86 lines
4.7 KiB
Python
86 lines
4.7 KiB
Python
from langchain_core.messages import AIMessage
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import time
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import json
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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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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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# 根据配置选择语言
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config = getattr(memory, 'config', {})
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if config.get("output_language", "english") == "chinese":
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prompt = f"""你是一个看跌分析师，提出反对投资该股票的案例。你的目标是提出一个理由充分的论点，强调风险、挑战和负面指标。利用提供的研究和数据来突出潜在的下行风险并有效反驳看涨论点。
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重点关注的要点：
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- 风险和挑战：突出可能阻碍股票表现的因素，如市场饱和、财务不稳定或宏观经济威胁。
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- 竞争劣势：强调弱点，如较弱的市场地位、创新下降或来自竞争对手的威胁。
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- 负面指标：使用来自财务数据、市场趋势或最近不利新闻的证据来支持你的立场。
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- 看涨反驳：用具体数据和合理推理批判性地分析看涨论点，暴露弱点或过于乐观的假设。
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- 参与度：以对话风格呈现你的论点，直接与看涨分析师的观点互动并有效辩论，而不是简单地列出事实。
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可用资源：
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市场研究报告：{market_research_report}
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社交媒体情绪报告：{sentiment_report}
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最新世界事务新闻：{news_report}
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公司基本面报告：{fundamentals_report}
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辩论对话历史：{history}
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最后的看涨论点：{current_response}
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类似情况的反思和经验教训：{past_memory_str}
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使用这些信息提供令人信服的看跌论点，反驳看涨声明，并参与展示投资该股票风险和弱点的动态辩论。你还必须解决反思并从过去犯的错误和经验教训中学习。"""
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else:
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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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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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