftime("%Y-%m-%d")
+            except ValueError:
+                continue
+        
+        raise ValueError(f"不支援的日期格式：{date_input}")
+    
+    raise ValueError(f"日期必須是字串或 datetime 物件，但得到的是 {type(date_input)}")
+
+
+def get_default_start_date(years_back: int = 3) -> str:
+    """
+    獲取預設的開始日期（預設為往前推算指定年數）。
+    
+    Args:
+        years_back: 往前推算的年數
+        
+    Returns:
+        str: 格式化的開始日期
+    """
+    start_date = datetime.now() - timedelta(days=years_back * 365)
+    return format_date(start_date)
+
+
+# ============================================================================
+# 輸出格式化（toon / JSON）
+# ============================================================================
+
+def _convert_to_serializable(obj):
+    """
+    將 numpy/pandas 資料類型轉換為 Python 原生類型，
+    以便 JSON 序列化。
+    """
+    import numpy as np
+    import pandas as pd
+    
+    if isinstance(obj, dict):
+        return {k: _convert_to_serializable(v) for k, v in obj.items()}
+    elif isinstance(obj, list):
+        return [_convert_to_serializable(item) for item in obj]
+    elif hasattr(obj, 'item'):  # numpy scalar types
+        return obj.item()
+    elif hasattr(obj, 'tolist'):  # numpy array
+        return obj.tolist()
+    elif pd.isna(obj):
+        return None
+    else:
+        return obj
+
+
+def format_output(data: dict, use_toon: bool = True) -> str:
+    """
+    格式化輸出資料，強制使用 toon 格式以減少 token 消耗。
+    
+    toon 格式可以大幅減少 token 消耗（通常節省 40-60%）。
+    
+    Args:
+        data: 要輸出的資料字典
+        use_toon: 是否使用 toon 格式。預設為 True（強制使用 toon）
+        
+    Returns:
+        str: 格式化後的字串（JSON 或 toon 格式）
+    """
+    # 先確保資料可序列化
+    serializable_data = _convert_to_serializable(data)
+    
+    if use_toon:
+        try:
+            from tradingagents.utils.toon_converter import convert_json_to_toon
+            return convert_json_to_toon(serializable_data)
+        except Exception as e:
+            print(f"警告：toon 轉換失敗：{e}，使用 JSON 格式")
+            return json.dumps(serializable_data, ensure_ascii=False, indent=2)
+    else:
+        return json.dumps(serializable_data, ensure_ascii=False, indent=2)
+
+
+# ============================================================================
+# API 請求處理
+# ============================================================================
+
+def _make_api_request(
+    dataset: str,
+    data_id: Optional[str] = None,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+    **extra_params
+) -> Dict[str, Any]:
+    """
+    發送 API 請求並處理回應的輔助函式。
+    
+    根據 FinMind 文檔，API 請求格式為：
+    GET https://api.finmindtrade.com/api/v4/data
+    
+    必要參數：
+    - dataset: 資料集名稱
+    
+    選填參數：
+    - data_id: 股票代碼（例如 "2330"）
+    - start_date: 開始日期
+    - end_date: 結束日期
+    
+    Args:
+        dataset: FinMind 資料集名稱
+        data_id: 股票代碼（例如 "2330"）
+        start_date: 開始日期 (YYYY-MM-DD)
+        end_date: 結束日期 (YYYY-MM-DD)
+        **extra_params: 額外的查詢參數
+        
+    Returns:
+        dict: API 回應的 JSON 資料
+        
+    Raises:
+        FinMindRateLimitError: 當超過 API 速率限制時
+        FinMindAuthenticationError: 當 Token 無效時
+        FinMindDataNotFoundError: 當資料不存在時
+        FinMindError: 其他 API 錯誤
+    """
+    # 獲取 Token
+    token = get_api_token()
+    
+    # 建立請求標頭
+    headers = {
+        "Authorization": f"Bearer {token}"
+    }
+    
+    # 建立請求參數
+    params = {
+        "dataset": dataset,
+    }
+    
+    # 添加股票代碼（如果提供）
+    if data_id:
+        params["data_id"] = normalize_stock_id(data_id)
+    
+    # 添加開始日期（如果提供）
+    if start_date:
+        params["start_date"] = format_date(start_date)
+    
+    # 添加結束日期（如果提供）
+    if end_date:
+        params["end_date"] = format_date(end_date)
+    
+    # 添加額外參數
+    params.update(extra_params)
+    
+    try:
+        response = requests.get(
+            API_BASE_URL,
+            headers=headers,
+            params=params,
+            timeout=30
+        )
+        response.raise_for_status()
+        
+        # 解析 JSON 回應
+        data = response.json()
+        
+        # 檢查 API 層級的錯誤
+        if "msg" in data:
+            msg = data["msg"].lower()
+            
+            # 速率限制錯誤
+            if "rate limit" in msg or "too many requests" in msg:
+                raise FinMindRateLimitError(f"超過 FinMind API 速率限制：{data['msg']}")
+            
+            # 認證錯誤
+            if "token" in msg or "authentication" in msg or "unauthorized" in msg:
+                raise FinMindAuthenticationError(f"FinMind API 認證失敗：{data['msg']}")
+            
+            # 資料不存在
+            if "no data" in msg or "not found" in msg:
+                raise FinMindDataNotFoundError(f"查無資料：{data['msg']}")
+            
+            # 其他錯誤
+            if data.get("status") != 200:
+                raise FinMindError(f"FinMind API 錯誤：{data['msg']}")
+        
+        return data
+        
+    except requests.exceptions.Timeout:
+        raise FinMindError("FinMind API 請求超時")
+    except requests.exceptions.RequestException as e:
+        raise FinMindError(f"FinMind API 請求失敗：{str(e)}")
+    except json.JSONDecodeError:
+        raise FinMindError("無法解析 FinMind API 回應")
+
+
+# ============================================================================
+# 資料驗證和處理工具
+# ============================================================================
+
+def validate_taiwan_stock_id(stock_id: str) -> bool:
+    """
+    驗證台灣股票代碼格式。
+    
+    台灣股票代碼通常為 4-6 位數字，
+    ETF 或特殊商品可能包含字母。
+    
+    Args:
+        stock_id: 股票代碼
+        
+    Returns:
+        bool: 是否為有效的股票代碼格式
+    """
+    if not stock_id:
+        return False
+    
+    # 移除空白
+    stock_id = stock_id.strip()
+    
+    # 基本長度檢查
+    if len(stock_id) < 4 or len(stock_id) > 6:
+        return False
+    
+    # 大多數台股代碼為純數字
+    if stock_id.isdigit():
+        return True
+    
+    # 某些 ETF 或特殊商品可能包含字母（如 00878）
+    if stock_id[0].isdigit():
+        return True
+    
+    return False
+
+
+def normalize_stock_id(stock_id: str) -> str:
+    """
+    標準化股票代碼格式。
+    
+    Args:
+        stock_id: 股票代碼
+        
+    Returns:
+        str: 標準化後的股票代碼
+    """
+    # 移除空白和特殊字元
+    normalized = stock_id.strip().upper()
+    
+    # 移除可能的 .TW 或 .TWO 後綴（台股在某些系統的格式）
+    for suffix in [".TW", ".TWO", ".TT"]:
+        if normalized.endswith(suffix):
+            normalized = normalized[:-len(suffix)]
+            break
+    
+    return normalized
+
+
+def _filter_by_date_range(
+    data: list,
+    date_field: str,
+    start_date: str,
+    end_date: str
+) -> list:
+    """
+    按日期範圍過濾資料列表。
+    
+    Args:
+        data: 要過濾的資料列表
+        date_field: 日期欄位名稱
+        start_date: 開始日期
+        end_date: 結束日期
+        
+    Returns:
+        list: 過濾後的資料列表
+    """
+    if not data:
+        return data
+    
+    try:
+        start_dt = datetime.strptime(start_date, "%Y-%m-%d")
+        end_dt = datetime.strptime(end_date, "%Y-%m-%d")
+        
+        filtered = []
+        for item in data:
+            if date_field in item:
+                try:
+                    item_date = datetime.strptime(item[date_field], "%Y-%m-%d")
+                    if start_dt <= item_date <= end_dt:
+                        filtered.append(item)
+                except (ValueError, TypeError):
+                    continue
+        
+        return filtered
+    except Exception:
+        return data
+
+
+# ============================================================================
+# 台股市場類型判斷
+# ============================================================================
+
+# 股票市場類型緩存（避免重複查詢）
+_stock_market_type_cache: Dict[str, str] = {}
+
+
+def get_stock_market_type(stock_id: str) -> str:
+    """
+    獲取台股的市場類型（上市/上櫃/興櫃）。
+    
+    透過 FinMind 的 TaiwanStockInfo API 查詢股票資訊，
+    根據 type 欄位判斷市場類型：
+    - twse: 上市（Yahoo Finance 後綴 .TW）
+    - tpex: 上櫃（Yahoo Finance 後綴 .TWO）
+    - rotc: 興櫃（Yahoo Finance 後綴 .TWO）
+    
+    Args:
+        stock_id: 台灣股票代碼（例如 "2330"）
+        
+    Returns:
+        str: 市場類型代碼 ("twse", "tpex", "rotc") 或 "unknown"
+    """
+    global _stock_market_type_cache
+    
+    stock_id = normalize_stock_id(stock_id)
+    
+    # 檢查緩存
+    if stock_id in _stock_market_type_cache:
+        return _stock_market_type_cache[stock_id]
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockInfo",
+            data_id=stock_id
+        )
+        
+        if "data" in response and response["data"]:
+            for item in response["data"]:
+                if item.get("stock_id") == stock_id:
+                    market_type = item.get("type", "unknown")
+                    _stock_market_type_cache[stock_id] = market_type
+                    return market_type
+        
+        # 如果找不到，預設為上市
+        _stock_market_type_cache[stock_id] = "twse"
+        return "twse"
+        
+    except Exception as e:
+        print(f"警告：無法獲取 {stock_id} 的市場類型：{e}")
+        # 預設為上市
+        return "twse"
+
+
+def get_yfinance_ticker(stock_id: str) -> str:
+    """
+    將台股代碼轉換為 Yahoo Finance 格式。
+    
+    根據市場類型添加適當的後綴：
+    - 上市(twse): 加 .TW
+    - 上櫃(tpex): 加 .TWO
+    - 興櫃(rotc): 加 .TWO
+    
+    Args:
+        stock_id: 台灣股票代碼（例如 "2330"）
+        
+    Returns:
+        str: Yahoo Finance 格式的代碼（例如 "2330.TW"）
+    """
+    stock_id = normalize_stock_id(stock_id)
+    
+    # 檢查是否為台股代碼（數字開頭，4-6位）
+    if not stock_id or not stock_id[0].isdigit():
+        return stock_id  # 非台股，直接返回
+    
+    if len(stock_id) < 4 or len(stock_id) > 6:
+        return stock_id  # 不符合台股格式
+    
+    # 獲取市場類型
+    market_type = get_stock_market_type(stock_id)
+    
+    if market_type == "twse":
+        return f"{stock_id}.TW"
+    elif market_type in ["tpex", "rotc"]:
+        return f"{stock_id}.TWO"
+    else:
+        # 預設使用上市後綴
+        return f"{stock_id}.TW"
+
+
+def is_taiwan_stock(stock_id: str) -> bool:
+    """
+    判斷股票代碼是否為台灣股票。
+    
+    判斷邏輯：
+    - 4-6 位數字開頭
+    - 或已有 .TW/.TWO 後綴
+    
+    Args:
+        stock_id: 股票代碼
+        
+    Returns:
+        bool: 是否為台灣股票
+    """
+    if not stock_id:
+        return False
+    
+    stock_id = stock_id.strip().upper()
+    
+    # 檢查是否有台股後綴
+    if stock_id.endswith(".TW") or stock_id.endswith(".TWO"):
+        return True
+    
+    # 檢查是否為純數字或數字開頭的 4-6 位代碼
+    if len(stock_id) >= 4 and len(stock_id) <= 6 and stock_id[0].isdigit():
+        return True
+    
+    return False

tradingagents/dataflows/finmind_fundamentals.py

@@ -0,0 +1,626 @@
+# -*- coding: utf-8 -*-
+"""
+FinMind 基本面資料模組
+用於獲取台灣股市基本面財務資料
+
+API 文檔：https://finmind.github.io/tutor/TaiwanMarket/Fundamental/
+
+可用的資料集：
+- TaiwanStockFinancialStatements: 綜合損益表（1990-03-01 ~ now）
+- TaiwanStockBalanceSheet: 資產負債表（2011-12-01 ~ now）
+- TaiwanStockCashFlowsStatement: 現金流量表（2008-06-01 ~ now）
+- TaiwanStockDividend: 股利政策表（2005-05-01 ~ now）
+- TaiwanStockDividendResult: 除權除息結果表（2003-05-01 ~ now）
+- TaiwanStockMonthRevenue: 月營收表（2002-02-01 ~ now）
+
+注意：本模組僅使用公開可用的 API 端點，
+不使用需要 backer/sponsor 會員資格的功能（如 TaiwanStockMarketValue）。
+"""
+
+import json
+import os
+from datetime import datetime, timedelta
+from typing import Optional, Dict, Any, List
+import pandas as pd
+import numpy as np
+
+from .finmind_common import (
+    _make_api_request,
+    format_date,
+    get_default_start_date,
+    normalize_stock_id,
+    FinMindError,
+    FinMindDataNotFoundError,
+)
+
+
+def _convert_to_serializable(obj):
+    """
+    將 numpy/pandas 資料類型轉換為 Python 原生類型，
+    以便 JSON 序列化。
+    """
+    if isinstance(obj, dict):
+        return {k: _convert_to_serializable(v) for k, v in obj.items()}
+    elif isinstance(obj, list):
+        return [_convert_to_serializable(item) for item in obj]
+    elif isinstance(obj, (np.integer, np.int64)):
+        return int(obj)
+    elif isinstance(obj, (np.floating, np.float64)):
+        return float(obj)
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    elif pd.isna(obj):
+        return None
+    else:
+        return obj
+
+
+def _format_output(data: dict, use_toon: bool = True) -> str:
+    """
+    格式化輸出資料，支援 JSON 或 toon 格式。
+    
+    Args:
+        data: 要輸出的資料字典
+        use_toon: 是否使用 toon 格式。預設為 True（強制使用 toon）
+        
+    Returns:
+        str: 格式化後的字串（JSON 或 toon 格式）
+    """
+    # 先確保資料可序列化
+    serializable_data = _convert_to_serializable(data)
+    
+    if use_toon:
+        try:
+            from tradingagents.utils.toon_converter import convert_json_to_toon
+            return convert_json_to_toon(serializable_data)
+        except Exception as e:
+            print(f"警告：toon 轉換失敗：{e}，使用 JSON 格式")
+            return json.dumps(serializable_data, ensure_ascii=False, indent=2)
+    else:
+        return json.dumps(serializable_data, ensure_ascii=False, indent=2)
+
+
+def get_income_statement(
+    ticker: str,
+    freq: str = "quarterly",
+    curr_date: Optional[str] = None,
+    use_toon: bool = True
+) -> str:
+    """
+    獲取綜合損益表（TaiwanStockFinancialStatements）。
+    
+    資料區間：1990-03-01 ~ now
+    
+    返回欄位：
+    - date: 財報日期
+    - stock_id: 股票代碼
+    - type: 科目類型
+    - value: 數值
+    - origin_name: 原始科目名稱
+    
+    Args:
+        ticker: 股票代碼（例如 "2330"）
+        freq: 報告頻率（quarterly/annual）- 此參數保留但 FinMind 統一返回所有資料
+        curr_date: 當前日期，用於計算查詢範圍
+        use_toon: 是否使用 toon 格式（保留但未實作）
+        
+    Returns:
+        str: JSON 格式的損益表資料
+    """
+    ticker = normalize_stock_id(ticker)
+    
+    # 計算日期範圍（往前推 3 年）
+    if curr_date:
+        end_date = format_date(curr_date)
+        start_dt = datetime.strptime(end_date, "%Y-%m-%d") - timedelta(days=3*365)
+        start_date = format_date(start_dt)
+    else:
+        start_date = get_default_start_date(years_back=3)
+        end_date = format_date(datetime.now())
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockFinancialStatements",
+            data_id=ticker,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            df = pd.DataFrame(response["data"])
+            
+            # 只保留最近 2 期的資料以減少 token
+            unique_dates = df["date"].unique()
+            if len(unique_dates) > 2:
+                recent_dates = sorted(unique_dates, reverse=True)[:2]
+                df = df[df["date"].isin(recent_dates)]
+            
+            # 轉換為 pivot 格式（更易閱讀）
+            try:
+                pivot_df = df.pivot_table(
+                    index="date",
+                    columns="type",
+                    values="value",
+                    aggfunc="first"
+                ).reset_index()
+                
+                result = {
+                    "stock_id": ticker,
+                    "report_type": "income_statement",
+                    "data": pivot_df.to_dict(orient="records")
+                }
+            except Exception:
+                # 如果 pivot 失敗，返回原始格式
+                result = {
+                    "stock_id": ticker,
+                    "report_type": "income_statement",
+                    "data": df.to_dict(orient="records")
+                }
+            
+            return _format_output(result, use_toon)
+        else:
+            return _format_output({
+                "stock_id": ticker,
+                "report_type": "income_statement",
+                "data": [],
+                "message": "查無資料"
+            }, use_toon)
+            
+    except FinMindDataNotFoundError:
+        return _format_output({
+            "stock_id": ticker,
+            "report_type": "income_statement",
+            "data": [],
+            "message": f"找不到股票 {ticker} 的損益表資料"
+        }, use_toon)
+    except FinMindError as e:
+        return _format_output({
+            "error": str(e),
+            "stock_id": ticker
+        }, use_toon)
+
+
+def get_balance_sheet(
+    ticker: str,
+    freq: str = "quarterly",
+    curr_date: Optional[str] = None,
+    use_toon: bool = True
+) -> str:
+    """
+    獲取資產負債表（TaiwanStockBalanceSheet）。
+    
+    資料區間：2011-12-01 ~ now
+    
+    返回欄位：
+    - date: 財報日期
+    - stock_id: 股票代碼
+    - type: 科目類型
+    - value: 數值
+    - origin_name: 原始科目名稱
+    
+    Args:
+        ticker: 股票代碼（例如 "2330"）
+        freq: 報告頻率（quarterly/annual）
+        curr_date: 當前日期
+        use_toon: 是否使用 toon 格式
+        
+    Returns:
+        str: JSON 格式的資產負債表資料
+    """
+    ticker = normalize_stock_id(ticker)
+    
+    if curr_date:
+        end_date = format_date(curr_date)
+        start_dt = datetime.strptime(end_date, "%Y-%m-%d") - timedelta(days=3*365)
+        start_date = format_date(start_dt)
+    else:
+        start_date = get_default_start_date(years_back=3)
+        end_date = format_date(datetime.now())
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockBalanceSheet",
+            data_id=ticker,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            df = pd.DataFrame(response["data"])
+            
+            # 只保留最近 2 期的資料
+            unique_dates = df["date"].unique()
+            if len(unique_dates) > 2:
+                recent_dates = sorted(unique_dates, reverse=True)[:2]
+                df = df[df["date"].isin(recent_dates)]
+            
+            # 轉換為 pivot 格式
+            try:
+                pivot_df = df.pivot_table(
+                    index="date",
+                    columns="type",
+                    values="value",
+                    aggfunc="first"
+                ).reset_index()
+                
+                result = {
+                    "stock_id": ticker,
+                    "report_type": "balance_sheet",
+                    "data": pivot_df.to_dict(orient="records")
+                }
+            except Exception:
+                result = {
+                    "stock_id": ticker,
+                    "report_type": "balance_sheet",
+                    "data": df.to_dict(orient="records")
+                }
+            
+            return _format_output(result, use_toon)
+        else:
+            return _format_output({
+                "stock_id": ticker,
+                "report_type": "balance_sheet",
+                "data": [],
+                "message": "查無資料"
+            }, use_toon)
+            
+    except FinMindDataNotFoundError:
+        return _format_output({
+            "stock_id": ticker,
+            "report_type": "balance_sheet",
+            "data": [],
+            "message": f"找不到股票 {ticker} 的資產負債表資料"
+        }, use_toon)
+    except FinMindError as e:
+        return _format_output({
+            "error": str(e),
+            "stock_id": ticker
+        }, use_toon)
+
+
+def get_cashflow(
+    ticker: str,
+    freq: str = "quarterly",
+    curr_date: Optional[str] = None,
+    use_toon: bool = True
+) -> str:
+    """
+    獲取現金流量表（TaiwanStockCashFlowsStatement）。
+    
+    資料區間：2008-06-01 ~ now
+    
+    Returns:
+        str: JSON 格式的現金流量表資料
+    """
+    ticker = normalize_stock_id(ticker)
+    
+    if curr_date:
+        end_date = format_date(curr_date)
+        start_dt = datetime.strptime(end_date, "%Y-%m-%d") - timedelta(days=3*365)
+        start_date = format_date(start_dt)
+    else:
+        start_date = get_default_start_date(years_back=3)
+        end_date = format_date(datetime.now())
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockCashFlowsStatement",
+            data_id=ticker,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            df = pd.DataFrame(response["data"])
+            
+            unique_dates = df["date"].unique()
+            if len(unique_dates) > 2:
+                recent_dates = sorted(unique_dates, reverse=True)[:2]
+                df = df[df["date"].isin(recent_dates)]
+            
+            try:
+                pivot_df = df.pivot_table(
+                    index="date",
+                    columns="type",
+                    values="value",
+                    aggfunc="first"
+                ).reset_index()
+                
+                result = {
+                    "stock_id": ticker,
+                    "report_type": "cashflow",
+                    "data": pivot_df.to_dict(orient="records")
+                }
+            except Exception:
+                result = {
+                    "stock_id": ticker,
+                    "report_type": "cashflow",
+                    "data": df.to_dict(orient="records")
+                }
+            
+            return _format_output(result, use_toon)
+        else:
+            return _format_output({
+                "stock_id": ticker,
+                "report_type": "cashflow",
+                "data": [],
+                "message": "查無資料"
+            }, use_toon)
+            
+    except FinMindDataNotFoundError:
+        return _format_output({
+            "stock_id": ticker,
+            "report_type": "cashflow",
+            "data": [],
+            "message": f"找不到股票 {ticker} 的現金流量表資料"
+        }, use_toon)
+    except FinMindError as e:
+        return _format_output({
+            "error": str(e),
+            "stock_id": ticker
+        }, use_toon)
+
+
+def get_month_revenue(
+    ticker: str,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None
+) -> str:
+    """
+    獲取月營收表（TaiwanStockMonthRevenue）。
+    
+    資料區間：2002-02-01 ~ now
+    
+    返回欄位：
+    - date: 公布日期
+    - stock_id: 股票代碼
+    - country: 地區
+    - revenue: 當月營收
+    - revenue_month: 營收月份
+    - revenue_year: 營收年份
+    
+    Returns:
+        str: JSON 格式的月營收資料
+    """
+    ticker = normalize_stock_id(ticker)
+    
+    if not start_date:
+        start_date = get_default_start_date(years_back=2)
+    if not end_date:
+        end_date = format_date(datetime.now())
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockMonthRevenue",
+            data_id=ticker,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            df = pd.DataFrame(response["data"])
+            
+            # 只保留最近 12 個月的資料
+            df = df.sort_values("date", ascending=False).head(12)
+            
+            result = {
+                "stock_id": ticker,
+                "report_type": "month_revenue",
+                "data": df.to_dict(orient="records")
+            }
+            
+            return _format_output(result)
+        else:
+            return _format_output({
+                "stock_id": ticker,
+                "report_type": "month_revenue",
+                "data": [],
+                "message": "查無資料"
+            })
+            
+    except FinMindError as e:
+        return _format_output({
+            "error": str(e),
+            "stock_id": ticker
+        })
+
+
+def get_dividend(
+    ticker: str,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None
+) -> str:
+    """
+    獲取股利政策表（TaiwanStockDividend）。
+    
+    資料區間：2005-05-01 ~ now
+    
+    返回欄位包含現金股利、股票股利、除權息日等完整股利資訊。
+    
+    Returns:
+        str: JSON 格式的股利政策資料
+    """
+    ticker = normalize_stock_id(ticker)
+    
+    if not start_date:
+        start_date = get_default_start_date(years_back=5)
+    if not end_date:
+        end_date = format_date(datetime.now())
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockDividend",
+            data_id=ticker,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            df = pd.DataFrame(response["data"])
+            
+            # 只保留最近 10 筆
+            df = df.sort_values("date", ascending=False).head(10)
+            
+            result = {
+                "stock_id": ticker,
+                "report_type": "dividend",
+                "data": df.to_dict(orient="records")
+            }
+            
+            return _format_output(result)
+        else:
+            return _format_output({
+                "stock_id": ticker,
+                "report_type": "dividend",
+                "data": [],
+                "message": "查無資料"
+            })
+            
+    except FinMindError as e:
+        return _format_output({
+            "error": str(e),
+            "stock_id": ticker
+        })
+
+
+def get_fundamentals(
+    ticker: str,
+    curr_date: Optional[str] = None,
+    use_toon: bool = True
+) -> str:
+    """
+    獲取公司綜合基本面資料。
+    
+    整合多個資料來源，提供完整的基本面概覽：
+    - 最新月營收
+    - 最近一期損益表摘要
+    - 最近一期資產負債表摘要
+    - 最近股利資訊
+    
+    Args:
+        ticker: 股票代碼（例如 "2330"）
+        curr_date: 當前日期 (YYYY-MM-DD)
+        use_toon: 是否使用 toon 格式
+        
+    Returns:
+        str: toon 格式的綜合基本面資料
+    """
+    ticker = normalize_stock_id(ticker)
+    
+    if not curr_date:
+        curr_date = format_date(datetime.now())
+    
+    start_date = get_default_start_date(years_back=2)
+    
+    fundamentals = {
+        "stock_id": ticker,
+        "report_date": curr_date,
+        "月營收": None,
+        "損益表摘要": None,
+        "資產負債表摘要": None,
+        "股利政策": None,
+    }
+    
+    # 1. 獲取月營收
+    try:
+        revenue_response = _make_api_request(
+            dataset="TaiwanStockMonthRevenue",
+            data_id=ticker,
+            start_date=start_date,
+            end_date=curr_date
+        )
+        
+        if "data" in revenue_response and revenue_response["data"]:
+            df = pd.DataFrame(revenue_response["data"])
+            latest = df.sort_values("date", ascending=False).iloc[0]
+            fundamentals["月營收"] = {
+                "日期": latest.get("date"),
+                "營收": latest.get("revenue"),
+                "營收年份": latest.get("revenue_year"),
+                "營收月份": latest.get("revenue_month"),
+            }
+    except FinMindError:
+        pass
+    
+    # 2. 獲取損益表
+    try:
+        income_response = _make_api_request(
+            dataset="TaiwanStockFinancialStatements",
+            data_id=ticker,
+            start_date=start_date,
+            end_date=curr_date
+        )
+        
+        if "data" in income_response and income_response["data"]:
+            df = pd.DataFrame(income_response["data"])
+            latest_date = df["date"].max()
+            latest_data = df[df["date"] == latest_date]
+            
+            key_items = {}
+            for _, row in latest_data.iterrows():
+                key_items[row["type"]] = row["value"]
+            
+            fundamentals["損益表摘要"] = {
+                "日期": latest_date,
+                "關鍵指標": key_items
+            }
+    except FinMindError:
+        pass
+    
+    # 3. 獲取資產負債表
+    try:
+        balance_response = _make_api_request(
+            dataset="TaiwanStockBalanceSheet",
+            data_id=ticker,
+            start_date=start_date,
+            end_date=curr_date
+        )
+        
+        if "data" in balance_response and balance_response["data"]:
+            df = pd.DataFrame(balance_response["data"])
+            latest_date = df["date"].max()
+            latest_data = df[df["date"] == latest_date]
+            
+            key_items = {}
+            for _, row in latest_data.iterrows():
+                key_items[row["type"]] = row["value"]
+            
+            fundamentals["資產負債表摘要"] = {
+                "日期": latest_date,
+                "關鍵指標": key_items
+            }
+    except FinMindError:
+        pass
+    
+    # 4. 獲取股利資訊
+    try:
+        dividend_start = get_default_start_date(years_back=3)
+        dividend_response = _make_api_request(
+            dataset="TaiwanStockDividend",
+            data_id=ticker,
+            start_date=dividend_start,
+            end_date=curr_date
+        )
+        
+        if "data" in dividend_response and dividend_response["data"]:
+            df = pd.DataFrame(dividend_response["data"])
+            latest = df.sort_values("date", ascending=False).iloc[0].to_dict()
+            
+            # 只保留重要欄位
+            key_fields = [
+                "date", "year", 
+                "CashEarningsDistribution", "StockEarningsDistribution",
+                "CashExDividendTradingDate", "StockExDividendTradingDate"
+            ]
+            fundamentals["股利政策"] = {
+                k: latest.get(k) for k in key_fields if k in latest
+            }
+    except FinMindError:
+        pass
+    
+    # 轉換所有資料為可序列化的格式並輸出
+    return _format_output(fundamentals, use_toon)
+
+
+# 別名，與現有系統相容
+get_financial_statements = get_income_statement

tradingagents/dataflows/finmind_indicator.py

@@ -0,0 +1,654 @@
+# -*- coding: utf-8 -*-
+"""
+FinMind 技術指標資料模組
+用於獲取台灣股市技術指標和籌碼面數據
+
+API 文檔：
+- 技術面：https://finmind.github.io/tutor/TaiwanMarket/Technical/
+- 籌碼面：https://finmind.github.io/tutor/TaiwanMarket/Chip/
+
+可用的資料集：
+- TaiwanStockPER: 個股 PER、PBR 資料
+- TaiwanStockMarginPurchaseShortSale: 個股融資融劵表
+- TaiwanStockInstitutionalInvestorsBuySell: 法人買賣表
+- TaiwanStockShareholding: 外資持股表
+
+技術指標計算：
+- 本模組會從 FinMind 獲取股價數據，自行計算技術指標（SMA、RSI、MACD 等）
+- 不需要依賴 Alpha Vantage 或其他外部服務
+
+注意：本模組不使用需要 backer/sponsor 會員資格的功能
+"""
+
+import json
+from datetime import datetime
+from dateutil.relativedelta import relativedelta
+from typing import Optional
+import pandas as pd
+import numpy as np
+
+from .finmind_common import (
+    _make_api_request,
+    format_date,
+    get_default_start_date,
+    normalize_stock_id,
+    FinMindError,
+    FinMindDataNotFoundError,
+    format_output,
+)
+
+
+# 指標描述
+INDICATOR_DESCRIPTIONS = {
+    "per": "本益比（PER）：股價與每股盈餘之比，用於評估股票價值。較低的 PER 可能表示股票被低估。",
+    "pbr": "股價淨值比（PBR）：股價與每股淨值之比，用於評估公司淨資產價值。PBR 低於 1 可能表示股票被低估。",
+    "dividend_yield": "殖利率：股利與股價之比，衡量股票收益率。較高的殖利率可能吸引存股族。",
+    "margin_purchase": "融資餘額：投資人向券商借錢買股票的未還金額。融資增加可能表示看多情緒。",
+    "short_sale": "融券餘額：投資人借股票賣出的未補回數量。融券增加可能表示看空情緒。",
+    "institutional": "三大法人買賣超：外資、投信、自營商的買賣超情況，是重要的市場動向指標。",
+    "foreign_holding": "外資持股比例：外資持有該股票的比例。外資持續買超可能推升股價。",
+    # 技術指標描述
+    "sma": "簡單移動平均線（SMA）：過去 N 日收盤價的平均值，用於判斷趨勢方向。",
+    "ema": "指數移動平均線（EMA）：加權移動平均，對近期價格給予較高權重。",
+    "rsi": "相對強弱指標（RSI）：衡量價格動能，RSI>70 為超買，RSI<30 為超賣。",
+    "macd": "MACD：趨勢動能指標，由快線、慢線和柱狀圖組成，用於判斷買賣時機。",
+    "bbands": "布林通道（Bollinger Bands）：由中軌（SMA）和上下軌組成，用於判斷價格波動範圍。",
+}
+
+# 支援的技術指標列表
+CALCULATED_INDICATORS = [
+    "sma", "ema", "rsi", "macd", "bbands",
+    "close_5_sma", "close_10_sma", "close_20_sma", "close_50_sma", "close_100_sma", "close_200_sma",
+    "close_5_ema", "close_10_ema", "close_20_ema", "close_50_ema",
+]
+
+
+def _get_stock_price_data(symbol: str, start_date: str, end_date: str) -> pd.DataFrame:
+    """獲取股價數據並轉換為 DataFrame"""
+    response = _make_api_request(
+        dataset="TaiwanStockPrice",
+        data_id=symbol,
+        start_date=start_date,
+        end_date=end_date
+    )
+    
+    if "data" not in response or not response["data"]:
+        raise FinMindDataNotFoundError(f"找不到 {symbol} 的股價數據")
+    
+    df = pd.DataFrame(response["data"])
+    df["date"] = pd.to_datetime(df["date"])
+    df = df.sort_values("date").reset_index(drop=True)
+    
+    # 重命名欄位
+    df = df.rename(columns={
+        "max": "high",
+        "min": "low",
+        "Trading_Volume": "volume"
+    })
+    
+    return df
+
+
+def _calculate_sma(df: pd.DataFrame, period: int) -> pd.Series:
+    """計算簡單移動平均線"""
+    return df["close"].rolling(window=period).mean()
+
+
+def _calculate_ema(df: pd.DataFrame, period: int) -> pd.Series:
+    """計算指數移動平均線"""
+    return df["close"].ewm(span=period, adjust=False).mean()
+
+
+def _calculate_rsi(df: pd.DataFrame, period: int = 14) -> pd.Series:
+    """計算 RSI 指標"""
+    delta = df["close"].diff()
+    gain = delta.where(delta > 0, 0)
+    loss = (-delta).where(delta < 0, 0)
+    
+    avg_gain = gain.rolling(window=period).mean()
+    avg_loss = loss.rolling(window=period).mean()
+    
+    rs = avg_gain / avg_loss
+    rsi = 100 - (100 / (1 + rs))
+    
+    return rsi
+
+
+def _calculate_macd(df: pd.DataFrame, fast: int = 12, slow: int = 26, signal: int = 9) -> dict:
+    """計算 MACD 指標"""
+    ema_fast = df["close"].ewm(span=fast, adjust=False).mean()
+    ema_slow = df["close"].ewm(span=slow, adjust=False).mean()
+    
+    macd_line = ema_fast - ema_slow
+    signal_line = macd_line.ewm(span=signal, adjust=False).mean()
+    histogram = macd_line - signal_line
+    
+    return {
+        "macd": macd_line,
+        "signal": signal_line,
+        "histogram": histogram
+    }
+
+
+def _calculate_bbands(df: pd.DataFrame, period: int = 20, std_dev: float = 2.0) -> dict:
+    """計算布林通道"""
+    sma = df["close"].rolling(window=period).mean()
+    std = df["close"].rolling(window=period).std()
+    
+    return {
+        "upper": sma + (std * std_dev),
+        "middle": sma,
+        "lower": sma - (std * std_dev)
+    }
+
+
+def get_indicator(
+    symbol: str,
+    indicator: str,
+    curr_date: str,
+    look_back_days: int,
+    interval: str = "daily",
+    time_period: int = 14,
+    series_type: str = "close"
+) -> str:
+    """
+    返回 FinMind 在一個時間窗口內的技術指標/籌碼面數據。
+    
+    支援的籌碼面指標：
+    - per: 本益比
+    - pbr: 股價淨值比
+    - dividend_yield: 殖利率
+    - margin_purchase: 融資餘額
+    - short_sale: 融券餘額
+    - institutional: 三大法人買賣超
+    - foreign_holding: 外資持股比例
+    
+    支援的技術指標（從股價計算）：
+    - sma, close_N_sma: 簡單移動平均線
+    - ema, close_N_ema: 指數移動平均線
+    - rsi: 相對強弱指標
+    - macd: MACD 指標
+    - bbands: 布林通道
+    
+    Args:
+        symbol: 股票代碼（例如 "2330"）
+        indicator: 要獲取的指標類型
+        curr_date: 當前交易日期，格式為 YYYY-mm-dd
+        look_back_days: 回溯天數
+        interval: 時間間隔（保留參數，FinMind 只支援日線）
+        time_period: 用於計算的天數
+        series_type: 價格類型（保留參數）
+        
+    Returns:
+        str: 包含指標值和描述的字串
+    """
+    symbol = normalize_stock_id(symbol)
+    indicator = indicator.lower()
+    
+    # 籌碼面指標
+    chip_indicators = [
+        "per", "pbr", "dividend_yield",
+        "margin_purchase", "short_sale",
+        "institutional", "foreign_holding"
+    ]
+    
+    curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
+    before = curr_date_dt - relativedelta(days=look_back_days)
+    start_date = format_date(before)
+    end_date = format_date(curr_date_dt)
+    
+    try:
+        # 籌碼面指標
+        if indicator in chip_indicators:
+            if indicator in ["per", "pbr", "dividend_yield"]:
+                return _get_per_pbr_indicator(symbol, indicator, start_date, end_date)
+            elif indicator in ["margin_purchase", "short_sale"]:
+                return _get_margin_indicator(symbol, indicator, start_date, end_date)
+            elif indicator == "institutional":
+                return _get_institutional_indicator(symbol, start_date, end_date)
+            elif indicator == "foreign_holding":
+                return _get_foreign_holding_indicator(symbol, start_date, end_date)
+        
+        # 技術指標（從股價計算）
+        else:
+            return _calculate_technical_indicator(
+                symbol=symbol,
+                indicator=indicator,
+                start_date=start_date,
+                end_date=end_date,
+                time_period=time_period,
+                look_back_days=look_back_days
+            )
+            
+    except Exception as e:
+        print(f"獲取 {indicator} 的 FinMind 指標數據時出錯：{e}")
+        return f"檢索 {indicator} 數據時出錯：{str(e)}"
+
+
+def _calculate_technical_indicator(
+    symbol: str,
+    indicator: str,
+    start_date: str,
+    end_date: str,
+    time_period: int,
+    look_back_days: int
+) -> str:
+    """計算技術指標"""
+    indicator = indicator.lower()
+    
+    # 解析指標名稱（例如 close_50_sma -> sma, period=50）
+    period = time_period
+    indicator_type = indicator
+    
+    # 解析 close_N_sma 或 close_N_ema 格式
+    if "_sma" in indicator:
+        parts = indicator.split("_")
+        for i, p in enumerate(parts):
+            if p.isdigit():
+                period = int(p)
+        indicator_type = "sma"
+    elif "_ema" in indicator:
+        parts = indicator.split("_")
+        for i, p in enumerate(parts):
+            if p.isdigit():
+                period = int(p)
+        indicator_type = "ema"
+    
+    # 需要更多歷史數據來計算指標
+    start_date_dt = datetime.strptime(start_date, "%Y-%m-%d")
+    extended_start = start_date_dt - relativedelta(days=max(period + 50, 300))
+    extended_start_str = format_date(extended_start)
+    
+    try:
+        # 獲取股價數據
+        df = _get_stock_price_data(symbol, extended_start_str, end_date)
+        
+        if df.empty:
+            return f"找不到 {symbol} 的股價數據來計算 {indicator}。"
+        
+        # 計算指標
+        if indicator_type == "sma":
+            df["indicator"] = _calculate_sma(df, period)
+            desc = f"{period} 日簡單移動平均線"
+        elif indicator_type == "ema":
+            df["indicator"] = _calculate_ema(df, period)
+            desc = f"{period} 日指數移動平均線"
+        elif indicator_type == "rsi":
+            df["indicator"] = _calculate_rsi(df, period)
+            desc = f"{period} 日 RSI 相對強弱指標"
+        elif indicator_type == "macd":
+            macd_data = _calculate_macd(df)
+            df["macd"] = macd_data["macd"]
+            df["signal"] = macd_data["signal"]
+            df["histogram"] = macd_data["histogram"]
+            desc = "MACD 指標（12, 26, 9）"
+        elif indicator_type == "bbands":
+            bbands_data = _calculate_bbands(df, period)
+            df["bb_upper"] = bbands_data["upper"]
+            df["bb_middle"] = bbands_data["middle"]
+            df["bb_lower"] = bbands_data["lower"]
+            desc = f"{period} 日布林通道"
+        else:
+            return f"不支援的技術指標 {indicator}。支援的技術指標：sma, ema, rsi, macd, bbands, close_N_sma, close_N_ema"
+        
+        # 過濾日期範圍
+        start_date_dt = datetime.strptime(start_date, "%Y-%m-%d")
+        df = df[df["date"] >= pd.Timestamp(start_date_dt)]
+        
+        # 只顯示最近的數據
+        df = df.tail(min(look_back_days, 30))
+        
+        # 格式化輸出
+        ind_string = ""
+        
+        if indicator_type == "macd":
+            for _, row in df.iterrows():
+                date = row["date"].strftime("%Y-%m-%d")
+                macd_val = row["macd"]
+                signal_val = row["signal"]
+                hist_val = row["histogram"]
+                if pd.notna(macd_val):
+                    ind_string += f"{date}: MACD={macd_val:.4f}, Signal={signal_val:.4f}, Histogram={hist_val:.4f}\n"
+        elif indicator_type == "bbands":
+            for _, row in df.iterrows():
+                date = row["date"].strftime("%Y-%m-%d")
+                upper = row["bb_upper"]
+                middle = row["bb_middle"]
+                lower = row["bb_lower"]
+                close = row["close"]
+                if pd.notna(upper):
+                    ind_string += f"{date}: Upper={upper:.2f}, Middle={middle:.2f}, Lower={lower:.2f}, Close={close:.2f}\n"
+        else:
+            for _, row in df.iterrows():
+                date = row["date"].strftime("%Y-%m-%d")
+                value = row["indicator"]
+                close = row["close"]
+                if pd.notna(value):
+                    ind_string += f"{date}: {indicator.upper()}={value:.4f}, Close={close:.2f}\n"
+        
+        if not ind_string:
+            ind_string = "指定日期範圍內無足夠數據計算指標。\n"
+        
+        result_str = (
+            f"## 從 {start_date} 到 {end_date} 的 {desc} ({symbol})：\n\n"
+            + ind_string
+            + "\n\n"
+            + INDICATOR_DESCRIPTIONS.get(indicator_type, "技術指標計算自 FinMind 股價數據。")
+        )
+        
+        return result_str
+        
+    except FinMindError as e:
+        return f"獲取 {indicator} 數據時出錯：{str(e)}"
+    except Exception as e:
+        return f"計算 {indicator} 時出錯：{str(e)}"
+
+
+def _get_per_pbr_indicator(
+    symbol: str,
+    indicator: str,
+    start_date: str,
+    end_date: str
+) -> str:
+    """獲取 PER、PBR、殖利率指標"""
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockPER",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            data = response["data"]
+            
+            # 欄位映射
+            field_map = {
+                "per": "PER",
+                "pbr": "PBR",
+                "dividend_yield": "dividend_yield"
+            }
+            
+            field_name = field_map.get(indicator, indicator)
+            
+            # 格式化輸出
+            ind_string = ""
+            for row in sorted(data, key=lambda x: x.get("date", "")):
+                date = row.get("date", "")
+                value = row.get(field_name, "N/A")
+                ind_string += f"{date}: {value}\n"
+            
+            if not ind_string:
+                ind_string = "指定日期範圍內無可用數據。\n"
+            
+            result_str = (
+                f"## 從 {start_date} 到 {end_date} 的 {indicator.upper()} 值：\n\n"
+                + ind_string
+                + "\n\n"
+                + INDICATOR_DESCRIPTIONS.get(indicator, "無可用描述。")
+            )
+            
+            return result_str
+        else:
+            return f"找不到 {symbol} 的 {indicator} 數據。"
+            
+    except FinMindError as e:
+        return f"獲取 {indicator} 數據時出錯：{str(e)}"
+
+
+def _get_margin_indicator(
+    symbol: str,
+    indicator: str,
+    start_date: str,
+    end_date: str
+) -> str:
+    """獲取融資融券指標"""
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockMarginPurchaseShortSale",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            data = response["data"]
+            
+            # 欄位映射
+            if indicator == "margin_purchase":
+                field_name = "MarginPurchaseTodayBalance"
+                display_name = "融資餘額"
+            else:  # short_sale
+                field_name = "ShortSaleTodayBalance"
+                display_name = "融券餘額"
+            
+            # 格式化輸出
+            ind_string = ""
+            for row in sorted(data, key=lambda x: x.get("date", "")):
+                date = row.get("date", "")
+                value = row.get(field_name, "N/A")
+                ind_string += f"{date}: {value:,}\n" if isinstance(value, (int, float)) else f"{date}: {value}\n"
+            
+            if not ind_string:
+                ind_string = "指定日期範圍內無可用數據。\n"
+            
+            result_str = (
+                f"## 從 {start_date} 到 {end_date} 的 {display_name} ({symbol})：\n\n"
+                + ind_string
+                + "\n\n"
+                + INDICATOR_DESCRIPTIONS.get(indicator, "無可用描述。")
+            )
+            
+            return result_str
+        else:
+            return f"找不到 {symbol} 的融資融券數據。"
+            
+    except FinMindError as e:
+        return f"獲取融資融券數據時出錯：{str(e)}"
+
+
+def _get_institutional_indicator(
+    symbol: str,
+    start_date: str,
+    end_date: str
+) -> str:
+    """獲取三大法人買賣超指標"""
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockInstitutionalInvestorsBuySell",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            data = response["data"]
+            df = pd.DataFrame(data)
+            
+            # 按日期分組計算各法人買賣超
+            ind_string = ""
+            
+            # 獲取唯一日期
+            dates = sorted(df["date"].unique())
+            for date in dates:
+                day_data = df[df["date"] == date]
+                
+                # 彙總各法人買賣超
+                foreign = day_data[day_data["name"].str.contains("外資", na=False)]["buy"].sum() - \
+                         day_data[day_data["name"].str.contains("外資", na=False)]["sell"].sum()
+                investment_trust = day_data[day_data["name"].str.contains("投信", na=False)]["buy"].sum() - \
+                                  day_data[day_data["name"].str.contains("投信", na=False)]["sell"].sum()
+                dealer = day_data[day_data["name"].str.contains("自營商", na=False)]["buy"].sum() - \
+                        day_data[day_data["name"].str.contains("自營商", na=False)]["sell"].sum()
+                
+                total = foreign + investment_trust + dealer
+                ind_string += f"{date}: 外資 {foreign:+,} / 投信 {investment_trust:+,} / 自營 {dealer:+,} / 合計 {total:+,}\n"
+            
+            if not ind_string:
+                ind_string = "指定日期範圍內無可用數據。\n"
+            
+            result_str = (
+                f"## 從 {start_date} 到 {end_date} 的三大法人買賣超 ({symbol})：\n\n"
+                + ind_string
+                + "\n\n"
+                + INDICATOR_DESCRIPTIONS.get("institutional", "無可用描述。")
+            )
+            
+            return result_str
+        else:
+            return f"找不到 {symbol} 的三大法人買賣超數據。"
+            
+    except FinMindError as e:
+        return f"獲取三大法人數據時出錯：{str(e)}"
+
+
+def _get_foreign_holding_indicator(
+    symbol: str,
+    start_date: str,
+    end_date: str
+) -> str:
+    """獲取外資持股比例指標"""
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockShareholding",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            data = response["data"]
+            
+            # 格式化輸出
+            ind_string = ""
+            for row in sorted(data, key=lambda x: x.get("date", "")):
+                date = row.get("date", "")
+                holding_percent = row.get("ForeignInvestmentSharesRatio", "N/A")
+                holding_shares = row.get("ForeignInvestmentShares", "N/A")
+                
+                if isinstance(holding_percent, (int, float)):
+                    ind_string += f"{date}: {holding_percent:.2f}% ({holding_shares:,} 股)\n"
+                else:
+                    ind_string += f"{date}: {holding_percent}\n"
+            
+            if not ind_string:
+                ind_string = "指定日期範圍內無可用數據。\n"
+            
+            result_str = (
+                f"## 從 {start_date} 到 {end_date} 的外資持股比例 ({symbol})：\n\n"
+                + ind_string
+                + "\n\n"
+                + INDICATOR_DESCRIPTIONS.get("foreign_holding", "無可用描述。")
+            )
+            
+            return result_str
+        else:
+            return f"找不到 {symbol} 的外資持股數據。"
+            
+    except FinMindError as e:
+        return f"獲取外資持股數據時出錯：{str(e)}"
+
+
+def get_margin_data(
+    symbol: str,
+    start_date: str,
+    end_date: str
+) -> str:
+    """
+    獲取個股融資融劵表完整數據。
+    
+    資料區間：2001-01-01 ~ now
+    
+    返回欄位：
+    - date: 日期
+    - stock_id: 股票代碼
+    - MarginPurchaseBuy: 融資買進
+    - MarginPurchaseSell: 融資賣出
+    - MarginPurchaseTodayBalance: 融資今日餘額
+    - ShortSaleBuy: 融券買進
+    - ShortSaleSell: 融券賣出
+    - ShortSaleTodayBalance: 融券今日餘額
+    
+    Returns:
+        str: JSON 格式的融資融券數據
+    """
+    symbol = normalize_stock_id(symbol)
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockMarginPurchaseShortSale",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            result = {
+                "stock_id": symbol,
+                "data_type": "margin_trading",
+                "data": response["data"]
+            }
+            return format_output(result)
+        else:
+            return format_output({
+                "stock_id": symbol,
+                "data": [],
+                "message": "查無資料"
+            })
+            
+    except FinMindError as e:
+        return format_output({
+            "error": str(e),
+            "stock_id": symbol
+        })
+
+
+def get_institutional_data(
+    symbol: str,
+    start_date: str,
+    end_date: str
+) -> str:
+    """
+    獲取法人買賣表完整數據。
+    
+    資料區間：2005-01-01 ~ now
+    
+    返回欄位：
+    - date: 日期
+    - stock_id: 股票代碼
+    - name: 法人名稱（外資、投信、自營商等）
+    - buy: 買進金額
+    - sell: 賣出金額
+    
+    Returns:
+        str: JSON 格式的法人買賣數據
+    """
+    symbol = normalize_stock_id(symbol)
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockInstitutionalInvestorsBuySell",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            result = {
+                "stock_id": symbol,
+                "data_type": "institutional_investors",
+                "data": response["data"]
+            }
+            return format_output(result)
+        else:
+            return format_output({
+                "stock_id": symbol,
+                "data": [],
+                "message": "查無資料"
+            })
+            
+    except FinMindError as e:
+        return format_output({
+            "error": str(e),
+            "stock_id": symbol
+        })

tradingagents/dataflows/finmind_news.py

@@ -0,0 +1,387 @@
+# -*- coding: utf-8 -*-
+"""
+FinMind 新聞資料模組
+用於獲取台灣股市相關新聞和公告
+
+API 文檔：https://finmind.github.io/
+
+主要資料集：
+- TaiwanStockNews: 台股相關新聞（主要新聞來源）
+- TaiwanStockDividendResult: 除權息公告
+- TaiwanStockMonthRevenue: 月營收公告
+- TaiwanStockInstitutionalInvestorsBuySell: 法人買賣超
+"""
+
+import json
+from datetime import datetime, timedelta
+from typing import Optional
+import pandas as pd
+
+from .finmind_common import (
+    _make_api_request,
+    format_date,
+    get_default_start_date,
+    normalize_stock_id,
+    FinMindError,
+    FinMindDataNotFoundError,
+    format_output,
+)
+
+
+def get_news(
+    ticker: str,
+    start_date: str,
+    end_date: str,
+    use_toon: bool = True
+) -> str:
+    """
+    獲取台灣股市相關新聞資訊。
+    
+    使用 FinMind 的 TaiwanStockNews 資料集獲取真正的台股新聞。
+    資料區間：2019-04-01 ~ now
+    
+    返回欄位：
+    - date: 新聞日期
+    - stock_id: 股票代碼
+    - description: 新聞內容
+    - link: 新聞連結
+    - source: 新聞來源
+    - title: 新聞標題
+    
+    Args:
+        ticker: 股票代碼（例如 "2330"）
+        start_date: 開始日期
+        end_date: 結束日期
+        use_toon: 是否使用 toon 格式（保留參數）
+        
+    Returns:
+        str: JSON 格式的新聞資訊
+    """
+    ticker = normalize_stock_id(ticker)
+    
+    news_items = []
+    
+    # 1. 獲取真正的新聞（TaiwanStockNews）
+    # 注意：TaiwanStockNews 只支援 start_date，不支援 end_date
+    try:
+        import requests
+        import os
+        
+        url = "https://api.finmindtrade.com/api/v4/data"
+        token = os.getenv("FINMIND_API_TOKEN") or os.getenv("FINMIND_API_KEY")
+        headers = {"Authorization": f"Bearer {token}"} if token else {}
+        params = {
+            "dataset": "TaiwanStockNews",
+            "data_id": ticker,
+            "start_date": start_date,
+        }
+        
+        response = requests.get(url, headers=headers, params=params, timeout=30)
+        response.raise_for_status()
+        news_response = response.json()
+        
+        if "data" in news_response and news_response["data"]:
+            # 過濾日期範圍內的新聞
+            filtered_news = []
+            for item in news_response["data"]:
+                news_date = item.get("date", "")[:10]  # 只取日期部分
+                if news_date and start_date <= news_date <= end_date:
+                    filtered_news.append(item)
+            
+            # 取最近 20 筆新聞
+            for item in filtered_news[-20:]:
+                news_items.append({
+                    "title": item.get("title", "無標題"),
+                    "date": item.get("date", "")[:10],  # 只保留日期部分
+                    "type": "news",
+                    "summary": "",  # TaiwanStockNews 沒有 description 欄位
+                    "link": item.get("link", ""),
+                    "source": item.get("source", "FinMind")
+                })
+    except FinMindError as e:
+        print(f"獲取新聞時發生錯誤: {e}")
+    
+    # 2. 如果沒有新聞或新聞太少，補充其他資訊
+    if len(news_items) < 5:
+        # 補充股利公告
+        try:
+            dividend_response = _make_api_request(
+                dataset="TaiwanStockDividendResult",
+                data_id=ticker,
+                start_date=start_date,
+                end_date=end_date
+            )
+            
+            if "data" in dividend_response and dividend_response["data"]:
+                for item in dividend_response["data"][:3]:
+                    news_items.append({
+                        "title": f"{ticker} 除權息公告",
+                        "date": item.get("date", ""),
+                        "type": "dividend",
+                        "summary": f"除權息日：{item.get('date', '')}，"
+                                  f"參考價：{item.get('reference_price', 'N/A')}，"
+                                  f"股利合計：{item.get('stock_and_cache_dividend', 'N/A')}",
+                        "link": "",
+                        "source": "FinMind"
+                    })
+        except FinMindError:
+            pass
+        
+        # 補充月營收公告
+        try:
+            revenue_response = _make_api_request(
+                dataset="TaiwanStockMonthRevenue",
+                data_id=ticker,
+                start_date=start_date,
+                end_date=end_date
+            )
+            
+            if "data" in revenue_response and revenue_response["data"]:
+                for item in revenue_response["data"][-3:]:
+                    revenue = item.get("revenue", 0)
+                    if isinstance(revenue, (int, float)):
+                        revenue_str = f"{revenue:,.0f}"
+                    else:
+                        revenue_str = str(revenue)
+                        
+                    news_items.append({
+                        "title": f"{ticker} 月營收公告",
+                        "date": item.get("date", ""),
+                        "type": "revenue",
+                        "summary": f"{item.get('revenue_year', '')}年{item.get('revenue_month', '')}月營收：{revenue_str}",
+                        "link": "",
+                        "source": "FinMind"
+                    })
+        except FinMindError:
+            pass
+    
+    # 按日期排序（最新的在前）
+    news_items.sort(key=lambda x: x.get("date", ""), reverse=True)
+    
+    # 統計新聞類型
+    news_count = len([n for n in news_items if n.get("type") == "news"])
+    other_count = len(news_items) - news_count
+    
+    result = {
+        "stock_id": ticker,
+        "items": len(news_items),
+        "news_count": news_count,
+        "note": f"包含 {news_count} 則新聞" + (f"和 {other_count} 則公司公告" if other_count > 0 else ""),
+        "feed": news_items[:15]  # 限制最多 15 筆
+    }
+    
+    return format_output(result, use_toon)
+
+
+def get_global_news(
+    curr_date: str,
+    look_back_days: int = 7
+) -> str:
+    """
+    獲取台灣股市整體市場新聞/動態。
+    
+    注意：FinMind 不提供全球新聞 API，
+    本函式透過市場整體指標提供替代資訊。
+    
+    Args:
+        curr_date: 當前日期
+        look_back_days: 回溯天數
+        
+    Returns:
+        str: JSON 格式的市場動態
+    """
+    curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
+    start_date_dt = curr_date_dt - timedelta(days=look_back_days)
+    start_date = format_date(start_date_dt)
+    end_date = format_date(curr_date_dt)
+    
+    market_news = []
+    
+    # 1. 獲取整體市場融資融券
+    try:
+        margin_response = _make_api_request(
+            dataset="TaiwanStockTotalMarginPurchaseShortSale",
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in margin_response and margin_response["data"]:
+            for item in margin_response["data"][-3:]:  # 取最近 3 筆
+                market_news.append({
+                    "title": "台股整體融資融券動態",
+                    "date": item.get("date", ""),
+                    "type": "margin_total",
+                    "summary": f"{item.get('name', '')}：今日餘額 {item.get('TodayBalance', 'N/A'):,}，"
+                              f"增減 {item.get('TodayBalance', 0) - item.get('YesBalance', 0):+,}",
+                    "source": "FinMind"
+                })
+    except FinMindError:
+        pass
+    
+    # 2. 獲取整體三大法人買賣超
+    try:
+        institutional_response = _make_api_request(
+            dataset="TaiwanStockTotalInstitutionalInvestors",
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in institutional_response and institutional_response["data"]:
+            df = pd.DataFrame(institutional_response["data"])
+            
+            dates = sorted(df["date"].unique(), reverse=True)[:3]
+            for date in dates:
+                day_data = df[df["date"] == date]
+                
+                summary_parts = []
+                for _, row in day_data.iterrows():
+                    name = row.get("name", "")
+                    buy = row.get("buy", 0)
+                    sell = row.get("sell", 0)
+                    net = buy - sell
+                    
+                    if isinstance(net, (int, float)):
+                        summary_parts.append(f"{name} {net/100000000:+,.2f} 億")
+                
+                if summary_parts:
+                    market_news.append({
+                        "title": "台股三大法人買賣超",
+                        "date": date,
+                        "type": "institutional_total",
+                        "summary": "，".join(summary_parts),
+                        "source": "FinMind"
+                    })
+    except FinMindError:
+        pass
+    
+    # 按日期排序
+    market_news.sort(key=lambda x: x.get("date", ""), reverse=True)
+    
+    result = {
+        "market": "Taiwan",
+        "items": len(market_news),
+        "note": "FinMind 不提供新聞 API，此為市場整體動態資訊",
+        "feed": market_news
+    }
+    
+    return format_output(result)
+
+
+def get_insider_sentiment(ticker: str, curr_date: str) -> str:
+    """
+    獲取內部人交易情緒（透過法人買賣超資料模擬）。
+    
+    Args:
+        ticker: 股票代碼
+        curr_date: 當前日期
+        
+    Returns:
+        str: JSON 格式的情緒分析
+    """
+    ticker = normalize_stock_id(ticker)
+    
+    curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
+    start_date_dt = curr_date_dt - timedelta(days=30)
+    start_date = format_date(start_date_dt)
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockInstitutionalInvestorsBuySell",
+            data_id=ticker,
+            start_date=start_date,
+            end_date=curr_date
+        )
+        
+        if "data" in response and response["data"]:
+            df = pd.DataFrame(response["data"])
+            
+            # 計算整體買賣超趨勢
+            total_buy = df["buy"].sum()
+            total_sell = df["sell"].sum()
+            net = total_buy - total_sell
+            
+            # 判斷情緒
+            if net > 0:
+                sentiment = "正面"
+                sentiment_score = min(1.0, net / (total_buy + total_sell) * 2) if (total_buy + total_sell) > 0 else 0
+            else:
+                sentiment = "負面"
+                sentiment_score = max(-1.0, net / (total_buy + total_sell) * 2) if (total_buy + total_sell) > 0 else 0
+            
+            result = {
+                "stock_id": ticker,
+                "period": f"{start_date} ~ {curr_date}",
+                "sentiment": sentiment,
+                "sentiment_score": round(sentiment_score, 3),
+                "total_buy": int(total_buy),
+                "total_sell": int(total_sell),
+                "net": int(net),
+                "note": "基於法人買賣超資料計算的情緒指標"
+            }
+            
+            return format_output(result)
+        else:
+            return format_output({
+                "stock_id": ticker,
+                "error": "查無資料"
+            })
+            
+    except FinMindError as e:
+        return format_output({
+            "stock_id": ticker,
+            "error": str(e)
+        })
+
+
+def get_insider_transactions(symbol: str) -> str:
+    """
+    獲取內部人交易資訊。
+    
+    注意：FinMind 未提供內部人交易 API，
+    本函式透過法人買賣超資料提供類似資訊。
+    
+    Args:
+        symbol: 股票代碼
+        
+    Returns:
+        str: JSON 格式的交易資訊
+    """
+    symbol = normalize_stock_id(symbol)
+    
+    end_date = format_date(datetime.now())
+    start_date = get_default_start_date(years_back=0)  # 最近 1 個月
+    start_date_dt = datetime.now() - timedelta(days=30)
+    start_date = format_date(start_date_dt)
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockInstitutionalInvestorsBuySell",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            # 只保留最近 15 筆
+            data = response["data"][-15:]
+            
+            result = {
+                "stock_id": symbol,
+                "data_type": "institutional_trading",
+                "note": "FinMind 不提供內部人交易 API，此為法人買賣超資料",
+                "data": data
+            }
+            
+            return format_output(result)
+        else:
+            return format_output({
+                "stock_id": symbol,
+                "data": [],
+                "message": "查無資料"
+            })
+            
+    except FinMindError as e:
+        return format_output({
+            "error": str(e),
+            "stock_id": symbol
+        })

tradingagents/dataflows/finmind_stock.py

@@ -0,0 +1,250 @@
+# -*- coding: utf-8 -*-
+"""
+FinMind 股價資料模組
+用於獲取台灣股市股價數據
+
+API 文檔：https://finmind.github.io/tutor/TaiwanMarket/Technical/
+
+可用的資料集：
+- TaiwanStockPrice: 股價日成交資訊（1994-10-01 ~ now）
+- TaiwanStockInfo: 台股總覽
+- TaiwanStockPER: 個股 PER、PBR 資料
+
+注意：本模組不使用需要 backer/sponsor 會員資格的功能
+（如 TaiwanStockPriceAdj、TaiwanStockWeekPrice、TaiwanStockMonthPrice）
+"""
+
+import json
+from datetime import datetime
+from typing import Optional
+
+from .finmind_common import (
+    _make_api_request,
+    format_date,
+    normalize_stock_id,
+    FinMindError,
+    FinMindDataNotFoundError,
+    _filter_by_date_range,
+    format_output,
+)
+
+
+def get_stock(
+    symbol: str,
+    start_date: str,
+    end_date: str
+) -> str:
+    """
+    返回台灣股票的每日 OHLCV 數據。
+    
+    資料區間：1994-10-01 ~ now
+    
+    返回欄位：
+    - date: 日期
+    - stock_id: 股票代碼
+    - Trading_Volume: 成交量
+    - Trading_money: 成交金額
+    - open: 開盤價
+    - max: 最高價
+    - min: 最低價
+    - close: 收盤價
+    - spread: 漲跌價差
+    - Trading_turnover: 成交筆數
+    
+    Args:
+        symbol: 股票代碼（例如 "2330"）
+        start_date: 開始日期，格式為 YYYY-MM-DD
+        end_date: 結束日期，格式為 YYYY-MM-DD
+        
+    Returns:
+        str: CSV 格式的股價數據字串
+    """
+    symbol = normalize_stock_id(symbol)
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockPrice",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            data = response["data"]
+            
+            # 轉換為 CSV 格式（與 Alpha Vantage 保持一致）
+            if not data:
+                return "date,open,high,low,close,volume\n"
+            
+            # 建立 CSV 標頭
+            csv_lines = ["date,open,high,low,close,volume"]
+            
+            for row in data:
+                csv_lines.append(
+                    f"{row.get('date', '')},"
+                    f"{row.get('open', '')},"
+                    f"{row.get('max', '')},"
+                    f"{row.get('min', '')},"
+                    f"{row.get('close', '')},"
+                    f"{row.get('Trading_Volume', '')}"
+                )
+            
+            return "\n".join(csv_lines)
+        else:
+            return "date,open,high,low,close,volume\n"
+            
+    except FinMindDataNotFoundError:
+        return "date,open,high,low,close,volume\n"
+    except FinMindError as e:
+        print(f"警告：獲取股價數據失敗：{e}")
+        return "date,open,high,low,close,volume\n"
+
+
+def get_stock_info(stock_id: Optional[str] = None) -> str:
+    """
+    獲取台股總覽資訊。
+    
+    包含所有上市、上櫃、興櫃的股票名稱、代碼和產業類別。
+    
+    返回欄位：
+    - industry_category: 產業類別
+    - stock_id: 股票代碼
+    - stock_name: 股票名稱
+    - type: 類型（twse/tpex/rotc）
+    - date: 更新日期
+    
+    Args:
+        stock_id: 股票代碼（選填，可查詢特定股票）
+        
+    Returns:
+        str: JSON 格式的股票資訊
+    """
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockInfo",
+            data_id=stock_id
+        )
+        
+        if "data" in response and response["data"]:
+            # 如果查詢特定股票，只返回該股票的資訊
+            if stock_id:
+                stock_id = normalize_stock_id(stock_id)
+                filtered = [
+                    d for d in response["data"] 
+                    if d.get("stock_id") == stock_id
+                ]
+                return format_output(filtered)
+            else:
+                # 限制返回數量以減少 token
+                data = response["data"][:100]
+                return format_output(data)
+        else:
+            return format_output([])
+            
+    except FinMindError as e:
+        return format_output({"error": str(e)})
+
+
+def get_stock_per(
+    symbol: str,
+    start_date: str,
+    end_date: str
+) -> str:
+    """
+    獲取個股 PER、PBR 資料。
+    
+    返回欄位：
+    - date: 日期
+    - stock_id: 股票代碼
+    - dividend_yield: 殖利率
+    - PER: 本益比
+    - PBR: 股價淨值比
+    
+    Args:
+        symbol: 股票代碼（例如 "2330"）
+        start_date: 開始日期
+        end_date: 結束日期
+        
+    Returns:
+        str: JSON 格式的 PER/PBR 資料
+    """
+    symbol = normalize_stock_id(symbol)
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockPER",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            result = {
+                "stock_id": symbol,
+                "data": response["data"]
+            }
+            return format_output(result)
+        else:
+            return format_output({
+                "stock_id": symbol,
+                "data": [],
+                "message": "查無資料"
+            })
+            
+    except FinMindDataNotFoundError:
+        return format_output({
+            "stock_id": symbol,
+            "data": [],
+            "message": f"找不到股票 {symbol} 的 PER/PBR 資料"
+        })
+    except FinMindError as e:
+        return format_output({
+            "error": str(e),
+            "stock_id": symbol
+        })
+
+
+def get_day_trading(
+    symbol: str,
+    start_date: str,
+    end_date: str
+) -> str:
+    """
+    獲取當日沖銷交易標的及成交量值。
+    
+    Args:
+        symbol: 股票代碼（例如 "2330"）
+        start_date: 開始日期
+        end_date: 結束日期
+        
+    Returns:
+        str: JSON 格式的當沖資料
+    """
+    symbol = normalize_stock_id(symbol)
+    
+    try:
+        response = _make_api_request(
+            dataset="TaiwanStockDayTrading",
+            data_id=symbol,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if "data" in response and response["data"]:
+            result = {
+                "stock_id": symbol,
+                "data": response["data"]
+            }
+            return format_output(result)
+        else:
+            return format_output({
+                "stock_id": symbol,
+                "data": [],
+                "message": "查無資料"
+            })
+            
+    except FinMindError as e:
+        return format_output({
+            "error": str(e),
+            "stock_id": symbol
+        })

tradingagents/dataflows/interface.py

@@ -17,6 +17,21 @@ from .alpha_vantage import (
 )
 from .alpha_vantage_common import AlphaVantageRateLimitError
 
+# FinMind 台灣股市資料供應商
+from .finmind import (
+    get_stock as get_finmind_stock,
+    get_indicator as get_finmind_indicator,
+    get_fundamentals as get_finmind_fundamentals,
+    get_balance_sheet as get_finmind_balance_sheet,
+    get_cashflow as get_finmind_cashflow,
+    get_income_statement as get_finmind_income_statement,
+    get_news as get_finmind_news,
+    get_global_news as get_finmind_global_news,
+    get_insider_sentiment as get_finmind_insider_sentiment,
+    get_insider_transactions as get_finmind_insider_transactions,
+    FinMindRateLimitError,
+)
+
 # 設定和路由邏輯
 from .config import get_config
 
@@ -58,7 +73,9 @@ VENDOR_LIST = [
     "local",
     "yfinance",
     "openai",
-    "google"
+    "google",
+    "alpha_vantage",
+    "finmind",  # 台灣股市資料供應商
 ]
 
 # 方法與其特定供應商實現的映射
@@ -68,33 +85,39 @@ VENDOR_METHODS = {
         "alpha_vantage": get_alpha_vantage_stock,
         "yfinance": get_YFin_data_online,
         "local": get_YFin_data,
+        "finmind": get_finmind_stock,  # 台股資料
     },
     # 技術指標
     "get_indicators": {
         "alpha_vantage": get_alpha_vantage_indicator,
         "yfinance": get_stock_stats_indicators_window,
-        "local": get_stock_stats_indicators_window
+        "local": get_stock_stats_indicators_window,
+        "finmind": get_finmind_indicator,  # 台股技術指標/籌碼面
     },
     # 基本面數據
     "get_fundamentals": {
         "alpha_vantage": get_alpha_vantage_fundamentals,
         "openai": get_fundamentals_openai,
         "yfinance": get_yfinance_fundamentals,
+        "finmind": get_finmind_fundamentals,  # 台股基本面
     },
     "get_balance_sheet": {
         "alpha_vantage": get_alpha_vantage_balance_sheet,
         "yfinance": get_yfinance_balance_sheet,
         "local": get_simfin_balance_sheet,
+        "finmind": get_finmind_balance_sheet,  # 台股資產負債表
     },
     "get_cashflow": {
         "alpha_vantage": get_alpha_vantage_cashflow,
         "yfinance": get_yfinance_cashflow,
         "local": get_simfin_cashflow,
+        "finmind": get_finmind_cashflow,  # 台股現金流量表
     },
     "get_income_statement": {
         "alpha_vantage": get_alpha_vantage_income_statement,
         "yfinance": get_yfinance_income_statement,
         "local": get_simfin_income_statements,
+        "finmind": get_finmind_income_statement,  # 台股損益表
     },
     # 新聞數據
     "get_news": {
@@ -102,18 +125,22 @@ VENDOR_METHODS = {
         "openai": get_stock_news_openai,
         "google": get_google_news,
         "local": [get_finnhub_news, get_reddit_company_news, get_google_news],
+        "finmind": get_finmind_news,  # 台股公告/法人動態
     },
     "get_global_news": {
         "openai": get_global_news_openai,
-        "local": get_reddit_global_news
+        "local": get_reddit_global_news,
+        "finmind": get_finmind_global_news,  # 台股市場動態
     },
     "get_insider_sentiment": {
-        "local": get_finnhub_company_insider_sentiment
+        "local": get_finnhub_company_insider_sentiment,
+        "finmind": get_finmind_insider_sentiment,  # 台股法人情緒
     },
     "get_insider_transactions": {
         "alpha_vantage": get_alpha_vantage_insider_transactions,
         "yfinance": get_yfinance_insider_transactions,
         "local": get_finnhub_company_insider_transactions,
+        "finmind": get_finmind_insider_transactions,  # 台股法人交易
     },
 }
 
@@ -213,6 +240,12 @@ def route_to_vendor(method: str, *args, **kwargs):
                     print(f"調試：速率限制詳細資訊：{e}")
                 # 繼續到下一個供應商進行備援
                 continue
+            except FinMindRateLimitError as e:
+                if vendor == "finmind":
+                    print(f"速率限制：超過 FinMind 速率限制，將備援至下一個可用供應商")
+                    print(f"調試：速率限制詳細資訊：{e}")
+                # 繼續到下一個供應商進行備援
+                continue
             except Exception as e:
                 # 記錄詳細錯誤但繼續其他實現
                 error_type = type(e).__name__

tradingagents/default_config.py

@@ -19,16 +19,24 @@ DEFAULT_CONFIG = {
     "max_recur_limit": 100,
     # 資料供應商設定
     # 類別層級設定 (該類別所有工具的預設值)
+    # 可用供應商:
+    #   - yfinance: Yahoo Finance (美股為主)
+    #   - alpha_vantage: Alpha Vantage API (美股為主)
+    #   - finmind: FinMind API (台股專用)
+    #   - openai: OpenAI 網路搜尋
+    #   - google: Google News
+    #   - local: 本地資料
     "data_vendors": {
-        "core_stock_apis": "yfinance",       # 選項: yfinance, alpha_vantage, local
+        "core_stock_apis": "yfinance",       # 選項: yfinance, alpha_vantage, finmind, local
-        "technical_indicators": "yfinance",  # 選項: yfinance, alpha_vantage, local
+        "technical_indicators": "yfinance",  # 選項: yfinance, alpha_vantage, finmind, local
-        "fundamental_data": "alpha_vantage", # 選項: openai, alpha_vantage, local
+        "fundamental_data": "alpha_vantage", # 選項: openai, alpha_vantage, yfinance, finmind
-        "news_data": "openai",               # 選項: openai, alpha_vantage, google, local
+        "news_data": "openai",               # 選項: openai, alpha_vantage, google, finmind, local
     },
     # 工具層級設定 (優先於類別層級設定)
     "tool_vendors": {
         # 範例: "get_stock_data": "alpha_vantage",  # 覆寫類別預設值
         # 範例: "get_news": "openai",               # 覆寫類別預設值
+        # 範例: "get_stock_data": "finmind",        # 使用 FinMind 獲取台股資料
         "get_global_news": "openai",  # get_global_news 不支持 alpha_vantage，使用 openai 作為主要供應商
     },
 }