69 lines
3.5 KiB
Python
69 lines
3.5 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
|
import time
|
|
import json
|
|
from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_transactions
|
|
from tradingagents.agents.utils.data_prefetch import prefetch_fundamentals_data, format_fundamentals_context
|
|
from tradingagents.dataflows.config import get_config
|
|
|
|
|
|
def create_fundamentals_analyst(llm):
|
|
def fundamentals_analyst_node(state):
|
|
current_date = state["trade_date"]
|
|
ticker = state["company_of_interest"]
|
|
company_name = state["company_of_interest"]
|
|
|
|
# Pre-fetch data so it's available even if the backend doesn't support tool calling
|
|
prefetched = prefetch_fundamentals_data(ticker, current_date)
|
|
data_context = format_fundamentals_context(prefetched)
|
|
|
|
tools = [
|
|
get_fundamentals,
|
|
get_balance_sheet,
|
|
get_cashflow,
|
|
get_income_statement,
|
|
]
|
|
|
|
system_message = (
|
|
"You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. 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."
|
|
+ " Use the provided financial data below for your analysis. If you have access to callable tools (`get_fundamentals`, `get_balance_sheet`, `get_cashflow`, `get_income_statement`), you may use them for additional data.",
|
|
)
|
|
|
|
prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
(
|
|
"system",
|
|
"You are a helpful AI assistant, collaborating with other assistants."
|
|
" Use the provided data below to write your analysis report."
|
|
" If you have access to callable tools, you may use them for additional data."
|
|
" 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."
|
|
"\n{system_message}"
|
|
"\nFor your reference, the current date is {current_date}. The company we want to look at is {ticker}"
|
|
"\n\n--- PRE-FETCHED FUNDAMENTALS DATA ---\n{data_context}\n--- END PRE-FETCHED DATA ---",
|
|
),
|
|
MessagesPlaceholder(variable_name="messages"),
|
|
]
|
|
)
|
|
|
|
prompt = prompt.partial(system_message=system_message)
|
|
prompt = prompt.partial(current_date=current_date)
|
|
prompt = prompt.partial(ticker=ticker)
|
|
prompt = prompt.partial(data_context=data_context)
|
|
|
|
chain = prompt | llm.bind_tools(tools)
|
|
|
|
result = chain.invoke(state["messages"])
|
|
|
|
report = ""
|
|
|
|
if len(result.tool_calls) == 0:
|
|
report = result.content
|
|
|
|
return {
|
|
"messages": [result],
|
|
"fundamentals_report": report,
|
|
}
|
|
|
|
return fundamentals_analyst_node
|