Yijia-Xiao
d7b40a2a5c
Agents had no ground-truth ticker→company mapping, so the market analyst could pattern-match a price chart to the wrong company (e.g. TOTDY read as "TotalEnergies"), and every downstream agent inherited the bad framing. Resolve identity once at run start via a cached, fail-open yfinance lookup and inject company/sector/exchange into the shared instrument context that all twelve agents consume, with an explicit do-not-substitute instruction. Resolution runs on both the propagate() and CLI entry points. Also replaces the bare "Continue" message-clear placeholder, which some OpenAI-compatible providers interpreted as the user task, with a context-anchored placeholder carrying the resolved identity and date. #814 #888
62 lines
3.1 KiB
Python
62 lines
3.1 KiB
Python
from tradingagents.agents.utils.agent_utils import (
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get_instrument_context_from_state,
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get_language_instruction,
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)
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def create_bull_researcher(llm):
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def bull_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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bull_history = investment_debate_state.get("bull_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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instrument_context = get_instrument_context_from_state(state)
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asset_type = state.get("asset_type", "stock")
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target_label = "stock" if asset_type == "stock" else "asset"
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fundamentals_label = (
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"Company fundamentals report"
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if asset_type == "stock"
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else "Asset fundamentals report (may be unavailable for crypto)"
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)
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prompt = f"""You are a Bull Analyst advocating for investing in the {target_label}. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.
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Key points to focus on:
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- Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability.
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- Competitive Advantages: Emphasize factors like unique products, strong branding, or dominant market positioning.
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- Positive Indicators: Use financial health, industry trends, and recent positive news as evidence.
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- Bear Counterpoints: Critically analyze the bear argument with specific data and sound reasoning, addressing concerns thoroughly and showing why the bull perspective holds stronger merit.
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- Engagement: Present your argument in a conversational style, engaging directly with the bear analyst's points and debating effectively rather than just listing data.
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Resources available:
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{instrument_context}
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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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{fundamentals_label}: {fundamentals_report}
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Conversation history of the debate: {history}
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Last bear argument: {current_response}
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Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position.
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""" + get_language_instruction()
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response = llm.invoke(prompt)
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argument = f"Bull Analyst: {response.content}"
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new_investment_debate_state = {
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"history": history + "\n" + argument,
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"bull_history": bull_history + "\n" + argument,
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"bear_history": investment_debate_state.get("bear_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 bull_node
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