feat: add Chief Analyst agent with structured LLM output

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Ali AL OGAILI 2026-03-24 14:07:30 +01:00
parent c9cbed8ad3
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from tradingagents.agents.utils.agent_states import ChiefAnalystReport
def create_chief_analyst(llm):
"""Create the Chief Analyst node — final synthesis step of the pipeline.
Uses structured output to produce a validated 4-section executive summary.
Returns {"chief_analyst_report": dict} to be stored in AgentState.
"""
structured_llm = llm.with_structured_output(ChiefAnalystReport)
def chief_analyst_node(state) -> dict:
company = state["company_of_interest"]
trade_date = state["trade_date"]
market_report = state.get("market_report", "")
sentiment_report = state.get("sentiment_report", "")
news_report = state.get("news_report", "")
fundamentals_report = state.get("fundamentals_report", "")
investment_plan = state.get("investment_plan", "")
trader_plan = state.get("trader_investment_plan", "")
final_decision = state.get("final_trade_decision", "")
prompt = f"""You are the Chief Analyst. You have received the outputs of a full multi-agent analysis pipeline for {company} on {trade_date}. Synthesize the key findings into a concise executive summary.
## Market Analysis
{market_report}
## Sentiment Analysis
{sentiment_report}
## News Analysis
{news_report}
## Fundamentals Analysis
{fundamentals_report}
## Research Investment Plan (Bull/Bear synthesis)
{investment_plan}
## Trader's Plan
{trader_plan}
## Risk Judge's Final Decision
{final_decision}
---
Produce a concise executive summary with exactly these four fields:
- verdict: The final trade decision must be exactly "BUY", "SELL", or "HOLD".
- catalyst: The 13 strongest data points driving this verdict (drawn from market, news, or fundamentals). Be specific and concrete.
- execution: A brief summary of the trader's entry/exit strategy. What is the plan?
- tail_risk: The single most significant unmitigated risk identified by the Risk Judge. What could make this trade go wrong?
Be decisive. Be concise. Do not hedge."""
report: ChiefAnalystReport = structured_llm.invoke(prompt)
return {"chief_analyst_report": report.model_dump()}
return chief_analyst_node