81 lines
4.3 KiB
Python
81 lines
4.3 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
|
import time
|
|
import json
|
|
from tradingagents.agents.utils.agent_utils import get_news, get_commodity_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"]
|
|
asset_class = state.get("asset_class", "equity")
|
|
is_commodity = asset_class.lower() == "commodity"
|
|
|
|
# Branch tools based on asset class
|
|
if is_commodity:
|
|
tools = [
|
|
get_commodity_news,
|
|
get_global_news,
|
|
]
|
|
system_message = (
|
|
f"You are a news researcher tasked with analyzing recent news and trends for the commodity {ticker}. "
|
|
"Please write a comprehensive report of relevant news over the past week that impacts this commodity's price. "
|
|
"Use the available tools: get_commodity_news(commodity, start_date, end_date) for commodity-specific news (searches by topic like 'energy' for oil, 'economy_macro' for agriculture), "
|
|
"and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic context. "
|
|
"IMPORTANT: If get_commodity_news returns limited results, make sure to use get_global_news to provide additional market context. "
|
|
"Focus on supply/demand factors, geopolitical events, weather impacts (for agriculture), and macroeconomic trends. "
|
|
"Do not simply state the trends are mixed, provide detailed and fine-grained analysis."
|
|
+ """ Make sure to append a Markdown table at the end of the report to organize key points."""
|
|
)
|
|
else:
|
|
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(ticker, 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 fine-grained 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",
|
|
"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}. 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
|