1

.gitignore

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+__pycache__/
+.env
+myenv/

README.md

@@ -11,3 +11,43 @@ short_description: 透過即時新聞/財報/和股價 ，幫你評估股票值
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+---
+
+
+# 股票基本面分析系統
+
+這是一個使用 AI 技術的股票基本面分析系統，能夠提供：
+- 股價走勢分析
+- 技術指標分析
+- 財務比率分析
+- 新聞分析
+- 產業分析
+- 競爭對手分析
+
+## 使用方式
+1. 輸入股票代號（例如：2330.TW）
+2. 選擇想要分析的資料期間（3個月、6個月或1年）
+3. 等待系統生成分析報告
+
+## 環境設置
+系統需要設置以下環境變數：
+- OPENAI_API_KEY：OpenAI API 金鑰
+
+## 本地運行
+```bash
+# 建立虛擬環境
+python -m venv venv
+
+# 啟動虛擬環境
+# Windows:
+venv\Scripts\activate
+# Linux/Mac:
+source venv/bin/activate
+
+# 安裝依賴
+pip install -r requirements.txt
+
+# 運行應用
+python app.py

app.py

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+import os
+import datetime as dt
+import yfinance as yf
+import traceback
+import json
+from typing import Union, Dict
+
+import gradio as gr
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
+
+from ta.momentum import RSIIndicator, StochasticOscillator
+from ta.trend import MACD
+from ta.volume import volume_weighted_average_price
+
+import dotenv
+dotenv.load_dotenv()
+
+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.
+
+You have access to the following tools:
+1. **get_stock_prices**: Retrieves stock price data and technical indicators.
+2. **get_financial_metrics**: Retrieves key financial metrics and financial ratios.
+3. **get_financial_news**: Retrieves the latest financial news related to the stock.
+4. **get_industry_data** *(if available)*: Retrieves industry trends and competitive positioning information.
+
+---
+
+### Your Task:
+1. Use the provided stock symbol to query the 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)
+   - **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.
+
+3. Provide a concise and structured summary covering all sections, ensuring each area has actionable insights.
+
+---
+
+### Output Format : 以下請用繁體中文輸出
+{
+  "stock": "",
+  "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": {
+      "profitability_ratios": "<獲利能力比率分析>",
+      "liquidity_ratios": "<流動性比率分析>",
+      "solvency_ratios": "<償債能力比率分析>",
+      "efficiency_ratios": "<營運效率比率分析>",
+      "market_ratios": "<市場表現比率分析>",
+      "summary": "<財務整體健康狀況與分析結論>"
+  },
+  "news_analysis": "<近期新聞摘要與其對股價的潛在影響>",
+  "industry_analysis": "<產業趨勢、成長動力與潛在風險>",
+  "competitor_analysis": "<主要競爭對手比較與市場地位分析>",
+  "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>"
+}
+"""
+
+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。
+    """
+    now = dt.datetime.now()
+    if period_str == "3mo":
+        start = now - dt.timedelta(weeks=13)
+    elif period_str == "6mo":
+        start = now - dt.timedelta(weeks=26)
+    elif period_str == "1yr":
+        start = now - dt.timedelta(weeks=52)
+    else:
+        start = now - dt.timedelta(weeks=13)
+    return start, now
+
+# --- 取得股票價格與技術指標 ---
+def get_stock_prices(ticker: str, period: str = "3mo") -> Union[Dict, str]:
+    """
+    使用 start 與 end 取得歷史股價資料（避免連線到 fc.yahoo.com），
+    並計算 RSI、Stochastic、MACD 與 VWAP 指標。
+    """
+    try:
+        start_date, end_date = period_to_start_end(period)
+        data = yf.download(
+            ticker,
+            start=start_date,
+            end=end_date,
+            interval='1d'
+        )
+        df = data.copy()
+        # 若有 multi-index，調整欄位名稱
+        if df.columns.nlevels > 1:
+            df.columns = [col[0] for col in df.columns]
+        data.reset_index(inplace=True)
+        data['Date'] = data['Date'].astype(str)
+
+        indicators = {}
+
+        # RSI
+        rsi_series = RSIIndicator(df['Close'], window=14).rsi().iloc[-12:]
+        indicators["RSI"] = {date.strftime('%Y-%m-%d'): int(value) for date, value in rsi_series.dropna().to_dict().items()}
+
+        # Stochastic Oscillator
+        sto_series = StochasticOscillator(df['High'], df['Low'], df['Close'], window=14).stoch().iloc[-12:]
+        indicators["Stochastic_Oscillator"] = {date.strftime('%Y-%m-%d'): int(value) for date, value in sto_series.dropna().to_dict().items()}
+
+        # MACD 與訊號線
+        macd = MACD(df['Close'])
+        macd_series = macd.macd().iloc[-12:]
+        indicators["MACD"] = {date.strftime('%Y-%m-%d'): int(value) for date, value in macd_series.to_dict().items()}
+        macd_signal_series = macd.macd_signal().iloc[-12:]
+        indicators["MACD_Signal"] = {date.strftime('%Y-%m-%d'): int(value) for date, value in macd_signal_series.to_dict().items()}
+
+        # VWAP
+        vwap_series = volume_weighted_average_price(
+            high=df['High'],
+            low=df['Low'],
+            close=df['Close'],
+            volume=df['Volume']
+        ).iloc[-12:]
+        indicators["vwap"] = {date.strftime('%Y-%m-%d'): int(value) for date, value in vwap_series.to_dict().items()}
+
+        return {'stock_price': data.to_dict(orient='records'), 'indicators': indicators}
+    except Exception as e:
+        return f"Error fetching price data: {str(e)}"
+
+# --- 取得財務新聞 ---
+def get_financial_news(ticker: str) -> Union[Dict, str]:
+    try:
+        stock = yf.Ticker(ticker)
+        news = stock.news
+        if not news:
+            return {"news": "No recent news found."}
+        latest_news = [
+            {
+                "title": item.get('title'),
+                "publisher": item.get('publisher'),
+                "link": item.get('link'),
+                "published_date": item.get('providerPublishTime')
+            }
+            for item in news[:5]
+        ]
+        return {"news": latest_news}
+    except Exception as e:
+        return f"Error fetching news: {str(e)}"
+
+# --- 取得財務指標 ---
+def get_financial_metrics(ticker: str) -> Union[Dict, str]:
+    try:
+        stock = yf.Ticker(ticker)
+        info = stock.info
+        return {
+            'pe_ratio': info.get('forwardPE'),
+            'price_to_book': info.get('priceToBook'),
+            'debt_to_equity': info.get('debtToEquity'),
+            'profit_margins': info.get('profitMargins')
+        }
+    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，
+    組合成分析 Prompt 呼叫 LLM，最後回傳基本面分析結果。
+    """
+    try:
+        # 建立 LLM 實例
+        llm = ChatOpenAI(
+            model='gpt-4o',
+            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
+        prompt = FUNDAMENTAL_ANALYST_PROMPT.replace("{company}", ticker)
+        user_question = "Should I buy this stock?"
+        analysis_prompt = f"""
+根據以下 {ticker} 的資料，進行全面的基本面分析並回答使用者問題："{user_question}"
+
+股價與技術指標資料：
+{json.dumps(price_data, ensure_ascii=False, indent=2)}
+
+財務指標：
+{json.dumps(metrics, ensure_ascii=False, indent=2)}
+
+相關新聞：
+{json.dumps(news, ensure_ascii=False, indent=2)}
+
+{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()}"
+
+# --- Gradio 介面 ---
+iface = gr.Interface(
+    fn=analyze_stock,
+    inputs=[
+        gr.Textbox(label="LLM API Key", type="password", placeholder="請輸入 OpenAI API Key"),
+        gr.Textbox(label="股票代號", placeholder="例如：TSLA 或 2330.TW"),
+        gr.Dropdown(choices=["3mo", "6mo", "1yr"], label="時間區間", value="3mo")
+    ],
+    outputs=gr.Textbox(label="基本面分析結果"),
+    title="股票基本面分析 App",
+    description="輸入您的 LLM API key、股票代號與時間區間，取得該股票的基本面分析報告。"
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+yfinance
+ta
+langchain
+langchain-openai
+openai
+python-dotenv
+pandas
+numpy