src/news_analysis.py

# import os
# import csv
# from datetime import datetime
# from langchain_openai import ChatOpenAI
# from langchain_core.prompts import PromptTemplate
# from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
# import requests
# from dotenv import load_dotenv
# from fin_interpreter import analyze_article
# from tavily import TavilyClient

# # === Load environment or passed keys ===
# load_dotenv()
# OPENAI_KEY = os.environ.get("OPENAI_API_KEY") or os.getenv("OPENAI_KEY")
# TAVILY_KEY = os.environ.get("TAVILY_API_KEY") or os.getenv("TAVILY_KEY")

# # === Initialize Tavily Client ===
# tavily_client = TavilyClient(api_key=TAVILY_KEY)

# # === Get OpenAI client when needed ===
# def get_llm():
#     openai_key = os.environ.get("OPENAI_API_KEY")
#     if not openai_key:
#         raise ValueError("OPENAI_API_KEY not found.")
#     return ChatOpenAI(model_name="gpt-4.1", openai_api_key=openai_key)

# # === Related Terms ===
# def get_related_terms(topic):
#     llm = get_llm()
#     prompt = f"What are 5 closely related financial or industry terms to '{topic}'?"
#     response = llm.invoke(prompt)
#     return response.content.split(",")

# def tavily_search(query, days, max_results=10):
#     api_key = os.getenv("TAVILY_KEY")
#     url = "https://api.tavily.com/search"
#     headers = {"Authorization": f"Bearer {api_key}"}
#     payload = {
#         "query": query,
#         "search_depth": "advanced",
#         "topic": "news",
#         "days": int(days),
#         "max_results": max_results,
#         "include_answer": False,
#         "include_raw_content": False
#     }
#     response = requests.post(url, json=payload, headers=headers)
#     return response.json()

# # === Smart News Search ===
# def fetch_deep_news(topic, days):
#     all_results = []
#     seen_urls = set()

#     base_queries = [
#         topic,
#         f"{topic} AND startup",
#         f"{topic} AND acquisition OR merger OR funding",
#         f"{topic} AND CEO OR executive OR leadership",
#         f"{topic} AND venture capital OR Series A OR Series B",
#         f"{topic} AND government grant OR approval OR contract",
#         f"{topic} AND underrated OR small-cap OR micro-cap"
#     ]

#     investor_queries = [
#         f"{topic} AND BlackRock OR Vanguard OR SoftBank",
#         f"{topic} AND Elon Musk OR Sam Altman OR Peter Thiel",
#         f"{topic} AND Berkshire Hathaway OR Warren Buffett",
#         f"{topic} AND institutional investor OR hedge fund",
#     ]

#     related_terms = get_related_terms(topic)
#     synonym_queries = [f"{term} AND {kw}" for term in related_terms for kw in ["startup", "funding", "merger", "acquisition"]]

#     all_queries = base_queries + investor_queries + synonym_queries

#     for query in all_queries:
#         try:
#             print(f"🔍 Tavily query: {query}")
#             response = requests.post(
#                 url="https://api.tavily.com/search",
#                 headers={
#                     "Authorization": f"Bearer {TAVILY_KEY}",
#                     "Content-Type": "application/json"
#                 },
#                 json={
#                     "query": query,
#                     "search_depth": "advanced",
#                     "topic": "news",
#                     "days": int(days),
#                     "max_results": 10,
#                     "include_answer": False,
#                     "include_raw_content": False
#                 }
#             )

#             if response.status_code != 200:
#                 print(f"⚠️ Tavily API error: {response.status_code} - {response.text}")
#                 continue

#             for item in response.json().get("results", []):
#                 url = item.get("url")
#                 content = item.get("content", "") or item.get("summary", "") or item.get("title", "")
#                 if url and url not in seen_urls and len(content) > 150:
#                     all_results.append({
#                         "title": item.get("title"),
#                         "url": url,
#                         "content": content
#                     })
#                     seen_urls.add(url)

#         except Exception as e:
#             print(f"⚠️ Tavily request failed for query '{query}': {e}")

#     print(f"📰 Total articles collected: {len(all_results)}")
#     return all_results

# # === Generate Markdown Report ===
# def generate_value_investor_report(topic, news_results, max_articles=20, max_chars_per_article=400):
#     news_results = news_results[:max_articles]

#     for item in news_results:
#         result = analyze_article(item["content"])
#         item["fin_sentiment"] = result.get("sentiment", "neutral")
#         item["fin_confidence"] = result.get("confidence", 0.0)
#         item["investment_decision"] = result.get("investment_decision", "Watch")

#     article_summary = "".join(
#         f"- **{item['title']}**: {item['content'][:max_chars_per_article]}... "
#         f"(Sentiment: {item['fin_sentiment'].title()}, Confidence: {item['fin_confidence']:.2f}, "
#         f"Decision: {item['investment_decision']}) [link]({item['url']})\n"
#         for item in news_results
#     )

#     prompt = PromptTemplate.from_template("""
# You're a highly focused value investor. Analyze this week's news on "{Topic}".

# Your goal is to uncover:
# - Meaningful events (e.g., CEO joining a startup, insider buys, big-name partnerships)
# - Startups or small caps that may signal undervalued opportunity
# - Connections to key individuals or institutions (e.g., Elon Musk investing, Sam Altman joining)
# - Companies with strong fundamentals: low P/E, low P/B, high ROE, recent IPOs, moats, or high free cash flow

# ### News
# {ArticleSummaries}

# Write a markdown memo with:
# 1. **Key Value Signals**
# 2. **Stocks or Startups to Watch**
# 3. **What Smart Money Might Be Acting On**
# 4. **References**
# 5. **Investment Hypothesis**

# Include context and macroeconomic/regulatory angles. Add an intro on sentiment and market trends for the week.
# """)

#     chat_prompt = ChatPromptTemplate.from_messages([
#         SystemMessagePromptTemplate(prompt=prompt)
#     ])
#     prompt_value = chat_prompt.format_prompt(
#         Topic=topic,
#         ArticleSummaries=article_summary
#     ).to_messages()

#     llm = get_llm()
#     result = llm.invoke(prompt_value)
#     return result.content

import os
import csv
from datetime import datetime
from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
import requests
from dotenv import load_dotenv
from fin_interpreter import analyze_article

# === Load environment ===
load_dotenv()
OPENAI_KEY = os.environ.get("OPENAI_API_KEY") or os.getenv("OPENAI_KEY")
TAVILY_KEY = None  # Will be accessed dynamically at runtime

# === Get OpenAI client when needed ===
def get_llm():
    openai_key = os.environ.get("OPENAI_API_KEY")
    if not openai_key:
        raise ValueError("OPENAI_API_KEY not found.")
    return ChatOpenAI(model_name="gpt-4.1", openai_api_key=openai_key)

# === Related Terms ===
def get_related_terms(topic):
    llm = get_llm()
    prompt = f"What are 5 closely related financial or industry terms to '{topic}'?"
    response = llm.invoke(prompt)
    return response.content.split(",")

# === Tavily Search ===
def tavily_search(query, days, max_results=10):
    api_key = os.environ.get("TAVILY_API_KEY") or TAVILY_KEY
    url = "https://api.tavily.com/search"
    headers = {"Authorization": f"Bearer {api_key}"}
    payload = {
        "query": query,
        "search_depth": "advanced",
        "topic": "news",
        "days": int(days),
        "max_results": max_results,
        "include_answer": False,
        "include_raw_content": False
    }
    response = requests.post(url, json=payload, headers=headers)
    return response.json()

# === Smart News Search ===
def fetch_deep_news(topic, days):
    all_results = []
    seen_urls = set()

    base_queries = [
        topic,
        f"{topic} AND startup",
        f"{topic} AND acquisition OR merger OR funding",
        f"{topic} AND CEO OR executive OR leadership",
        f"{topic} AND venture capital OR Series A OR Series B",
        f"{topic} AND government grant OR approval OR contract",
        f"{topic} AND underrated OR small-cap OR micro-cap"
    ]

    investor_queries = [
        f"{topic} AND BlackRock OR Vanguard OR SoftBank",
        f"{topic} AND Elon Musk OR Sam Altman OR Peter Thiel",
        f"{topic} AND Berkshire Hathaway OR Warren Buffett",
        f"{topic} AND institutional investor OR hedge fund",
    ]

    related_terms = get_related_terms(topic)
    synonym_queries = [f"{term} AND {kw}" for term in related_terms for kw in ["startup", "funding", "merger", "acquisition"]]

    all_queries = base_queries + investor_queries + synonym_queries

    for query in all_queries:
        try:
            print(f"🔍 Tavily query: {query}")
            response = tavily_search(query, days)

            if not isinstance(response, dict) or "results" not in response:
                print(f"⚠️ Tavily API response issue: {response}")
                continue

            for item in response.get("results", []):
                url = item.get("url")
                content = item.get("content", "") or item.get("summary", "") or item.get("title", "")
                if url and url not in seen_urls and len(content) > 150:
                    all_results.append({
                        "title": item.get("title"),
                        "url": url,
                        "content": content
                    })
                    seen_urls.add(url)

        except Exception as e:
            print(f"⚠️ Tavily request failed for query '{query}': {e}")

    print(f"📰 Total articles collected: {len(all_results)}")
    return all_results

# === Generate Markdown Report ===
def generate_value_investor_report(topic, news_results, max_articles=20, max_chars_per_article=400):
    news_results = news_results[:max_articles]

    for item in news_results:
        result = analyze_article(item["content"])
        item["fin_sentiment"] = result.get("sentiment", "neutral")
        item["fin_confidence"] = result.get("confidence", 0.0)
        item["investment_decision"] = result.get("investment_decision", "Watch")

    article_summary = "".join(
        f"- **{item['title']}**: {item['content'][:max_chars_per_article]}... "
        f"(Sentiment: {item['fin_sentiment'].title()}, Confidence: {item['fin_confidence']:.2f}, "
        f"Decision: {item['investment_decision']}) [link]({item['url']})\n"
        for item in news_results
    )

    prompt = PromptTemplate.from_template("""
You're a highly focused value investor. Analyze this week's news on "{Topic}".

Your goal is to uncover:
- Meaningful events (e.g., CEO joining a startup, insider buys, big-name partnerships)
- Startups or small caps that may signal undervalued opportunity
- Connections to key individuals or institutions (e.g., Elon Musk investing, Sam Altman joining)
- Companies with strong fundamentals: low P/E, low P/B, high ROE, recent IPOs, moats, or high free cash flow

### News
{ArticleSummaries}

Write a markdown memo with:
1. **Key Value Signals**
2. **Stocks or Startups to Watch**
3. **What Smart Money Might Be Acting On**
4. **References**
5. **Investment Hypothesis**

Include context and macroeconomic/regulatory angles. Add an intro on sentiment and market trends for the week.
""")

    chat_prompt = ChatPromptTemplate.from_messages([
        SystemMessagePromptTemplate(prompt=prompt)
    ])
    prompt_value = chat_prompt.format_prompt(
        Topic=topic,
        ArticleSummaries=article_summary
    ).to_messages()

    llm = get_llm()
    result = llm.invoke(prompt_value)
    return result.content