91 lines
4.6 KiB
Python
91 lines
4.6 KiB
Python
import time
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import json
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def create_risk_manager(llm, memory):
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def risk_manager_node(state) -> dict:
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company_name = state["company_of_interest"]
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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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market_research_report = state["market_report"]
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news_report = state["news_report"]
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fundamentals_report = state["news_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"{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"""作为风险管理法官和辩论主持人，你的目标是评估三位风险分析师——激进型、中性型和保守型——之间的辩论，并确定交易员的最佳行动方案。你的决定必须产生明确的建议：买入、卖出或持有。只有在基于具体论点有强有力理由时才选择持有，而不是当所有方面看起来都有效时的退路。力求清晰和果断。
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决策指导原则：
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1. **总结关键论点**：从每位分析师那里提取最强有力的观点，专注于与背景相关的内容。
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2. **提供理由**：用辩论中的直接引用和反驳来支持你的建议。
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3. **完善交易员计划**：从交易员的原始计划**{trader_plan}**开始，根据分析师的洞察进行调整。
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4. **从过去错误中学习**：利用**{past_memory_str}**的经验教训来解决先前的误判，改进你现在做出的决定，确保不会做出错误的买入/卖出/持有决定而导致损失。
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可交付成果：
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- 明确且可行的建议：买入、卖出或持有。
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- 基于辩论和过去反思的详细推理。
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---
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**分析师辩论历史：**
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{history}
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---
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专注于可行的洞察和持续改进。基于过去的经验教训，批判性地评估所有观点，确保每个决定都能推进更好的结果。"""
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else:
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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—Risky, Neutral, and Safe/Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold 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 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 BUY/SELL/HOLD call that loses money.
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Deliverables:
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- A clear and actionable recommendation: Buy, Sell, or Hold.
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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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"risky_history": risk_debate_state["risky_history"],
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"safe_history": risk_debate_state["safe_history"],
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"neutral_history": risk_debate_state["neutral_history"],
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"latest_speaker": "Judge",
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"current_risky_response": risk_debate_state["current_risky_response"],
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"current_safe_response": risk_debate_state["current_safe_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_trade_decision": response.content,
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}
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return risk_manager_node
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