78 lines
3.6 KiB
Python
78 lines
3.6 KiB
Python
from datetime import datetime, timedelta
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from langchain_core.messages import HumanMessage
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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get_global_news,
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get_news,
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prefetch_tool_data,
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supports_tool_calling,
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)
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from tradingagents.dataflows.config import get_config
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def create_news_analyst(llm):
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def news_analyst_node(state):
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current_date = state["trade_date"]
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instrument_context = build_instrument_context(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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system_message = (
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"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."
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+ """ 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."""
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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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"You are a helpful AI assistant, collaborating with other assistants."
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" Use the provided tools to progress towards answering the question."
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" If you are unable to fully answer, that's OK; another assistant with different tools"
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" will help where you left off. Execute what you can to make progress."
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. {instrument_context}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(instrument_context=instrument_context)
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if supports_tool_calling():
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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else:
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ticker = state["company_of_interest"]
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start_date = (datetime.strptime(current_date, "%Y-%m-%d") - timedelta(days=7)).strftime("%Y-%m-%d")
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tool_data = prefetch_tool_data(tools, [
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{"ticker": ticker, "start_date": start_date, "end_date": current_date},
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{"curr_date": current_date, "look_back_days": 7, "limit": 5},
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])
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result = (prompt | llm).invoke([
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HumanMessage(content=f"Analyze {ticker}.\n\nHere is the pre-fetched news data:\n\n{tool_data}\n\nWrite your comprehensive report.")
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])
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report = ""
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if not getattr(result, "tool_calls", None):
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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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