from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder import time import json from tradingagents.agents.utils.agent_utils import get_news, get_global_news from tradingagents.dataflows.config import get_config def create_news_analyst(llm): def news_analyst_node(state): current_date = state["trade_date"] ticker = state["company_of_interest"] tools = [ get_news, # get_global_news, ] system_message = ( "You are a News Analyst focused on IMMEDIATE market impact (1-2 weeks). " "Interpret breaking news and upcoming macro events (CPI, Fed, Geopolitics) to determine short-term direction. " "Focus on the 'Narrative' — what story is the market telling itself right now? " "Use the available tools: get_news(query, start_date, end_date) for company-specific news. " "Filter out noise and old news. Prioritize news that changes the short-term outlook." + "\n\nDECISION LOGIC:" + "\n- LONG: Distinct positive news cycle, analyst upgrades, or strong macro tailwinds." + "\n- SHORT: Distinct negative news cycle, lawsuits, regulatory hits, or macro headwinds." + "\n- HOLD: Mixed news, stale news, or high uncertainty waiting for a major event." + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""" + "\n\nYOU MUST CONCLUDE YOUR REPORT WITH: 'SIGNAL: [LONG/SHORT/HOLD]'" ) prompt = ChatPromptTemplate.from_messages( [ ( "system", "You are a helpful AI assistant, collaborating with other assistants." " Use the provided tools to progress towards answering the question." " If you are unable to fully answer, that's OK; another assistant with different tools" " will help where you left off. Execute what you can to make progress." " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **LONG/HOLD/SHORT** or deliverable," " prefix your response with FINAL TRANSACTION PROPOSAL: **LONG/HOLD/SHORT** so the team knows to stop." " You have access to the following tools: {tool_names}.\n{system_message}" "For your reference, the current date is {current_date}. We are looking at the company {ticker}", ), MessagesPlaceholder(variable_name="messages"), ] ) prompt = prompt.partial(system_message=system_message) prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) prompt = prompt.partial(current_date=current_date) prompt = prompt.partial(ticker=ticker) chain = prompt | llm.bind_tools(tools) result = chain.invoke(state["messages"]) report = "" if len(result.tool_calls) == 0: report = result.content return { "messages": [result], "news_report": report, } return news_analyst_node