75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
from tradingagents.agents.utils.agent_utils import update_risk_debate_state
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from tradingagents.agents.utils.llm_utils import parse_llm_response
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def create_safe_debator(llm):
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def safe_node(state) -> dict:
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risk_debate_state = state["risk_debate_state"]
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history = risk_debate_state.get("history", "")
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current_risky_response = risk_debate_state.get("current_risky_response", "")
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current_neutral_response = risk_debate_state.get("current_neutral_response", "")
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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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trader_decision = state["trader_investment_plan"]
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prompt = f"""You are the Risk Audit Reviewer. Your job is to find the fastest ways this trade fails (5-14 days) and tighten the setup if possible.
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## CORE RULES
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- Evaluate this ticker IN ISOLATION (no portfolio sizing or correlation analysis).
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- Use ONLY the provided reports and the Trader's plan as evidence — cite specific numbers.
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- You are not required to be conservative; you are required to be PRECISE about invalidation and risk.
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## OUTPUT STRUCTURE (MANDATORY)
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### Stance
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Choose BUY or SELL (no HOLD). If the setup looks poor, still pick the less-bad side and explain why the setup needs tightening.
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### Failure Modes (Top 3)
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For each, cite specific evidence:
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1. [Risk] — Evidence: [specific data point from reports] — What we'd see: [observable signal]
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2. [Risk] — Evidence: [specific data point] — What we'd see: [signal]
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3. [Risk] — Evidence: [specific data point] — What we'd see: [signal]
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### Invalidation & Risk Controls
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- **Invalidation trigger:** [specific price level or event that kills the thesis]
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- **Stop improvement:** [if Trader's stop is too loose/tight, suggest better level with rationale]
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- **Timing risk:** [what catalyst or event could flip this within the holding period]
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### Response to Aggressive/Neutral (1-2 bullets)
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- [Brief counter to their strongest point, with data]
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---
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**TRADER'S PLAN:**
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{trader_decision}
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**MARKET DATA:**
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- Technical: {market_research_report}
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- Sentiment: {sentiment_report}
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- News: {news_report}
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- Fundamentals: {fundamentals_report}
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**DEBATE HISTORY:**
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{history}
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**AGGRESSIVE ARGUMENT:**
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{current_risky_response}
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**NEUTRAL ARGUMENT:**
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{current_neutral_response}
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**If no other arguments yet:** Identify the top failure modes and invalidation points using only the provided data."""
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response = llm.invoke(prompt)
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response_text = parse_llm_response(response.content)
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argument = f"Safe Analyst: {response_text}"
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return {"risk_debate_state": update_risk_debate_state(risk_debate_state, argument, "Safe")}
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return safe_node
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