67 lines
3.4 KiB
Python
67 lines
3.4 KiB
Python
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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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odds_report = state["odds_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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event_report = state["event_report"]
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investment_debate_state = state["investment_debate_state"]
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curr_situation = f"{odds_report}\n\n{sentiment_report}\n\n{news_report}\n\n{event_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 research manager and debate judge, your role is to critically evaluate this 3-way debate between the YES Advocate, NO Advocate, and Timing Advocate, and make a definitive decision: YES, NO, or SKIP.
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Summarize the key points from all three sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation must be clear and actionable:
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- YES: Bet that the event will occur (buy YES shares).
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- NO: Bet that the event will not occur (buy NO shares).
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- SKIP: There is no meaningful edge at current prices — pass on this market entirely.
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Avoid defaulting to SKIP simply because all sides have valid points; commit to a stance grounded in the debate's strongest arguments and the actual market edge.
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Additionally, develop a detailed investment plan for the trader. This should include:
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Your Recommendation: A decisive stance (YES / NO / SKIP) supported by the most convincing arguments.
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Confidence Level: High / Medium / Low — reflecting your certainty in the recommendation.
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Estimated True Probability: Your best estimate of the actual probability of the YES outcome (e.g., "We estimate ~65% true probability vs. 50% implied by current odds").
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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 (entry price targets, position sizing, exit conditions).
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Take into account past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. 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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"yes_history": investment_debate_state.get("yes_history", ""),
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"no_history": investment_debate_state.get("no_history", ""),
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"timing_history": investment_debate_state.get("timing_history", ""),
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"current_yes_response": investment_debate_state.get("current_yes_response", ""),
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"current_no_response": investment_debate_state.get("current_no_response", ""),
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"current_timing_response": investment_debate_state.get("current_timing_response", ""),
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"latest_speaker": "Research Manager",
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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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