73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
from tradingagents.agents.utils.agent_utils import build_instrument_context, get_memory_matches
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from tradingagents.schemas import build_decision_output_instructions, ensure_structured_decision_json
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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(
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state["company_of_interest"],
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state.get("instrument_profile"),
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)
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history = state["investment_debate_state"].get("history", "")
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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investment_debate_state = state["investment_debate_state"]
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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 = get_memory_matches(memory, curr_situation)
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past_memory_str = ""
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for rec in past_memories:
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past_memory_str += rec["recommendation"] + "\n\n"
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prompt = f"""As the research manager and evidence arbiter, critically evaluate the bull and bear debate and produce a structured investment view for the trader.
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{instrument_context}
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Your job:
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- weigh the strongest bullish and bearish evidence
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- reduce action bias; use NO_TRADE when the evidence is too weak or conflicted
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- focus on evidence arbitration rather than rhetorical style
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- make the catalysts and invalidators explicit
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Use these objective reports for grounding:
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Market Report:
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{market_research_report}
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Sentiment Report:
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{sentiment_report}
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News Report:
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{news_report}
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Fundamentals Report:
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{fundamentals_report}
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Debate History:
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{history}
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Lessons from past mistakes:
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{past_memory_str or "No past reflections available."}
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{build_decision_output_instructions("research manager investment plan")}"""
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response = llm.invoke(prompt)
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decision_json = ensure_structured_decision_json(response.content)
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new_investment_debate_state = {
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"judge_decision": decision_json,
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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": decision_json,
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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": decision_json,
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}
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return research_manager_node
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