app.py

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()