fix: harden valuation analyst parsing

tests/test_valuation_analyst.py

@@ -2,7 +2,10 @@ import json
 
 from langchain_core.messages import AIMessage
 from langchain_core.runnables import RunnableLambda
+from langgraph.graph import END, START, StateGraph
+from langgraph.prebuilt import ToolNode
 
+from tradingagents.agents.utils.agent_states import AgentState
 from tradingagents.graph.setup import GraphSetup
 from tradingagents.graph.trading_graph import TradingAgentsGraph
 
@@ -203,3 +206,114 @@ def test_valuation_analyst_returns_structured_valuation_data():
 
     assert result["valuation_data"] == response
     assert list(result) == ["messages", "valuation_data"]
+
+
+def test_valuation_analyst_marks_parse_failure_without_changing_shape():
+    from tradingagents.agents.analysts.valuation_analyst import create_valuation_analyst
+
+    class FakeLLM:
+        def bind_tools(self, _tools):
+            return RunnableLambda(
+                lambda _inputs: AIMessage(content="not valid json", tool_calls=[])
+            )
+
+    node = create_valuation_analyst(FakeLLM())
+    result = node(
+        {
+            "trade_date": "2026-03-24",
+            "company_of_interest": "NVDA",
+            "messages": [("human", "Value NVDA")],
+        }
+    )
+
+    assert set(result["valuation_data"]) == {
+        "fair_value_range",
+        "expected_return_pct",
+        "primary_method",
+        "thesis",
+    }
+    assert result["valuation_data"]["fair_value_range"] == {"low": None, "high": None}
+    assert result["valuation_data"]["expected_return_pct"] is None
+    assert result["valuation_data"]["primary_method"] == "parse_error"
+    assert result["valuation_data"]["thesis"] == "not valid json"
+
+
+def test_valuation_analyst_populates_structured_data_after_tool_loop(monkeypatch):
+    import tradingagents.dataflows.interface as interface
+    from tradingagents.agents.analysts.valuation_analyst import create_valuation_analyst
+    from tradingagents.agents.utils.valuation_tools import get_valuation_inputs
+
+    llm_responses = iter(
+        [
+            AIMessage(
+                content="",
+                tool_calls=[
+                    {
+                        "name": "get_valuation_inputs",
+                        "args": {"ticker": "NVDA", "curr_date": "2026-03-24"},
+                        "id": "call_1",
+                        "type": "tool_call",
+                    }
+                ],
+            ),
+            AIMessage(
+                content=json.dumps(
+                    {
+                        "fair_value_range": {"low": 120.5, "high": 145.0},
+                        "expected_return_pct": 18.2,
+                        "primary_method": "discounted cash flow",
+                        "thesis": "Free cash flow implies upside versus the current price.",
+                    }
+                ),
+                tool_calls=[],
+            ),
+        ]
+    )
+
+    calls = []
+
+    def fake_route_to_vendor(method, *args, **kwargs):
+        calls.append((method, args, kwargs))
+        return "valuation inputs"
+
+    monkeypatch.setattr(interface, "route_to_vendor", fake_route_to_vendor)
+
+    class FakeLLM:
+        def bind_tools(self, _tools):
+            return RunnableLambda(lambda _inputs: next(llm_responses))
+
+    node = create_valuation_analyst(FakeLLM())
+    workflow = StateGraph(AgentState)
+    workflow.add_node("Valuation Analyst", node)
+    workflow.add_node("tools_valuation", ToolNode([get_valuation_inputs]))
+    workflow.add_node("Msg Clear Valuation", lambda _state: {})
+    workflow.add_edge(START, "Valuation Analyst")
+    workflow.add_conditional_edges(
+        "Valuation Analyst",
+        lambda state: (
+            "tools_valuation"
+            if getattr(state["messages"][-1], "tool_calls", None)
+            else "Msg Clear Valuation"
+        ),
+        ["tools_valuation", "Msg Clear Valuation"],
+    )
+    workflow.add_edge("tools_valuation", "Valuation Analyst")
+    workflow.add_edge("Msg Clear Valuation", END)
+
+    final_state = workflow.compile().invoke(
+        {
+            "trade_date": "2026-03-24",
+            "company_of_interest": "NVDA",
+            "messages": [("human", "Value NVDA")],
+        }
+    )
+
+    assert final_state["valuation_data"] == {
+        "fair_value_range": {"low": 120.5, "high": 145.0},
+        "expected_return_pct": 18.2,
+        "primary_method": "discounted cash flow",
+        "thesis": "Free cash flow implies upside versus the current price.",
+    }
+    assert calls == [
+        ("get_fundamentals", (), {"ticker": "NVDA", "curr_date": "2026-03-24"})
+    ]

tradingagents/agents/analysts/valuation_analyst.py

@@ -33,7 +33,7 @@ def _coerce_optional_float(value):
 def _parse_json_payload(raw_text: str):
     text = raw_text.strip()
     if not text:
-        return {}
+        return {}, False
 
     candidates = [text]
     fenced_blocks = re.findall(r"```(?:json)?\s*(.*?)```", text, flags=re.DOTALL)
@@ -45,12 +45,13 @@ def _parse_json_payload(raw_text: str):
         except json.JSONDecodeError:
             continue
         if isinstance(parsed, dict):
-            return parsed
-    return {}
+            return parsed, True
+    return {}, False
 
 
 def _parse_valuation_data(content):
-    payload = _parse_json_payload(_content_to_text(content))
+    raw_text = _content_to_text(content).strip()
+    payload, parsed = _parse_json_payload(raw_text)
     valuation_data = make_default_valuation_data()
 
     fair_value_range = payload.get("fair_value_range")
@@ -66,6 +67,10 @@ def _parse_valuation_data(content):
     valuation_data["primary_method"] = str(payload.get("primary_method") or "")
     valuation_data["thesis"] = str(payload.get("thesis") or "")
 
+    if not parsed:
+        valuation_data["primary_method"] = "parse_error"
+        valuation_data["thesis"] = raw_text or "[empty model response]"
+
     return valuation_data