o3

app.py

@@ -13,6 +13,7 @@ from ta.momentum import RSIIndicator, StochasticOscillator
 from ta.trend import MACD
 from ta.volume import volume_weighted_average_price
 
+import pandas as pd
 import dotenv
 dotenv.load_dotenv()
 
@@ -75,6 +76,7 @@ You have access to the following tools:
 - Output should be structured, easy to read, and in Traditional Chinese.
 """
 
+
 def period_to_start_end(period_str: str):
     """
     根據使用者選擇的時間區間，計算起始與結束日期。
@@ -91,15 +93,13 @@ def period_to_start_end(period_str: str):
         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 indicators for a given ticker."""
     try:
         start, end = period_to_start_end(period)
-
-        # --- 除錯訊息開始 ---
         print(f"[get_stock_prices] Downloading data for ticker={ticker}, period={period}")
         print(f"[get_stock_prices] Start={start}, End={end}")
-        # --- 除錯訊息結束 ---
 
         data = yf.download(
             ticker,
@@ -108,24 +108,27 @@ def get_stock_prices(ticker: str, period: str = "3mo") -> Union[Dict, str]:
             interval='1d'
         )
 
-        # --- 除錯訊息：看資料長度與前幾筆 ---
         print(f"[get_stock_prices] Fetched data shape={data.shape}")
         if not data.empty:
             print(f"[get_stock_prices] Head of data:\n{data.head()}")
         else:
             print("[get_stock_prices] No data returned from yfinance.")
-        # ---
 
         if data.empty:
             return {"error": f"No stock data found for {ticker}"}
 
-        # 處理多層欄位
+        # 如果是多層欄位，就平坦化
         if data.columns.nlevels > 1:
             data.columns = [col[0] for col in data.columns]
 
-        data.reset_index(inplace=True)
-        data["Date"] = data["Date"].astype(str)
+        # 設定日期為索引 (DatetimeIndex)
+        data.index = pd.to_datetime(data.index)
 
+        # 保留一份副本來輸出給 LLM 用
+        df_for_output = data.copy().reset_index()
+        df_for_output["Date"] = df_for_output["Date"].astype(str)
+
+        # 另存一份給技術指標用 (確保是 DatetimeIndex)
         df = data.copy()
 
         # 指標只取最後 12 筆即可
@@ -136,24 +139,33 @@ def get_stock_prices(ticker: str, period: str = "3mo") -> Union[Dict, str]:
         # RSI
         rsi_series = RSIIndicator(df['Close'], window=14).rsi().iloc[-n:]
         indicators["RSI"] = {
-            str(date.date()): float(value) for date, value in rsi_series.dropna().items()
+            str(date.date()): float(value)
+            for date, value in rsi_series.dropna().items()
         }
 
         # Stochastic
-        sto_series = StochasticOscillator(df['High'], df['Low'], df['Close'], window=14).stoch().iloc[-n:]
+        sto_series = StochasticOscillator(
+            df['High'],
+            df['Low'],
+            df['Close'],
+            window=14
+        ).stoch().iloc[-n:]
         indicators["Stochastic_Oscillator"] = {
-            str(date.date()): float(value) for date, value in sto_series.dropna().items()
+            str(date.date()): float(value)
+            for date, value in sto_series.dropna().items()
         }
 
         # MACD
         macd = MACD(df['Close'])
         macd_series = macd.macd().iloc[-n:]
         indicators["MACD"] = {
-            str(date.date()): float(value) for date, value in macd_series.items()
+            str(date.date()): float(value)
+            for date, value in macd_series.dropna().items()
         }
         macd_signal_series = macd.macd_signal().iloc[-n:]
         indicators["MACD_Signal"] = {
-            str(date.date()): float(value) for date, value in macd_signal_series.items()
+            str(date.date()): float(value)
+            for date, value in macd_signal_series.dropna().items()
         }
 
         # VWAP
@@ -164,17 +176,20 @@ def get_stock_prices(ticker: str, period: str = "3mo") -> Union[Dict, str]:
             volume=df['Volume']
         ).iloc[-n:]
         indicators["vwap"] = {
-            str(date.date()): float(value) for date, value in vwap_series.items()
+            str(date.date()): float(value)
+            for date, value in vwap_series.dropna().items()
         }
 
         return {
-            "stock_price": data.to_dict(orient="records"),
+            "stock_price": df_for_output.to_dict(orient="records"),
             "indicators": indicators
         }
+
     except Exception as e:
         print(f"[get_stock_prices] Exception: {str(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:
@@ -202,13 +217,15 @@ def get_financial_news(ticker: str) -> Union[Dict, str]:
         print(f"[get_financial_news] Exception: {str(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:
         print(f"[get_financial_metrics] Fetching financial metrics for ticker={ticker}")
         stock = yf.Ticker(ticker)
         info = stock.info
-        # --- 除錯訊息：打印完整 info 來檢查看看 ---
+
+        # 顯示完整 info 以便除錯
         print(f"[get_financial_metrics] Info returned:\n{json.dumps(info, indent=2)}")
 
         return {
@@ -221,6 +238,7 @@ def get_financial_metrics(ticker: str) -> Union[Dict, str]:
         print(f"[get_financial_metrics] Exception: {str(e)}")
         return f"Error fetching ratios: {str(e)}"
 
+
 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，
@@ -257,7 +275,6 @@ def analyze_stock(api_key: str, ticker: str, period: str) -> str:
 {prompt}
 """
 
-        # 呼叫 LLM 生成最終分析報告
         print("[analyze_stock] Sending prompt to LLM...")
         response = llm.invoke(analysis_prompt)
 
@@ -267,6 +284,7 @@ def analyze_stock(api_key: str, ticker: str, period: str) -> str:
         print(f"[analyze_stock] Exception: {str(e)}")
         return f"分析過程中發生錯誤: {str(e)}\n{traceback.format_exc()}"
 
+
 # --- Gradio 介面 ---
 iface = gr.Interface(
     fn=analyze_stock,