test: run the whole graph end to end with scripted models

- every analyst calls its tools through the router, offline; the decision is parsed and logged
- free-text and structured-output paths, and resume from a checkpoint
- remove the unused mock_llm_client fixture

tests/test_graph_end_to_end.py

@@ -0,0 +1,155 @@
+"""The whole graph, end to end, with scripted models and no network.
+
+Every tool-using analyst calls each of its tools once through the vendor router,
+the debates and managers run, and the decision is parsed and logged. This pins
+the wiring: a restructure that drops a node, a tool or an edge fails here.
+"""
+
+from __future__ import annotations
+
+import copy
+
+import pandas as pd
+import pytest
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import AIMessage, ToolMessage
+from langchain_core.outputs import ChatGeneration, ChatResult
+from langchain_core.runnables import RunnableLambda
+from pydantic import Field
+
+from tradingagents.agents import schemas
+from tradingagents.agents.analysts import sentiment_analyst
+from tradingagents.agents.utils import agent_utils
+from tradingagents.dataflows import interface, market_data_validator
+from tradingagents.default_config import DEFAULT_CONFIG
+from tradingagents.graph import trading_graph
+
+TRADE_DATE = "2026-01-09"
+
+# Enough for every free-text reader: the PM's labelled rating and the trader's
+# closing proposal line.
+TEXT = "Report.\n\n**Rating**: Overweight\n\nFINAL TRANSACTION PROPOSAL: **BUY**"
+
+STRUCTURED = {
+    schemas.ResearchPlan: schemas.ResearchPlan(
+        recommendation=schemas.PortfolioRating.OVERWEIGHT, rationale="r", strategic_actions="a"),
+    schemas.TraderProposal: schemas.TraderProposal(action=schemas.TraderAction.BUY, reasoning="r"),
+    schemas.PortfolioDecision: schemas.PortfolioDecision(
+        rating=schemas.PortfolioRating.OVERWEIGHT, executive_summary="s", investment_thesis="t"),
+    schemas.SentimentReport: schemas.SentimentReport(
+        overall_band=schemas.SentimentBand.NEUTRAL, overall_score=5.0, confidence="low", narrative="n"),
+}
+
+ARGS = {"symbol": "NVDA", "ticker": "NVDA", "curr_date": TRADE_DATE, "start_date": "2026-01-02",
+        "end_date": TRADE_DATE, "indicator": "rsi", "topic": "Fed rate cut", "freq": "quarterly"}
+
+
+class ScriptedModel(BaseChatModel):
+    """Calls every bound tool once, then answers with TEXT."""
+
+    structured: bool = False
+    tools: tuple = ()
+    calls: list = Field(default_factory=list)   # shared across bound copies
+    fail_at: int | None = None                  # raise on this call, once
+
+    @property
+    def _llm_type(self) -> str:
+        return "scripted"
+
+    def bind_tools(self, tools, **kwargs):
+        return self.model_copy(update={"tools": tuple(tools)})
+
+    def with_structured_output(self, schema, **kwargs):
+        if not self.structured:
+            raise NotImplementedError
+        return RunnableLambda(lambda _: self._count() or STRUCTURED[schema])
+
+    def _count(self) -> None:
+        self.calls.append(1)
+        if len(self.calls) == self.fail_at:
+            raise RuntimeError("provider unavailable")
+
+    def _generate(self, messages, stop=None, run_manager=None, **kwargs) -> ChatResult:
+        self._count()
+        if self.tools and not isinstance(messages[-1], ToolMessage):
+            calls = [{"name": t.name, "id": f"call_{i}",
+                      "args": {k: v for k, v in ARGS.items()
+                               if k in t.tool_call_schema.model_json_schema()["properties"]}}
+                     for i, t in enumerate(self.tools)]
+            message = AIMessage(content="", tool_calls=calls)
+        else:
+            message = AIMessage(content=TEXT)
+        return ChatResult(generations=[ChatGeneration(message=message)])
+
+
+class _Client:
+    def __init__(self, model):
+        self.model = model
+
+    def get_llm(self):
+        return self.model
+
+
+@pytest.fixture
+def offline(monkeypatch, tmp_path):
+    """Every vendor answers offline; returns the set of router methods called."""
+    called: set[str] = set()
+    for method, vendors in interface.VENDOR_METHODS.items():
+        for vendor in vendors:
+            monkeypatch.setitem(vendors, vendor,
+                                lambda *a, _m=method, **k: called.add(_m) or f"{_m} data")
+    prices = pd.DataFrame({
+        "Date": pd.bdate_range(end=TRADE_DATE, periods=60),
+        "Open": 100.0, "High": 101.0, "Low": 99.0, "Close": 100.5, "Volume": 1_000_000,
+    })
+    monkeypatch.setattr(market_data_validator, "load_ohlcv",
+                        lambda *a, **k: called.add("ohlcv") or prices.copy())
+    monkeypatch.setattr(sentiment_analyst, "fetch_stocktwits_messages", lambda *a, **k: "no posts")
+    monkeypatch.setattr(sentiment_analyst, "fetch_reddit_posts", lambda *a, **k: "no posts")
+    monkeypatch.setattr(agent_utils.yf, "Ticker", lambda s: type("T", (), {"info": {"longName": "NVIDIA"}})())
+    agent_utils.resolve_instrument_identity.cache_clear()
+    return called
+
+
+def _graph(tmp_path, monkeypatch, model, **config):
+    cfg = copy.deepcopy(DEFAULT_CONFIG)
+    cfg.update(results_dir=str(tmp_path / "results"), data_cache_dir=str(tmp_path / "cache"),
+               memory_log_path=str(tmp_path / "log.md"), **config)
+    monkeypatch.setattr(trading_graph, "create_llm_client", lambda **k: _Client(model))
+    return trading_graph.TradingAgentsGraph(config=cfg)
+
+
+@pytest.mark.unit
+@pytest.mark.parametrize("structured", [False, True], ids=["free-text", "structured"])
+def test_a_full_run_reaches_a_logged_decision(tmp_path, monkeypatch, offline, structured):
+    graph = _graph(tmp_path, monkeypatch, ScriptedModel(structured=structured))
+
+    state, signal = graph.propagate("NVDA", TRADE_DATE)
+
+    assert signal == "Overweight"
+    for key in ("market_report", "sentiment_report", "news_report", "fundamentals_report",
+                "investment_plan", "trader_investment_plan", "final_trade_decision"):
+        assert state[key].strip(), key
+    tool_methods = {"get_stock_data", "get_indicators", "get_news", "get_global_news",
+                    "get_macro_indicators", "get_prediction_markets", "get_fundamentals",
+                    "get_balance_sheet", "get_cashflow", "get_income_statement", "ohlcv"}
+    assert offline == tool_methods
+    assert [e["rating"] for e in graph.memory_log.load_entries()] == ["Overweight"]
+
+
+@pytest.mark.unit
+def test_an_interrupted_run_resumes_from_its_checkpoint(tmp_path, monkeypatch, offline):
+    model = ScriptedModel(fail_at=12)            # past the analysts, before the decision
+    graph = _graph(tmp_path, monkeypatch, model, checkpoint_enabled=True)
+    with pytest.raises(RuntimeError, match="provider unavailable"):
+        graph.propagate("NVDA", TRADE_DATE)
+    calls_before = len(model.calls)
+
+    _, signal = graph.propagate("NVDA", TRADE_DATE)
+
+    assert signal == "Overweight"
+    resumed_calls = len(model.calls) - calls_before
+    full_run = ScriptedModel()
+    _graph(tmp_path / "fresh", monkeypatch, full_run).propagate("NVDA", TRADE_DATE)
+    # The resumed run makes only the calls the interrupted one had not completed.
+    assert resumed_calls == len(full_run.calls) - (model.fail_at - 1)