97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
from tradingagents.agents.utils.agent_utils import format_memory_context
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from tradingagents.agents.utils.llm_utils import create_and_invoke_chain, parse_llm_response
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def create_bear_researcher(llm, memory):
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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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past_memory_str = format_memory_context(memory, state)
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prompt = f"""You are the Bear Analyst making the case for SHORT-TERM SELL/AVOID (1-2 weeks).
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## YOUR OBJECTIVE
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Build an evidence-based bear case using ONLY data from the provided reports. Refute Bull claims with data.
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## STRUCTURE
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### Core Thesis (2-3 sentences)
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Why this is SELL/AVOID for short-term traders NOW.
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### Key Bearish Points (3-4 max)
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For each:
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- **Risk:** [Bearish argument]
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- **Evidence:** [Specific data from reports — cite numbers, dates, indicator values]
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- **Short-Term Impact:** [Impact in next 1-2 weeks]
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- **Strength:** [Strong/Moderate/Weak] based on evidence quality
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### Bull Rebuttals
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For EACH Bull claim:
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- **Bull Says:** "[Quote their specific claim]"
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- **Counter:** [Data-driven refutation — cite report data]
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- **Rebuttal Strength:** [Strong/Moderate/Weak]
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### Strengths I Acknowledge
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- [1-2 legitimate Bull points from the data]
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- [Why risks still dominate]
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## RULES
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- Every claim must cite specific data from the reports (numbers, dates, indicator values)
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- If data is unavailable to support a point, do not make that point
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- Do not exaggerate risks — be precise about probability and magnitude
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- Engage directly with Bull's arguments — don't ignore strong ones
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- Short-term focus: 1-2 weeks only
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---
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**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:**
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History: {history}
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Last Bull: {current_response}
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""" + (
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f"""
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## PAST LESSONS APPLICATION (Review BEFORE making arguments)
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{past_memory_str}
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**For each relevant past lesson:**
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1. **Similar Situation:** [What was similar?]
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2. **What Went Wrong/Right:** [Specific outcome]
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3. **How I'm Adjusting:** [Specific change to current argument based on lesson]
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4. **Impact on Conviction:** [Increases/Decreases/No change to conviction level]
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Apply lessons: How are you adjusting?"""
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if past_memory_str
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else ""
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)
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response = create_and_invoke_chain(llm, [], prompt, [])
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response_text = parse_llm_response(response.content)
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argument = f"Bear Analyst: {response_text}"
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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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}
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return {"investment_debate_state": new_investment_debate_state}
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return bear_node
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