82 lines
3.7 KiB
Python
82 lines
3.7 KiB
Python
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
|