# Modified by TradingAgents-Studio contributors (2026) — see CHANGELOG.md
# Original: github.com/TauricResearch/TradingAgents (Apache License 2.0)

from tradingagents.agents.utils.agent_utils import get_language_instruction


def create_bear_researcher(llm):
    def bear_node(state) -> dict:
        investment_debate_state = state["investment_debate_state"]
        history = investment_debate_state.get("history", "")
        bear_history = investment_debate_state.get("bear_history", "")

        current_response = investment_debate_state.get("current_response", "")
        market_research_report = state["market_report"]
        sentiment_report = state["sentiment_report"]
        news_report = state["news_report"]
        fundamentals_report = state["fundamentals_report"]
        cn_sentiment_report = state.get("cn_sentiment_report", "")
        event_impact_report = state.get("event_impact_report", "")
        capital_flow_report = state.get("capital_flow_report", "")
        macro_report = state.get("macro_report", "")
        asset_type = state.get("asset_type", "stock")
        target_label = "stock" if asset_type == "stock" else "asset"
        fundamentals_label = (
            "Company fundamentals report"
            if asset_type == "stock"
            else "Asset fundamentals report (may be unavailable for crypto)"
        )

        extra_reports = ""
        if macro_report:
            extra_reports += f"\nMacro backdrop report: {macro_report}"
        if cn_sentiment_report:
            extra_reports += f"\nCN A-share sentiment report: {cn_sentiment_report}"
        if event_impact_report:
            extra_reports += f"\nEvent-driven impact report: {event_impact_report}"
        if capital_flow_report:
            extra_reports += f"\nCapital flow report (主力资金/北向/两融/龙虎榜): {capital_flow_report}"

        prompt = f"""You are a Bear Analyst making the case against investing in the {target_label}. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.

Key points to focus on:

- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance.
- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors.
- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position.
- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions.
- Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts.

Resources available:

Market research report: {market_research_report}
Social media sentiment report: {sentiment_report}
Latest world affairs news: {news_report}
{fundamentals_label}: {fundamentals_report}{extra_reports}
Conversation history of the debate: {history}
Last bull argument: {current_response}
Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the {target_label}.
""" + get_language_instruction()

        response = llm.invoke(prompt)

        argument = f"Bear Analyst: {response.content}"

        new_investment_debate_state = {
            "history": history + "\n" + argument,
            "bear_history": bear_history + "\n" + argument,
            "bull_history": investment_debate_state.get("bull_history", ""),
            "current_response": argument,
            "count": investment_debate_state["count"] + 1,
        }

        return {"investment_debate_state": new_investment_debate_state}

    return bear_node
