83 lines
4.0 KiB
Python
83 lines
4.0 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,
|
|
get_ttm_analysis,
|
|
get_peer_comparison,
|
|
get_sector_relative,
|
|
)
|
|
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"]
|
|
|
|
tools = [
|
|
get_ttm_analysis,
|
|
get_fundamentals,
|
|
get_balance_sheet,
|
|
get_cashflow,
|
|
get_income_statement,
|
|
get_peer_comparison,
|
|
get_sector_relative,
|
|
]
|
|
|
|
system_message = (
|
|
"You are a researcher tasked with performing deep fundamental analysis of a company over the last 8 quarters (2 years) to support medium-term investment decisions."
|
|
" Follow this sequence:"
|
|
" 1. Call `get_ttm_analysis` first — this provides a Trailing Twelve Months (TTM) trend report covering revenue growth (QoQ and YoY), margin trajectories (gross, operating, net), return on equity trend, debt/equity trend, and free cash flow over 8 quarters."
|
|
" 2. Call `get_fundamentals` for the latest snapshot of key ratios (PE, PEG, price-to-book, beta, 52-week range)."
|
|
" 3. Call `get_peer_comparison` to see how the company ranks against sector peers over 1-week, 1-month, 3-month, and 6-month periods."
|
|
" 4. Call `get_sector_relative` to compute the company's alpha vs its sector ETF benchmark."
|
|
" 5. Optionally call `get_balance_sheet`, `get_cashflow`, or `get_income_statement` for additional detail."
|
|
" Write a comprehensive report covering: multi-quarter revenue and margin trends, TTM metrics, relative valuation vs peers, sector outperformance or underperformance, and a clear medium-term fundamental thesis."
|
|
" Do not simply state trends are mixed — provide detailed, fine-grained analysis that identifies inflection points, acceleration or deceleration in growth, and specific risks and opportunities."
|
|
" Make sure to append a Markdown summary table at the end of the report organising key metrics for easy reference.",
|
|
)
|
|
|
|
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}. The company we want to look at is {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],
|
|
"fundamentals_report": report,
|
|
}
|
|
|
|
return fundamentals_analyst_node
|