63 lines
3.2 KiB
Python
63 lines
3.2 KiB
Python
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from tradingagents.agents.utils.strategy_utils import get_signal_section
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def create_bear_researcher(llm, memory):
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def bear_node(state) -> dict:
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investment_debate_state = state["investment_debate_state"]
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history = investment_debate_state.get("history", "")
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bear_history = investment_debate_state.get("bear_history", "")
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current_response = investment_debate_state.get("current_response", "")
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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signal_section = get_signal_section(state, "researcher")
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curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=2)
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past_memory_str = ""
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for i, rec in enumerate(past_memories, 1):
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past_memory_str += rec["recommendation"] + "\n\n"
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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.
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Key points to focus on:
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- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance.
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- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors.
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- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position.
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- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions.
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- Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts.
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Resources available:
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Market research report: {market_research_report}
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Social media sentiment report: {sentiment_report}
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Latest world affairs news: {news_report}
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Company fundamentals report: {fundamentals_report}
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Conversation history of the debate: {history}
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Last bull argument: {current_response}
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Reflections from similar situations and lessons learned: {past_memory_str}
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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. You must also address reflections and learn from lessons and mistakes you made in the past.
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{signal_section}"""
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response = llm.invoke(prompt)
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argument = f"Bear Analyst: {response.content}"
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new_investment_debate_state = {
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"history": history + "\n" + argument,
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"bear_history": bear_history + "\n" + argument,
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"bull_history": investment_debate_state.get("bull_history", ""),
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"current_response": argument,
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"count": investment_debate_state["count"] + 1,
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}
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return {"investment_debate_state": new_investment_debate_state}
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return bear_node
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