diff --git a/README.md b/README.md index e9795319..6fc52088 100644 --- a/README.md +++ b/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) --- diff --git a/backend/app/api/routes.py b/backend/app/api/routes.py index 866ea864..334c8720 100644 --- a/backend/app/api/routes.py +++ b/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 diff --git a/backend/app/models/schemas.py b/backend/app/models/schemas.py index ddc9f922..c2e9059c 100644 --- a/backend/app/models/schemas.py +++ b/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( - ..., - description="Alpha Vantage API Key (required for fundamental data)", - min_length=1 + alpha_vantage_api_key: Optional[str] = Field( + None, + description="Alpha Vantage API Key (optional, for US stock fundamental data)", + min_length=0 + ) + finmind_api_key: Optional[str] = Field( + None, + description="FinMind API Token (optional, for Taiwan stock data)", + min_length=0 ) diff --git a/backend/app/services/price_service.py b/backend/app/services/price_service.py index 4c220143..40584dd6 100644 --- a/backend/app/services/price_service.py +++ b/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") - return None + # 如果是台股，嘗試另一個後綴 + 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 - cache_file = Path(data_cache_dir) / f"{ticker}-YFin-data-{start_date.strftime('%Y-%m-%d')}-{end_date.strftime('%Y-%m-%d')}.csv" + # Save to cache - 使用原始代碼作為檔名（不含後綴） + 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 diff --git a/backend/app/services/trading_service.py b/backend/app/services/trading_service.py index 73fa4283..ffea02ea 100644 --- a/backend/app/services/trading_service.py +++ b/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) diff --git a/frontend/components/AgentFlowDiagram.tsx b/frontend/components/AgentFlowDiagram.tsx index a4e7bab2..928c60f7 100644 --- a/frontend/components/AgentFlowDiagram.tsx +++ b/frontend/components/AgentFlowDiagram.tsx @@ -50,7 +50,7 @@ export function AgentFlowDiagram() { /> } - name="Alpha Vantage" + name="Alpha Vantage / FinMind" description="財務數據" color="purple" /> diff --git a/frontend/components/analysis/AnalysisForm.tsx b/frontend/components/analysis/AnalysisForm.tsx index 00f2814d..e7b100f6 100644 --- a/frontend/components/analysis/AnalysisForm.tsx +++ b/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) { /> - {/* 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） */} +
+ {/* 市場類型選擇 */} + ( + + 市場類型 + + + 選擇分析的股票市場 + + + + )} + /> + + {/* 股票代碼 */} 股票代碼 - + - 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）" + } diff --git a/frontend/components/settings/ApiSettingsDialog.tsx b/frontend/components/settings/ApiSettingsDialog.tsx index d811f0fa..919b9dbf 100644 --- a/frontend/components/settings/ApiSettingsDialog.tsx +++ b/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() { )} /> +
- {/* Alpha Vantage API Key */} + {/* Stock Market Data APIs Section */} +
+

+ 股市資料 API（依分析市場選擇填寫） +

+ + {/* FinMind API Key - Taiwan Stocks */} + ( + + FinMind API Token（台股） + + + + + 用於獲取台灣股市資料（在 finmindtrade.com 註冊取得） + + + + )} + /> + + {/* Alpha Vantage API Key - US Stocks */} ( - Alpha Vantage API Key * + Alpha Vantage API Key（美股） - 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） @@ -160,10 +190,10 @@ export function ApiSettingsDialog() { />
- {/* Optional Section */} + {/* Optional LLM Providers Section */}

- 選填項目（依需求填寫） + 選填項目（其他 LLM 供應商）

{/* Anthropic API Key */} diff --git a/frontend/lib/storage.ts b/frontend/lib/storage.ts index ed0df41b..edb7dd3a 100644 --- a/frontend/lib/storage.ts +++ b/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: "", }; diff --git a/frontend/lib/types.ts b/frontend/lib/types.ts index 9d810c50..54d7fbd5 100644 --- a/frontend/lib/types.ts +++ b/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 { diff --git a/frontend/pnpm-lock.yaml b/frontend/pnpm-lock.yaml index 1346c61f..bcfada27 100644 --- a/frontend/pnpm-lock.yaml +++ b/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: 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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, diff --git a/tradingagents/dataflows/alpha_vantage_news.py b/tradingagents/dataflows/alpha_vantage_news.py index f0d4aa05..87dfaffe 100644 --- a/tradingagents/dataflows/alpha_vantage_news.py +++ b/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 diff --git a/tradingagents/dataflows/findmind.py b/tradingagents/dataflows/findmind.py new file mode 100644 index 00000000..234180f8 --- /dev/null +++ b/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 +) diff --git a/tradingagents/dataflows/finmind.py b/tradingagents/dataflows/finmind.py new file mode 100644 index 00000000..3f66b744 --- /dev/null +++ b/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", +] diff --git a/tradingagents/dataflows/finmind_common.py b/tradingagents/dataflows/finmind_common.py new file mode 100644 index 00000000..4045fe7a --- /dev/null +++ b/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 diff --git a/tradingagents/dataflows/finmind_fundamentals.py b/tradingagents/dataflows/finmind_fundamentals.py new file mode 100644 index 00000000..2ba6a228 --- /dev/null +++ b/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 diff --git a/tradingagents/dataflows/finmind_indicator.py b/tradingagents/dataflows/finmind_indicator.py new file mode 100644 index 00000000..5a194377 --- /dev/null +++ b/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 + }) diff --git a/tradingagents/dataflows/finmind_news.py b/tradingagents/dataflows/finmind_news.py new file mode 100644 index 00000000..ae77f40f --- /dev/null +++ b/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 + }) diff --git a/tradingagents/dataflows/finmind_stock.py b/tradingagents/dataflows/finmind_stock.py new file mode 100644 index 00000000..447f844d --- /dev/null +++ b/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 + }) diff --git a/tradingagents/dataflows/interface.py b/tradingagents/dataflows/interface.py index a227b054..59dec51c 100644 --- a/tradingagents/dataflows/interface.py +++ b/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__ diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py index 00aecb6a..5ea07140 100644 --- a/tradingagents/default_config.py +++ b/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 - "technical_indicators": "yfinance", # 選項: yfinance, alpha_vantage, local - "fundamental_data": "alpha_vantage", # 選項: openai, alpha_vantage, local - "news_data": "openai", # 選項: openai, alpha_vantage, google, local + "core_stock_apis": "yfinance", # 選項: yfinance, alpha_vantage, finmind, local + "technical_indicators": "yfinance", # 選項: yfinance, alpha_vantage, finmind, local + "fundamental_data": "alpha_vantage", # 選項: openai, alpha_vantage, yfinance, finmind + "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 }, } \ No newline at end of file