TradingAgents/tradingagents/agents/analysts/news_analyst.py

74 lines
2.7 KiB
Python

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from tradingagents.agents.utils.agent_utils import get_news, get_global_news
def create_news_analyst(llm, config):
"""Create the news analyst node with language support."""
def news_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
tools = [
get_news,
get_global_news,
]
language = config["output_language"]
language_prompts = {
"en": "",
"zh-tw": "Use Traditional Chinese as the output.",
"zh-cn": "Use Simplified Chinese as the output.",
}
language_prompt = language_prompts.get(language, "")
system_message = (
f"""
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.
Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make 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.
"""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
f"""
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: {tools}.
{system_message}
For your reference, the current date is {current_date}.
The company we want to look at is {ticker}
Output language: ***{language_prompt}***
""",
),
MessagesPlaceholder(variable_name="messages"),
]
)
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