def create_bear_researcher(llm): def bear_node(state) -> dict: investment_debate_state = state["investment_debate_state"] history = investment_debate_state.get("history", "") bear_history = investment_debate_state.get("bear_history", "") current_response = investment_debate_state.get("current_response", "") market_research_report = state["market_report"] sentiment_report = state["sentiment_report"] news_report = state["news_report"] fundamentals_report = state["fundamentals_report"] prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively. Key points to focus on: - Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance. - Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors. - Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position. - Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions. - Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts. Resources available: Market research report: {market_research_report} Social media sentiment report: {sentiment_report} Latest world affairs news: {news_report} Company fundamentals report: {fundamentals_report} Conversation history of the debate: {history} Last bull argument: {current_response} Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. """ response = llm.invoke(prompt) argument = f"Bear Analyst: {response.content}" new_investment_debate_state = { "history": history + "\n" + argument, "bear_history": bear_history + "\n" + argument, "bull_history": investment_debate_state.get("bull_history", ""), "current_response": argument, "count": investment_debate_state["count"] + 1, } return {"investment_debate_state": new_investment_debate_state} return bear_node