TradingAgents/tradingagents/agents/risk_mgmt/neutral_debator.py

76 lines
2.3 KiB
Python

from tradingagents.agents.utils.agent_utils import update_risk_debate_state
from tradingagents.agents.utils.llm_utils import parse_llm_response
def create_neutral_debator(llm):
def neutral_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
current_risky_response = risk_debate_state.get("current_risky_response", "")
current_safe_response = risk_debate_state.get("current_safe_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 Neutral Trade Reviewer. Your job is to sanity-check the trade with a realistic base case (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 what is most likely to happen next and whether the setup is actually tradeable (clear entry/stop/target).
## OUTPUT STRUCTURE (MANDATORY)
### Stance
Choose BUY or SELL (no HOLD). If the edge is unclear, pick the less-bad side and keep the reasoning explicit.
### Base-Case Setup
- Entry: [price/condition]
- Stop: [price] ([%] risk)
- Target: [price] ([%] reward)
- Risk/Reward: [ratio]
### Base-Case View
- Most likely outcome in 5-14 days: [up / down / range]
- Why: [2 bullets max, data-backed]
### Adjustments
- [1-2 concrete improvements to entry/stop/target or timing]
---
**TRADER'S PLAN:**
{trader_decision}
**MARKET DATA:**
- Technical: {market_research_report}
- Sentiment: {sentiment_report}
- News: {news_report}
- Fundamentals: {fundamentals_report}
**DEBATE HISTORY:**
{history}
**AGGRESSIVE ARGUMENT:**
{current_risky_response}
**SAFE ARGUMENT:**
{current_safe_response}
**If no other arguments yet:** Provide a simple base-case view using only the provided data."""
response = llm.invoke(prompt)
response_text = parse_llm_response(response.content)
argument = f"Neutral Analyst: {response_text}"
return {
"risk_debate_state": update_risk_debate_state(risk_debate_state, argument, "Neutral")
}
return neutral_node