89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
import time
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import json
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def create_neutral_debator(llm):
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def neutral_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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neutral_history = risk_debate_state.get("neutral_history", "")
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current_risky_response = risk_debate_state.get("current_risky_response", "")
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current_safe_response = risk_debate_state.get("current_safe_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 Neutral Trade Reviewer. Your job is to sanity-check the trade with a realistic base case (5-14 days).
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## CORE RULES (CRITICAL)
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- Evaluate this ticker IN ISOLATION (no portfolio sizing, no portfolio impact).
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- Use ONLY the provided reports and the trader plan as evidence.
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- Focus on what is most likely to happen next and whether the setup is actually tradeable (clear entry/stop/target).
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## OUTPUT STRUCTURE (MANDATORY)
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### Stance
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Choose BUY or SELL (no HOLD). If the edge is unclear, pick the less-bad side and keep the reasoning explicit.
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### Base-Case Setup
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- Entry: [price/condition]
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- Stop: [price] ([%] risk)
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- Target: [price] ([%] reward)
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- Risk/Reward: [ratio]
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### Base-Case View
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- Most likely outcome in 5-14 days: [up / down / range]
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- Why: [2 bullets max, data-backed]
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### Adjustments
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- [1-2 concrete improvements to entry/stop/target or timing]
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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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**SAFE ARGUMENT:**
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{current_safe_response}
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**If no other arguments yet:** Provide a simple base-case view using only the provided data."""
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response = llm.invoke(prompt)
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argument = f"Neutral Analyst: {response.content}"
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new_risk_debate_state = {
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"history": history + "\n" + argument,
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"risky_history": risk_debate_state.get("risky_history", ""),
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"safe_history": risk_debate_state.get("safe_history", ""),
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"neutral_history": neutral_history + "\n" + argument,
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"latest_speaker": "Neutral",
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"current_risky_response": risk_debate_state.get(
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"current_risky_response", ""
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),
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"current_safe_response": risk_debate_state.get("current_safe_response", ""),
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"current_neutral_response": argument,
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"count": risk_debate_state["count"] + 1,
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}
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return {"risk_debate_state": new_risk_debate_state}
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return neutral_node
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