Create core/stock_analysis.py

core/stock_analysis.py

@@ -0,0 +1,108 @@
+# 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}