feat: add valuation analyst
This commit is contained in:
Garrick
parent
1b2728f99d
commit
85377d27e2
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@ -0,0 +1,205 @@
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import json
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from langchain_core.messages import AIMessage
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from langchain_core.runnables import RunnableLambda
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from tradingagents.graph.setup import GraphSetup
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from tradingagents.graph.trading_graph import TradingAgentsGraph
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class DummyStateGraph:
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def __init__(self, _state_type):
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self.nodes = {}
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self.conditional_edges = {}
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def add_node(self, name, node):
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self.nodes[name] = node
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def add_edge(self, *_args, **_kwargs):
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return None
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def add_conditional_edges(self, source, condition, destinations):
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self.conditional_edges[source] = {
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"condition": condition,
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"destinations": destinations,
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}
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def compile(self):
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return {
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"nodes": self.nodes,
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"conditional_edges": self.conditional_edges,
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}
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class DummyToolNode:
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def __init__(self, tools):
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self.tools = tools
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def test_valuation_tools_route_to_vendor(monkeypatch):
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import tradingagents.dataflows.interface as interface
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from tradingagents.agents.utils.valuation_tools import get_valuation_inputs
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calls = []
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def fake_route_to_vendor(method, *args, **kwargs):
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calls.append((method, args, kwargs))
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return f"{method}-result"
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monkeypatch.setattr(interface, "route_to_vendor", fake_route_to_vendor)
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assert (
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get_valuation_inputs.invoke({"ticker": "NVDA", "curr_date": "2026-03-24"})
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== "get_fundamentals-result"
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)
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assert calls == [
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("get_fundamentals", (), {"ticker": "NVDA", "curr_date": "2026-03-24"})
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]
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def test_graph_setup_wires_valuation_analyst_and_tools(monkeypatch):
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recorded_llms = {}
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monkeypatch.setattr("tradingagents.graph.setup.StateGraph", DummyStateGraph)
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monkeypatch.setattr("tradingagents.graph.setup.create_msg_delete", lambda: "delete")
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def make_factory(node_name):
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def factory(llm, *_args):
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recorded_llms[node_name] = llm
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return node_name
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return factory
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_market_analyst",
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make_factory("Market Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_valuation_analyst",
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make_factory("Valuation Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_social_media_analyst",
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make_factory("Social Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_news_analyst",
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make_factory("News Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_fundamentals_analyst",
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make_factory("Fundamentals Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_factor_rule_analyst",
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make_factory("Factor Rules Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_macro_analyst",
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make_factory("Macro Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_bull_researcher",
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make_factory("Bull Researcher"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_bear_researcher",
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make_factory("Bear Researcher"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_research_manager",
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make_factory("Research Manager"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_trader",
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make_factory("Trader"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_aggressive_debator",
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make_factory("Aggressive Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_neutral_debator",
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make_factory("Neutral Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_conservative_debator",
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make_factory("Conservative Analyst"),
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)
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monkeypatch.setattr(
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"tradingagents.graph.setup.create_portfolio_manager",
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make_factory("Portfolio Manager"),
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)
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class PartialConditionalLogic:
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def should_continue_market(self, _state):
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return "Msg Clear Market"
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def should_continue_debate(self, _state):
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return "Research Manager"
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def should_continue_risk_analysis(self, _state):
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return "Portfolio Manager"
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setup = GraphSetup(
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quick_thinking_llm="quick-llm",
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deep_thinking_llm="deep-llm",
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tool_nodes={"market": "market-tools", "valuation": "valuation-tools"},
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bull_memory=object(),
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bear_memory=object(),
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trader_memory=object(),
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invest_judge_memory=object(),
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portfolio_manager_memory=object(),
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conditional_logic=PartialConditionalLogic(),
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role_llms={"valuation": "valuation-llm"},
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)
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graph = setup.setup_graph(selected_analysts=["market", "valuation"])
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assert recorded_llms["Valuation Analyst"] == "valuation-llm"
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assert graph["nodes"]["Valuation Analyst"] == "Valuation Analyst"
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assert graph["nodes"]["tools_valuation"] == "valuation-tools"
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assert graph["conditional_edges"]["Valuation Analyst"]["destinations"] == [
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"tools_valuation",
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"Msg Clear Valuation",
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]
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def test_trading_graph_creates_valuation_tool_node(monkeypatch):
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monkeypatch.setattr("tradingagents.graph.trading_graph.ToolNode", DummyToolNode)
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graph = TradingAgentsGraph.__new__(TradingAgentsGraph)
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tool_nodes = TradingAgentsGraph._create_tool_nodes(graph)
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assert [tool.name for tool in tool_nodes["valuation"].tools] == [
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"get_valuation_inputs"
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]
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def test_valuation_analyst_returns_structured_valuation_data():
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from tradingagents.agents.analysts.valuation_analyst import create_valuation_analyst
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response = {
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"fair_value_range": {"low": 120.5, "high": 145.0},
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"expected_return_pct": 18.2,
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"primary_method": "discounted cash flow",
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"thesis": "Free cash flow implies upside versus the current price.",
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}
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class FakeLLM:
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def bind_tools(self, _tools):
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return RunnableLambda(
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lambda _inputs: AIMessage(content=json.dumps(response), tool_calls=[])
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)
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node = create_valuation_analyst(FakeLLM())
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result = node(
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{
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"trade_date": "2026-03-24",
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"company_of_interest": "NVDA",
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"messages": [("human", "Value NVDA")],
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}
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)
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assert result["valuation_data"] == response
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assert list(result) == ["messages", "valuation_data"]
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@ -3,10 +3,12 @@ from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState
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from .utils.memory import FinancialSituationMemory
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from .analysts.fundamentals_analyst import create_fundamentals_analyst
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from .analysts.factor_rule_analyst import create_factor_rule_analyst
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from .analysts.macro_analyst import create_macro_analyst
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from .analysts.market_analyst import create_market_analyst
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from .analysts.news_analyst import create_news_analyst
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from .analysts.social_media_analyst import create_social_media_analyst
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from .analysts.valuation_analyst import create_valuation_analyst
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from .researchers.bear_researcher import create_bear_researcher
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from .researchers.bull_researcher import create_bull_researcher
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@ -29,11 +31,13 @@ __all__ = [
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"create_bear_researcher",
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"create_bull_researcher",
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"create_research_manager",
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"create_factor_rule_analyst",
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"create_fundamentals_analyst",
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"create_macro_analyst",
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"create_market_analyst",
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"create_neutral_debator",
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"create_news_analyst",
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"create_valuation_analyst",
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"create_aggressive_debator",
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"create_portfolio_manager",
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"create_conservative_debator",
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@ -0,0 +1,120 @@
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import json
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import re
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_states import make_default_valuation_data
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from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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get_valuation_inputs,
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)
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def _content_to_text(content) -> str:
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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return "".join(
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part.get("text", "") if isinstance(part, dict) else str(part)
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for part in content
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)
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return str(content)
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def _coerce_optional_float(value):
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if value in (None, ""):
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return None
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try:
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return float(value)
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except (TypeError, ValueError):
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return None
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def _parse_json_payload(raw_text: str):
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text = raw_text.strip()
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if not text:
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return {}
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candidates = [text]
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fenced_blocks = re.findall(r"```(?:json)?\s*(.*?)```", text, flags=re.DOTALL)
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candidates.extend(block.strip() for block in fenced_blocks if block.strip())
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for candidate in candidates:
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try:
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parsed = json.loads(candidate)
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except json.JSONDecodeError:
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continue
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if isinstance(parsed, dict):
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return parsed
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return {}
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def _parse_valuation_data(content):
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payload = _parse_json_payload(_content_to_text(content))
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valuation_data = make_default_valuation_data()
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fair_value_range = payload.get("fair_value_range")
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if isinstance(fair_value_range, dict):
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valuation_data["fair_value_range"] = {
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"low": _coerce_optional_float(fair_value_range.get("low")),
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"high": _coerce_optional_float(fair_value_range.get("high")),
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}
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valuation_data["expected_return_pct"] = _coerce_optional_float(
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payload.get("expected_return_pct")
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)
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valuation_data["primary_method"] = str(payload.get("primary_method") or "")
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valuation_data["thesis"] = str(payload.get("thesis") or "")
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return valuation_data
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def create_valuation_analyst(llm):
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def valuation_analyst_node(state):
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current_date = state["trade_date"]
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instrument_context = build_instrument_context(state["company_of_interest"])
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tools = [get_valuation_inputs]
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system_message = (
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"You are a valuation analyst responsible for translating company "
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"fundamentals into a concise underwriting view. Use `get_valuation_inputs` "
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"to gather valuation context, estimate a fair value range, choose the "
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"primary valuation method, and explain the core thesis. Respond with valid "
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"JSON only using this exact schema: "
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'{"fair_value_range":{"low":null,"high":null},"expected_return_pct":null,'
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'"primary_method":"","thesis":""}. '
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"Use null for unknown numeric values and do not add any extra keys."
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful AI assistant, collaborating with other assistants."
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" Use the provided tools to progress towards answering the question."
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" If you are unable to fully answer, that's OK; another assistant with different tools"
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" will help where you left off. Execute what you can to make progress."
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. {instrument_context}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join(tool.name for tool in tools))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(instrument_context=instrument_context)
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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payload = {"messages": [result]}
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if len(result.tool_calls) == 0:
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payload["valuation_data"] = _parse_valuation_data(result.content)
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return payload
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return valuation_analyst_node
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@ -26,6 +26,9 @@ from tradingagents.agents.utils.macro_data_tools import (
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get_fed_calendar,
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get_yield_curve,
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)
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from tradingagents.agents.utils.valuation_tools import (
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get_valuation_inputs,
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)
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__all__ = [
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@ -43,6 +46,7 @@ __all__ = [
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"get_insider_transactions",
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"get_news",
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"get_stock_data",
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"get_valuation_inputs",
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"get_yield_curve",
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]
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@ -0,0 +1,18 @@
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from typing import Annotated
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from langchain_core.tools import tool
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@tool
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def get_valuation_inputs(
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ticker: Annotated[str, "ticker symbol"],
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curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
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) -> str:
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"""Retrieve valuation-oriented fundamental inputs for a company."""
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from tradingagents.dataflows.interface import route_to_vendor
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return route_to_vendor(
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"get_fundamentals",
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ticker=ticker,
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curr_date=curr_date,
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)
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@ -47,6 +47,9 @@ class GraphSetup:
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self.factor_rules_analyst_llm = self._get_role_llm(
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"factor_rules", self.quick_thinking_llm
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)
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self.valuation_analyst_llm = self._get_role_llm(
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"valuation", self.quick_thinking_llm
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)
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self.macro_analyst_llm = self._get_role_llm("macro", self.quick_thinking_llm)
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self.bull_researcher_llm = self._get_role_llm(
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"bull_researcher", self.quick_thinking_llm
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@ -104,6 +107,7 @@ class GraphSetup:
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- "news": News analyst
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- "fundamentals": Fundamentals analyst
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- "factor_rules": Factor rule analyst
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- "valuation": Valuation analyst
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- "macro": Macro analyst
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"""
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if len(selected_analysts) == 0:
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@ -148,6 +152,13 @@ class GraphSetup:
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)
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delete_nodes["factor_rules"] = create_msg_delete()
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if "valuation" in selected_analysts:
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analyst_nodes["valuation"] = create_valuation_analyst(
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self.valuation_analyst_llm
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)
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delete_nodes["valuation"] = create_msg_delete()
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tool_nodes["valuation"] = self.tool_nodes["valuation"]
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if "macro" in selected_analysts:
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analyst_nodes["macro"] = create_macro_analyst(self.macro_analyst_llm)
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delete_nodes["macro"] = create_msg_delete()
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@ -33,6 +33,7 @@ from tradingagents.agents.utils.agent_utils import (
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get_news,
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get_insider_transactions,
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get_global_news,
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get_valuation_inputs,
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get_yield_curve,
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)
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@ -62,6 +63,7 @@ class TradingAgentsGraph:
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"news",
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"fundamentals",
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"factor_rules",
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"valuation",
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"macro",
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"bull_researcher",
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"bear_researcher",
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@ -304,6 +306,12 @@ class TradingAgentsGraph:
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get_income_statement,
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]
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),
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"valuation": ToolNode(
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[
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# Valuation analysis tools
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get_valuation_inputs,
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]
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),
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"macro": ToolNode(
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[
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# Macroeconomic analysis tools
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