import time
import json
from tradingagents.agents.utils.korean_prompt import (
    KOREAN_INVESTOR_GUIDE,
    KOREAN_DEBATE_GUIDE,
    KOREAN_FINAL_DECISION_GUIDE,
)


def create_risk_manager(llm, memory):
    def risk_manager_node(state) -> dict:

        company_name = state["company_of_interest"]

        history = state["risk_debate_state"]["history"]
        risk_debate_state = state["risk_debate_state"]
        market_research_report = state["market_report"]
        news_report = state["news_report"]
        fundamentals_report = state["fundamentals_report"]
        sentiment_report = state["sentiment_report"]
        trader_plan = state["investment_plan"]

        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
        past_memories = memory.get_memories(curr_situation, n_matches=2)

        past_memory_str = ""
        for i, rec in enumerate(past_memories, 1):
            past_memory_str += rec["recommendation"] + "\n\n"

        prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Aggressive, Neutral, and Conservative—and determine whether to ENTER a NEW long position in this stock.

Context: There is currently NO existing position. The trader is evaluating a fresh entry. The only valid decisions are:
- **BUY**: Enter a new long position now — the risk/reward is favorable for a new entry.
- **PASS**: Do not enter — the risks are too high or the timing is wrong; skip this trade.

SELL is NOT a valid option since there is no existing position.

Guidelines for Decision-Making:
1. **Summarize Key Arguments**: Extract the strongest points from each analyst about whether this is a good entry point, focusing on entry risk and reward.
2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate.
3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights about entry risk.
4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/PASS call.

Deliverables:
- A clear and actionable recommendation: Buy or Pass.
- Detailed reasoning anchored in the debate and past reflections.

---

**Analysts Debate History:**  
{history}

---

Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes.
{KOREAN_INVESTOR_GUIDE}
{KOREAN_DEBATE_GUIDE}
{KOREAN_FINAL_DECISION_GUIDE}
"""

        response = llm.invoke(prompt)

        new_risk_debate_state = {
            "judge_decision": response.content,
            "history": risk_debate_state["history"],
            "aggressive_history": risk_debate_state["aggressive_history"],
            "conservative_history": risk_debate_state["conservative_history"],
            "neutral_history": risk_debate_state["neutral_history"],
            "latest_speaker": "Judge",
            "current_aggressive_response": risk_debate_state["current_aggressive_response"],
            "current_conservative_response": risk_debate_state["current_conservative_response"],
            "current_neutral_response": risk_debate_state["current_neutral_response"],
            "count": risk_debate_state["count"],
        }

        return {
            "risk_debate_state": new_risk_debate_state,
            "final_trade_decision": response.content,
        }

    return risk_manager_node