core/stock_analysis.py

# core/stock_analysis.py

import requests
import json
from datetime import datetime, timedelta
from tradingview_ta import TA_Handler, Interval
from langchain.document_loaders import WebBaseLoader
from langchain.docstore.document import Document
from bs4 import BeautifulSoup
from GoogleNews import GoogleNews
from langchain.prompts import PromptTemplate
from langchain.chains import StuffDocumentsChain, LLMChain

def clean_google_news_url(url: str):
    for ext in [".html", ".cms"]:
        if ext in url:
            return url.split(ext)[0] + ext
    return url.split("&")[0]

def get_google_news_documents(query: str, max_articles: int = 10, timeout: int = 10):
    googlenews = GoogleNews(lang="en")
    end_date = datetime.today()
    start_date = end_date - timedelta(days=2)
    googlenews.set_time_range(start_date.strftime("%m/%d/%Y"), end_date.strftime("%m/%d/%Y"))
    googlenews.search(query)
    articles = googlenews.result()

    documents = []
    for article in articles[:max_articles]:
        url = clean_google_news_url(article.get("link"))
        try:
            response = requests.get(url, timeout=timeout, headers={"User-Agent": "Mozilla/5.0"})
            response.raise_for_status()
            soup = BeautifulSoup(response.text, "html.parser")
            paragraphs = soup.find_all("p")
            content = "\n".join([p.get_text(strip=True) for p in paragraphs if p.get_text(strip=True)])
            if content and len(content) > 200:
                doc = Document(
                    page_content=content,
                    metadata={
                        "source": "Google News",
                        "title": article.get("title", ""),
                        "published": article.get("date", ""),
                        "link": url,
                    }
                )
                documents.append(doc)
        except Exception:
            continue
    return documents

def analyze_stock(ticker, llm):
    try:
        handler = TA_Handler(symbol=ticker, screener="india", exchange="NSE", interval=Interval.INTERVAL_1_DAY)
        summary = handler.get_analysis().summary
    except Exception:
        return {"error": "Invalid ticker or failed to fetch trading data"}

    urls = [
        f"https://www.google.com/finance/quote/{ticker}:NSE?hl=en",
        f"https://in.tradingview.com/symbols/NSE-{ticker}/",
        f"https://in.tradingview.com/symbols/NSE-{ticker}/news/",
        f"https://in.tradingview.com/symbols/NSE-{ticker}/minds/"
    ]

    loader = WebBaseLoader(urls)
    web_docs = loader.load()

    news_docs = get_google_news_documents(f"Trending News for {ticker}", max_articles=10)
    docs = web_docs + news_docs

    prompt_template = """You are an expert Stock Market Trader...

    Context:
    {input_documents}

    Task:
    Summarize the stock...
    RETURN JSON with this schema:

    ```json
    {{
      "stock_summary": {{
        "company_name": "",
        "ticker": "",
        "exchange": "",
        ...
      }},
      ...
    }}
    ```
    """

    prompt = PromptTemplate.from_template(prompt_template)
    chain = StuffDocumentsChain(llm_chain=LLMChain(llm=llm, prompt=prompt), document_variable_name="input_documents")
    response = chain.invoke({"input_documents": docs})
    raw = response["output_text"].strip()

    # Clean code block markdown if present
    if raw.startswith("```json"):
        raw = raw[len("```json"):]
    if raw.endswith("```"):
        raw = raw[:-3]

    try:
        return json.loads(raw.strip())
    except json.JSONDecodeError:
        return {"error": "Failed to parse model output", "raw": raw}