67 lines
3.0 KiB
Python
67 lines
3.0 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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prompt = f"""作为风险管理法官和辩论主持人，您的目标是评估三位风险分析师（风险、中立和安全/保守）之间的辩论，并确定交易员的最佳行动方案。您的决定必须产生明确的建议：买入、卖出或持有。仅当有特定论点充分证明时才选择持有，而不是在各方似乎都有效时的后备方案。力求清晰和果断。
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决策指南：
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1. **总结关键论点**：从每位分析师中提取最强有力的观点，重点关注与背景的相关性。
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2. **提供理由**：用辩论中的直接引述和反驳来支持您的建议。
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3. **完善交易员的计划**：从交易员的原始计划 **{trader_plan}** 开始，并根据分析师的见解进行调整。
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4. **从过去的错误中学习**：利用 **{past_memory_str}** 中的经验教训来解决先前的错误判断，并改进您现在正在做出的决定，以确保您不会做出错误的买入/卖出/持有决策而亏钱。
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可交付成果：
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- 清晰可行的建议：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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专注于可行的见解和持续改进。借鉴过去的经验教训，批判性地评估所有观点，并确保每项决策都能带来更好的结果。"""
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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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