72 lines
3.0 KiB
Python
72 lines
3.0 KiB
Python
from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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get_language_instruction,
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get_memory_matches,
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)
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from tradingagents.schemas import build_decision_output_instructions, ensure_structured_decision_json
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def create_portfolio_manager(llm, memory):
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def portfolio_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["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["fundamentals_report"]
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sentiment_report = state["sentiment_report"]
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research_plan = state["investment_plan"]
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trader_plan = state["trader_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 = 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 Portfolio Manager, synthesize the risk analysts' debate and deliver the final trading decision.
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{instrument_context}
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Use the common decision schema and be explicit about rating, confidence, time horizon, entry logic, exit logic, position sizing, risk limits, catalysts, and invalidators.
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NO_TRADE is allowed and should be used whenever the setup is not compelling enough to allocate capital.
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Context:
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- Research Manager investment plan JSON: {research_plan}
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- Trader execution plan JSON: {trader_plan}
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- Lessons from past decisions: {past_memory_str or "No past reflections available."}
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Risk Analysts Debate History:
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{history}
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Ground every conclusion in specific evidence from the analysts. {get_language_instruction()}
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{build_decision_output_instructions("portfolio manager final decision")}"""
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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_risk_debate_state = {
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"judge_decision": decision_json,
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"history": risk_debate_state["history"],
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"aggressive_history": risk_debate_state["aggressive_history"],
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"conservative_history": risk_debate_state["conservative_history"],
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"neutral_history": risk_debate_state["neutral_history"],
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"latest_speaker": "Judge",
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"current_aggressive_response": risk_debate_state["current_aggressive_response"],
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"current_conservative_response": risk_debate_state["current_conservative_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": decision_json,
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}
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return portfolio_manager_node
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