97 lines
4.4 KiB
Python
97 lines
4.4 KiB
Python
import time
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import json
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from tradingagents.agents.utils.agent_utils import (
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build_debate_brief,
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build_instrument_context,
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extract_feedback_snapshot,
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get_language_instruction,
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get_snapshot_template,
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get_snapshot_writing_instruction,
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localize_label,
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localize_rating_term,
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localize_role_name,
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truncate_for_prompt,
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)
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def create_research_manager(llm, memory):
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def research_manager_node(state) -> dict:
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instrument_context = build_instrument_context(state["company_of_interest"])
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market_research_report = truncate_for_prompt(state["market_report"])
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sentiment_report = truncate_for_prompt(state["sentiment_report"])
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news_report = truncate_for_prompt(state["news_report"])
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fundamentals_report = truncate_for_prompt(state["fundamentals_report"])
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investment_debate_state = state["investment_debate_state"]
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bull_snapshot = investment_debate_state.get("bull_snapshot", "")
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bear_snapshot = investment_debate_state.get("bear_snapshot", "")
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debate_brief = investment_debate_state.get("debate_brief", "")
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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"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the {localize_role_name("Bear Analyst")}, the {localize_role_name("Bull Analyst")}, or choose {localize_rating_term("Hold")} only if it is strongly justified based on the arguments presented.
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Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation—{localize_rating_term("Buy")}, {localize_rating_term("Sell")}, or {localize_rating_term("Hold")}—must be clear and actionable. Avoid defaulting to {localize_rating_term("Hold")} simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments.
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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 supported by the most convincing arguments.
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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.
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Take into account your 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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After your analysis, append a feedback block in this exact format:
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{get_snapshot_template()}
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{get_snapshot_writing_instruction()}
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Here are your past reflections on mistakes:
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\"{past_memory_str}\"
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{instrument_context}
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Here is the latest debate context:
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{localize_label("Rolling debate brief:", "滚动辩论摘要:")}
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{debate_brief}
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{localize_label("Bull latest snapshot:", f"{localize_role_name('Bull Analyst')} 最新快照:")}
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{bull_snapshot}
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{localize_label("Bear latest snapshot:", f"{localize_role_name('Bear Analyst')} 最新快照:")}
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{bear_snapshot}{get_language_instruction()}
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"""
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response = llm.invoke(prompt)
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judge_snapshot = extract_feedback_snapshot(response.content)
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updated_brief = build_debate_brief(
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{
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"Bull Analyst": bull_snapshot,
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"Bear Analyst": bear_snapshot,
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"Research Manager": judge_snapshot,
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},
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latest_speaker="Research Manager",
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)
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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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"bear_history": investment_debate_state.get("bear_history", ""),
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"bull_history": investment_debate_state.get("bull_history", ""),
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"current_response": response.content,
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"bull_snapshot": bull_snapshot,
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"bear_snapshot": bear_snapshot,
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"debate_brief": updated_brief,
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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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