tradingagents/tests/test_graph_end_to_end.py

"""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 context, schemas
from tradingagents.agents.analysts import sentiment_analyst
from tradingagents.dataflows import router
from tradingagents.dataflows.vendors.yahoo import market as yahoo_market, snapshot
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 router.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(snapshot, "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(yahoo_market.yf, "Ticker", lambda s: type("T", (), {"info": {"longName": "NVIDIA"}})())
    context.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",
                    "get_insider_transactions", "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)


@pytest.mark.unit
def test_a_graph_reused_across_runs_keeps_no_run_state(tmp_path, monkeypatch, offline):
    """A backtest reuses one graph over its whole grid; holding every run's full
    state would grow without bound."""
    graph = _graph(tmp_path, monkeypatch, ScriptedModel())
    for trade_date in ("2026-01-08", TRADE_DATE):
        graph.propagate("NVDA", trade_date)

    held = [v for v in vars(graph).values() if isinstance(v, dict) and TRADE_DATE in v]
    assert held == []
    assert len(list(tmp_path.glob("results/NVDA/TradingAgentsStrategy_logs/*.json"))) == 2