86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
# -*- coding: utf-8 -*-
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import time
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import json
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from tradingagents.agents.utils.output_filter import fix_common_llm_errors, validate_and_warn
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from tradingagents.agents.utils.prompts import get_risk_manager_prompt
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def create_risk_manager(llm, memory, language: str = "zh-TW"):
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"""
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建立一個風險管理員（裁判）節點。
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Args:
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llm: 用於生成決策的語言模型。
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memory: 儲存過去情況和反思的記憶體物件。
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language: 報告語言 ('en' 或 'zh-TW')
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Returns:
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function: 一個代表風險管理員節點的函式。
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"""
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def risk_manager_node(state) -> dict:
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"""風險管理員節點的執行函式。"""
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company_name = state["company_of_interest"]
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risk_debate_state = state["risk_debate_state"]
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history = risk_debate_state["history"]
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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["fundamentals_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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recommendation = rec["recommendation"]
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past_memory_str += recommendation + "\n\n"
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# Get language-specific prompt
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base_prompt = get_risk_manager_prompt(language)
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if language == "en":
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prompt = f"""{base_prompt}
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【Available Information】
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- Past Reflections: "{past_memory_str}"
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- Trader Plan: {trader_plan}
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- Debate History: {history}
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Please provide your risk management decision report."""
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else:
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prompt = f"""{base_prompt}
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【可用資訊】
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- 過去反思："{past_memory_str}"
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- 交易員計畫：{trader_plan}
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- 辯論歷史：{history}
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請提供您的風險管理決策報告。"""
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response = llm.invoke(prompt)
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response.content = fix_common_llm_errors(response.content)
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validate_and_warn(response.content, "Risk_Manager")
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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 |