o1

app.py

@@ -16,6 +16,7 @@ from ta.volume import volume_weighted_average_price
 import dotenv
 dotenv.load_dotenv()
 
+# --- 新版 Prompt ---
 FUNDAMENTAL_ANALYST_PROMPT = """
 You are a fundamental analyst specializing in evaluating company (whose symbol is {company}) performance based on stock prices, technical indicators, financial metrics, recent news, industry trends, competitor positioning, and financial ratios. Your task is to provide a comprehensive summary.
 
@@ -32,11 +33,11 @@ You have access to the following tools:
 2. Analyze the following areas in sequence:
    - **Stock price movements and technical indicators**: Examine recent price trends, volatility, and signals from RSI, MACD, VWAP, and other indicators.
    - **Financial health and key financial ratios**: Assess profitability, liquidity, solvency, and operational efficiency using metrics such as:
-     - Profitability Ratios: Gross Profit Margin, Net Profit Margin, Operating Profit Margin
-     - Liquidity Ratios: Current Ratio, Quick Ratio
-     - Solvency Ratios: Debt-to-Equity Ratio, Interest Coverage Ratio
-     - Efficiency Ratios: Inventory Turnover, Accounts Receivable Turnover
-     - Market Ratios: Price-to-Earnings Ratio (P/E), Price-to-Book Ratio (P/B)
+     - Profitability Ratios: Gross Profit Margin, Net Profit Margin, Operating Profit Margin  
+     - Liquidity Ratios: Current Ratio, Quick Ratio  
+     - Solvency Ratios: Debt-to-Equity Ratio, Interest Coverage Ratio  
+     - Efficiency Ratios: Inventory Turnover, Accounts Receivable Turnover  
+     - Market Ratios: Price-to-Earnings Ratio (P/E), Price-to-Book Ratio (P/B)  
    - **Recent news and market sentiment**: Identify significant events or trends impacting the company's market perception.
    - **Industry analysis**: Evaluate the industry’s growth trends, technological advancements, and regulatory environment. Identify how the industry is evolving and how it affects the target company.
    - **Competitor analysis**: Compare the target company with key competitors in terms of market share, financial health, and growth potential.
@@ -47,7 +48,7 @@ You have access to the following tools:
 
 ### Output Format : 以下請用繁體中文輸出
 {
-  "stock": "",
+  "stock": "<Stock Symbol>",
   "price_analysis": "<股票價格趨勢與技術指標分析>",
   "technical_analysis": "<技術指標分析與見解>",
   "financial_analysis": {
@@ -64,6 +65,14 @@ You have access to the following tools:
   "final_summary": "<整體綜合結論與投資建議>",
   "Asked Question Answer": "<根據上述分析的具體回答>"
 }
+
+---
+
+### Guidelines:
+- Use the provided tools for data. If any data is unavailable, clearly state so in the respective section.
+- Ensure the analysis is objective, data-driven, and free of speculative language.
+- Keep responses concise but informative. Highlight actionable insights and risks.
+- Output should be structured, easy to read, and in Traditional Chinese.
 """
 
 def period_to_start_end(period_str: str):
@@ -78,12 +87,12 @@ def period_to_start_end(period_str: str):
     elif period_str == "1yr":
         start = now - dt.timedelta(weeks=52)
     else:
+        # 若未指定或指定其他值，一律視為 3 個月
         start = now - dt.timedelta(weeks=13)
     return start, now
 
-
 def get_stock_prices(ticker: str, period: str = "3mo") -> Union[Dict, str]:
-    """Fetches historical stock price data and technical indicator for a given ticker."""
+    """Fetches historical stock price data and technical indicators for a given ticker."""
     try:
         start, end = period_to_start_end(period)
         data = yf.download(
@@ -96,7 +105,7 @@ def get_stock_prices(ticker: str, period: str = "3mo") -> Union[Dict, str]:
         if data.empty:
             return {"error": f"No stock data found for {ticker}"}
 
-        # 如果 columns 有多層，才做重命名
+        # 處理多層欄位
         if data.columns.nlevels > 1:
             data.columns = [col[0] for col in data.columns]
 
@@ -105,9 +114,11 @@ def get_stock_prices(ticker: str, period: str = "3mo") -> Union[Dict, str]:
 
         df = data.copy()
 
-        indicators = {}
+        # 指標只取最後 12 筆即可
         n = min(12, len(df))
 
+        indicators = {}
+
         # RSI
         rsi_series = RSIIndicator(df['Close'], window=14).rsi().iloc[-n:]
         indicators["RSI"] = {
@@ -149,7 +160,6 @@ def get_stock_prices(ticker: str, period: str = "3mo") -> Union[Dict, str]:
     except Exception as e:
         return f"Error fetching price data: {str(e)}"
 
-
 def get_financial_news(ticker: str) -> Union[Dict, str]:
     """Fetches the latest financial news related to a given ticker."""
     try:
@@ -171,7 +181,6 @@ def get_financial_news(ticker: str) -> Union[Dict, str]:
     except Exception as e:
         return f"Error fetching news: {str(e)}"
 
-
 def get_financial_metrics(ticker: str) -> Union[Dict, str]:
     """Fetches key financial ratios for a given ticker."""
     try:
@@ -186,7 +195,6 @@ def get_financial_metrics(ticker: str) -> Union[Dict, str]:
     except Exception as e:
         return f"Error fetching ratios: {str(e)}"
 
-# --- 綜合基本面分析 ---
 def analyze_stock(api_key: str, ticker: str, period: str) -> str:
     """
     根據輸入的 LLM API key、股票代號與時間區間，抓取各項資料後，
@@ -199,12 +207,13 @@ def analyze_stock(api_key: str, ticker: str, period: str) -> str:
             openai_api_key=api_key,
             temperature=0
         )
+
         # 取得資料
         price_data = get_stock_prices(ticker, period)
         metrics = get_financial_metrics(ticker)
         news = get_financial_news(ticker)
 
-        # 準備 prompt
+        # 組合 Prompt
         prompt = FUNDAMENTAL_ANALYST_PROMPT.replace("{company}", ticker)
         user_question = "Should I buy this stock?"
         analysis_prompt = f"""
@@ -221,9 +230,11 @@ def analyze_stock(api_key: str, ticker: str, period: str) -> str:
 
 {prompt}
 """
+
         # 呼叫 LLM 生成最終分析報告
         response = llm.invoke(analysis_prompt)
         return response.content
+
     except Exception as e:
         return f"分析過程中發生錯誤: {str(e)}\n{traceback.format_exc()}"