82 lines
3.8 KiB
Python
82 lines
3.8 KiB
Python
from langchain_core.messages import AIMessage
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import time
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import json
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from tradingagents.agents.utils.schemas import ConfidenceOutput
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def create_bear_researcher(llm, memory):
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# Bind structured output
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structured_llm = llm.with_structured_output(ConfidenceOutput)
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def bear_node(state) -> dict:
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investment_debate_state = state["investment_debate_state"]
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history = investment_debate_state.get("history", "")
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bear_history = investment_debate_state.get("bear_history", "")
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current_response = investment_debate_state.get("current_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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curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=2)
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past_memory_str = ""
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for i, rec in enumerate(past_memories, 1):
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past_memory_str += rec["recommendation"] + "\n\n"
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prompt = f"""ROLE: Hostile Bearish Litigator.
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OBJECTIVE: Win the debate by destroying the Bull case.
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STYLE: Aggressive, data-driven, direct. NO "I agree with my colleague." NO politeness.
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INSTRUCTIONS:
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1. Expose Risks: Highlight failure points, debt loads, and macro headwinds.
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2. Attack Bull Points: If Bull cites "growth," cite "saturation" and "valuation bubble."
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3. Evidence First: Every claim must cite specific data points.
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WARNING: You will be Fact-Checked. If you lie about numbers, the Trade will be REJECTED.
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Key points to focus on:
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- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance.
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- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors.
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- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position.
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- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions.
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- Engagement: Present your argument in a direct, adversarial style, refuting the bull analyst's points with data.
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Resources available:
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Market research report: {market_research_report}
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Social media sentiment report: {sentiment_report}
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Latest world affairs news: {news_report}
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Company fundamentals report: {fundamentals_report}
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Conversation history of the debate: {history}
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Last bull argument: {current_response}
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Reflections from similar situations and lessons learned: {past_memory_str}
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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 stock. You must also address reflections and learn from lessons and mistakes you made in the past.
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WARNING: You must provide a clear rationale and a numeric confidence score (0.0 to 1.0).
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"""
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# Call structured LLM
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result = structured_llm.invoke(prompt)
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argument = f"Bear Analyst: {result.rationale}"
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confidence = result.confidence
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new_investment_debate_state = {
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"history": history + "\n" + argument,
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"bear_history": bear_history + "\n" + argument,
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"bull_history": investment_debate_state.get("bull_history", ""),
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"current_response": argument,
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"count": investment_debate_state["count"] + 1,
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"confidence": confidence # Local confidence
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}
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return {
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"investment_debate_state": new_investment_debate_state,
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"bear_confidence": confidence # Global floor for Gatekeeper
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}
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return bear_node
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