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 pandas as pd
import dotenv
dotenv.load_dotenv()

# --- 新版 Prompt（改為人類可讀的非 JSON 輸出） ---
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，且以「易讀文字段落/清單」方式呈現 (非 JSON)：
請根據以下結構，撰寫一份詳細的分析報告：

1. 【股票代碼】  
   - 說明：顯示本次分析所針對的股票代號。

2. 【股票價格趨勢與技術指標分析】  
   - 說明：闡述近期股價變化趨勢、波動情況，並搭配 RSI、MACD、VWAP 等技術指標，提供觀察與解讀。

3. 【技術指標分析與見解】  
   - 說明：進一步解釋 RSI、MACD 等所呈現的訊號，與可能的後續走勢。

4. 【財務分析】  
   - 4.1 獲利能力比率分析  
   - 4.2 流動性比率分析  
   - 4.3 償債能力比率分析  
   - 4.4 營運效率比率分析  
   - 4.5 市場表現比率分析  
   - 4.6 總結財務健康狀況

5. 【新聞分析】  
   - 說明：列出近期相關新聞及其對股價、企業形象可能產生的影響。

6. 【產業分析】  
   - 說明：討論產業趨勢、發展動能與可能風險。

7. 【競爭對手分析】  
   - 說明：比較主要競爭對手之優勢與劣勢，以及對目標公司的影響。

8. 【最終結論與投資建議】  
   - 說明：歸納整體分析結果，給出投資人應採取之行動或建議。

9. 【回答使用者問題】  
   - 針對「Should I buy this stock?」之回應，提出依據上述分析的結論性建議。

※ 請確保敘述完整、清晰、條理分明、方便一般讀者閱讀。同時避免使用 JSON 格式，改以段落或清單式敘述。
"""

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:
        # 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 indicators for a given ticker."""
    try:
        start, end = period_to_start_end(period)
        print(f"[get_stock_prices] Downloading data for ticker={ticker}, period={period}")
        print(f"[get_stock_prices] Start={start}, End={end}")

        data = yf.download(
            ticker,
            start=start,
            end=end,
            interval='1d'
        )

        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}"}

        # 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]

        # 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
        n = min(12, len(df))

        indicators = {}

        # 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()
        }

        # Stochastic
        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()
        }

        # MACD
        macd = MACD(df['Close'])
        macd_series = macd.macd().iloc[-n:]
        indicators["MACD"] = {
            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.dropna().items()
        }

        # VWAP
        vwap_series = volume_weighted_average_price(
            high=df['High'],
            low=df['Low'],
            close=df['Close'],
            volume=df['Volume']
        ).iloc[-n:]
        indicators["vwap"] = {
            str(date.date()): float(value)
            for date, value in vwap_series.dropna().items()
        }

        return {
            "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:
        print(f"[get_financial_news] Fetching news for ticker={ticker}")
        stock = yf.Ticker(ticker)
        news = stock.news  # 從 Yahoo Finance 獲取新聞

        if not news:
            print("[get_financial_news] No news data returned from yfinance.")
            return {"news": "No recent news found."}

        # 詳細列印前 5 則新聞資訊
        latest_news = []
        for i, item in enumerate(news[:5], start=1):
            print(f"[get_financial_news] News item {i} RAW: {json.dumps(item, indent=2)}")
            latest_news.append({
                "title": item.get("title"),
                "publisher": item.get("publisher"),
                "link": item.get("link"),
                "published_date": item.get("providerPublishTime")
            })

        print(f"[get_financial_news] Found {len(latest_news)} news items (detailed above).")
        return {"news": latest_news}
    except Exception as e:
        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

        print(f"[get_financial_metrics] Info returned:\n{json.dumps(info, indent=2)}")

        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:
        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，
    組合成分析 Prompt 呼叫 LLM，最後回傳『易讀文字段落』的基本面分析結果。
    """
    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)

        # 組合 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)}

請注意：即使新聞的 description 或 summary 為空，也請務必根據新聞的 title、publisher 或其他字段，推斷其可能對股價與公司形象造成的影響。請將這些新聞具體列在新聞分析章節。
{prompt}
"""

        print("[analyze_stock] Sending prompt to LLM...")
        response = llm.invoke(analysis_prompt)

        return response.content  # 回傳「非 JSON」的易讀文字段落
    except Exception as e:
        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,
    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="基本面財務新聞方面分析結果"),
    title="我應該買這支股票嗎？ by DAVID888",
    description="輸入您的 LLM openai key、股票代號與時間區間，取得該股票的分析報告"
)

if __name__ == "__main__":
    iface.launch()