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
3.7 KiB
Python
62 lines
3.7 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_conservative_debator(llm):
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def conservative_node(state) -> dict:
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risk_debate_state = state["risk_debate_state"]
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history = risk_debate_state.get("history", "")
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conservative_history = risk_debate_state.get("conservative_history", "")
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current_aggressive_response = risk_debate_state.get("current_aggressive_response", "")
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current_neutral_response = risk_debate_state.get("current_neutral_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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trader_decision = state["trader_investment_plan"]
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prompt = f"""As the Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility. When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains. Here is the trader's decision:
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{trader_decision}
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Your task is to actively counter the arguments of the Aggressive and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability. Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision:
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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 Report: {news_report}
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Company Fundamentals Report: {fundamentals_report}
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Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
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Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting.""" + get_language_instruction()
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response = llm.invoke(prompt)
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argument = f"Conservative Analyst: {response.content}"
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new_risk_debate_state = {
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"history": history + "\n" + argument,
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"aggressive_history": risk_debate_state.get("aggressive_history", ""),
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"conservative_history": conservative_history + "\n" + argument,
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"neutral_history": risk_debate_state.get("neutral_history", ""),
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"latest_speaker": "Conservative",
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"current_aggressive_response": risk_debate_state.get(
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"current_aggressive_response", ""
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),
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"current_conservative_response": argument,
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"current_neutral_response": risk_debate_state.get(
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"current_neutral_response", ""
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),
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"count": risk_debate_state["count"] + 1,
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}
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return {"risk_debate_state": new_risk_debate_state}
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return conservative_node
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