65 lines
3.2 KiB
Python
65 lines
3.2 KiB
Python
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def create_risk_manager(llm, memory):
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def risk_manager_node(state) -> dict:
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event_id = state["event_id"]
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history = state["risk_debate_state"]["history"]
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risk_debate_state = state["risk_debate_state"]
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odds_report = state["odds_report"]
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news_report = state["news_report"]
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event_report = state["event_report"]
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sentiment_report = state["sentiment_report"]
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trader_plan = state["investment_plan"]
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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 Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Aggressive, Neutral, and Conservative—and determine the best course of action for the prediction market trader. Your decision must result in a clear recommendation: YES (bet on event occurring), NO (bet against event occurring), or SKIP (no bet). Choose SKIP only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
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Guidelines for Decision-Making:
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1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the prediction market context.
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2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate.
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3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights.
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4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong YES/NO/SKIP call that loses money.
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Deliverables:
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- A clear and actionable recommendation: YES, NO, or SKIP.
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- Detailed reasoning anchored in the debate and past reflections.
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---
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**Analysts Debate History:**
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{history}
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---
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Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes."""
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response = llm.invoke(prompt)
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new_risk_debate_state = {
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"judge_decision": response.content,
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"history": risk_debate_state["history"],
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"aggressive_history": risk_debate_state["aggressive_history"],
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"conservative_history": risk_debate_state["conservative_history"],
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"neutral_history": risk_debate_state["neutral_history"],
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"latest_speaker": "Judge",
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"current_aggressive_response": risk_debate_state["current_aggressive_response"],
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"current_conservative_response": risk_debate_state["current_conservative_response"],
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"current_neutral_response": risk_debate_state["current_neutral_response"],
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"count": risk_debate_state["count"],
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}
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return {
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"risk_debate_state": new_risk_debate_state,
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"final_decision": response.content,
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}
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return risk_manager_node
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