from tradingagents.agents.utils.agent_utils import update_risk_debate_state from tradingagents.agents.utils.llm_utils import parse_llm_response def create_risky_debator(llm): def risky_node(state) -> dict: risk_debate_state = state["risk_debate_state"] history = risk_debate_state.get("history", "") current_safe_response = risk_debate_state.get("current_safe_response", "") current_neutral_response = risk_debate_state.get("current_neutral_response", "") market_research_report = state["market_report"] sentiment_report = state["sentiment_report"] news_report = state["news_report"] fundamentals_report = state["fundamentals_report"] trader_decision = state["trader_investment_plan"] prompt = f"""You are the Aggressive Trade Reviewer. Your job is to push for taking the trade if there is a short-term edge (5-14 days). ## CORE RULES (CRITICAL) - Evaluate this ticker IN ISOLATION (no portfolio sizing, no portfolio impact). - Use ONLY the provided reports and the trader plan as evidence. - Focus on the upside path: what must happen for this to work, and how to structure the trade to capture it. ## OUTPUT STRUCTURE (MANDATORY) ### Stance State whether you agree with the Trader's direction (BUY/SELL) or flip it (no HOLD). ### Best-Case Setup - Entry: [price/condition] - Stop: [price] ([%] risk) - Target: [price] ([%] reward) - Risk/Reward: [ratio] ### Why This Can Work Soon - [3 bullets max: catalyst + technical + sentiment/news/fundamentals, all from provided data] ### Counters (Brief) - Respond to the Safe and Neutral critiques with 1-2 data-backed points each. --- **TRADER'S PLAN:** {trader_decision} **YOUR TASK:** Argue why this plan should be executed with conviction and clear triggers. **MARKET DATA:** - Technical: {market_research_report} - Sentiment: {sentiment_report} - News: {news_report} - Fundamentals: {fundamentals_report} **DEBATE HISTORY:** {history} **CONSERVATIVE ARGUMENT:** {current_safe_response} **NEUTRAL ARGUMENT:** {current_neutral_response} **If no other arguments yet:** Present your strongest case for why this trade can work soon, using only the provided data.""" response = llm.invoke(prompt) response_text = parse_llm_response(response.content) argument = f"Risky Analyst: {response_text}" return {"risk_debate_state": update_risk_debate_state(risk_debate_state, argument, "Risky")} return risky_node