from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from tradingagents.agents.analysts._claude_agent_runner import run_sdk_analyst from tradingagents.agents.utils.agent_utils import ( build_instrument_context, get_global_news, get_language_instruction, get_news, ) from tradingagents.dataflows.config import get_config from tradingagents.llm_clients.claude_agent_client import ChatClaudeAgent def create_news_analyst(llm): def news_analyst_node(state): current_date = state["trade_date"] instrument_context = build_instrument_context(state["company_of_interest"]) tools = [ get_news, get_global_news, ] system_message = ( "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Provide specific, actionable insights with supporting evidence to help traders make informed decisions." + """ 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.""" + get_language_instruction() ) 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: **BUY/HOLD/SELL** or deliverable," " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** 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}. {instrument_context}", ), 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(instrument_context=instrument_context) if isinstance(llm, ChatClaudeAgent): full_system = ( "You are a helpful AI assistant. Use the provided tools to progress towards " "producing the requested report. " f"You have access to the following tools: {', '.join(t.name for t in tools)}. " f"For your reference, the current date is {current_date}. {instrument_context}\n\n" f"{system_message}" ) return run_sdk_analyst( llm=llm, state=state, system_prompt=full_system, lc_tools=tools, server_name="news_analyst", report_field="news_report", ) 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