from tradingagents.agents.utils.agent_utils import build_instrument_context


def create_research_manager(llm):
    def research_manager_node(state) -> dict:
        instrument_context = build_instrument_context(state["company_of_interest"])
        history = state["investment_debate_state"].get("history", "")

        investment_debate_state = state["investment_debate_state"]

        prompt = f"""As the Research Manager and debate facilitator, your role is to critically evaluate this round of debate and deliver a clear, actionable investment plan for the trader.

{instrument_context}

---

**Rating Scale** (use exactly one):
- **Buy**: Strong conviction in the bull thesis; recommend taking or growing the position
- **Overweight**: Constructive view; recommend gradually increasing exposure
- **Hold**: Balanced view; recommend maintaining the current position
- **Underweight**: Cautious view; recommend trimming exposure
- **Sell**: Strong conviction in the bear thesis; recommend exiting or avoiding the position

Commit to a clear stance whenever the debate's strongest arguments warrant one; reserve Hold for situations where the evidence on both sides is genuinely balanced.

**Required Output Structure:**
1. **Recommendation**: State one of Buy / Overweight / Hold / Underweight / Sell.
2. **Rationale**: Summarise the key points from both sides and explain which arguments led to this recommendation.
3. **Strategic Actions**: Concrete steps for the trader to implement the recommendation, including position sizing guidance consistent with the rating.

Present your analysis conversationally, as if speaking naturally to a teammate.

---

**Debate History:**
{history}"""
        response = llm.invoke(prompt)

        new_investment_debate_state = {
            "judge_decision": response.content,
            "history": investment_debate_state.get("history", ""),
            "bear_history": investment_debate_state.get("bear_history", ""),
            "bull_history": investment_debate_state.get("bull_history", ""),
            "current_response": response.content,
            "count": investment_debate_state["count"],
        }

        return {
            "investment_debate_state": new_investment_debate_state,
            "investment_plan": response.content,
        }

    return research_manager_node