71 lines
2.9 KiB
Python
71 lines
2.9 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_utils import get_news, get_global_news
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def create_news_analyst(llm, config):
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"""Create the news analyst node with language support."""
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def news_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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tools = [
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get_news,
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get_global_news,
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]
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language = config["output_language"]
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language_prompts = {
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"en": "",
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"zh-tw": "Use Traditional Chinese as the output.",
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"zh-cn": "Use Simplified Chinese as the output.",
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}
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language_prompt = language_prompts.get(language, "")
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system_message = (
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f"""
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You are a senior news and macroeconomic researcher. Your job is to analyze major global and regional news and macroeconomic trends over the past 7 days that are relevant for trading and investment decisions.
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Use the available tools to search for company-specific and global macro news:
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- get_news(query, start_date, end_date) → targeted or company-level analysis
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- get_global_news(curr_date, look_back_days, limit) → broad macroeconomic overview
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Your report must be data-driven, concise, and actionable — highlight causal relationships, policy context, and potential market implications.
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Avoid generic phrases like 'trends are mixed'; instead, quantify or explain the drivers behind market sentiment changes.
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Conclude with a Markdown table summarizing the most important insights (region / driver / potential market impact).
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"""
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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f"""
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You are a helpful AI assistant collaborating with other domain experts.
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Use the provided tools to make concrete progress toward the analysis goal.
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If the deliverable includes a final trading stance (BUY/HOLD/SELL), prefix your message clearly with:
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FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**
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You have access to the following tools: {tools}.
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{system_message}
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Current date: {current_date} | Target company: {ticker}
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Output language: ***{language_prompt}***
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"""
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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report = ""
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if len(result.tool_calls) == 0:
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report = result.content
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return {
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"messages": [result],
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"news_report": report,
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}
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return news_analyst_node
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