56 lines
2.5 KiB
Python
56 lines
2.5 KiB
Python
import time
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import json
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def create_research_manager(llm, memory):
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def research_manager_node(state) -> dict:
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history = state["investment_debate_state"].get("history", "")
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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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investment_debate_state = state["investment_debate_state"]
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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"""作为投资组合经理和辩论主持人，您的角色是批判性地评估本轮辩论并做出明确的决定：与空头分析师、多头分析师保持一致，或者仅在有充分理由支持的情况下选择持有。
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简明扼要地总结双方的要点，重点关注最有说服力的证据或推理。您的建议——买入、卖出或持有——必须清晰且可操作。避免仅仅因为双方都有道理就默认持有；致力于基于辩论中最有力论点的立场。
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此外，为交易员制定详细的投资计划。这应包括：
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您的建议：一个由最有说服力的论点支持的果断立场。
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基本原理：解释为什么这些论点会得出您的结论。
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战略行动：实施建议的具体步骤。
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考虑您过去在类似情况下的错误。利用这些见解来完善您的决策，并确保您正在学习和进步。以对话方式呈现您的分析，就像自然说话一样，无需特殊格式。
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以下是您过去对错误的思考：
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\"{past_memory_str}\"
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这是辩论：
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辩论历史：
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{history}"""
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response = llm.invoke(prompt)
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new_investment_debate_state = {
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"judge_decision": response.content,
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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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"bull_history": investment_debate_state.get("bull_history", ""),
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"current_response": response.content,
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"count": investment_debate_state["count"],
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}
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return {
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"investment_debate_state": new_investment_debate_state,
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"investment_plan": response.content,
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}
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return research_manager_node
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