57 lines
2.8 KiB
Python
Executable File
57 lines
2.8 KiB
Python
Executable File
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"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision for the **Short Term (1-2 Weeks)**: align with the bear analyst, the bull analyst, or choose Hold.
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Summarize the key points from both sides concisely, focusing on the most compelling evidence for immediate price action. Your recommendation—**Long, Short, or Hold**—must be clear and actionable. Avoid defaulting to Hold simply because both sides have valid points; commit to a stance grounded in the debate's strongest short-term arguments.
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Additionally, develop a detailed short-term investment plan for the trader. This should include:
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Your Recommendation: A decisive stance (Long/Short/Hold) supported by the most convincing arguments for the next 2 weeks.
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Rationale: An explanation of why these arguments lead to your conclusion.
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Strategic Actions: Concrete steps for implementing the recommendation (e.g., entry zones, stop losses).
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Take into account your past mistakes on similar situations. Use these insights to refine your decision-making. Present your analysis conversationally, as if speaking naturally, without special formatting.
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Here are your past reflections on mistakes:
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\"{past_memory_str}\"
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Here is the debate:
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Debate History:
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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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