fix(agents): record the decision that was made, or flag it for review

- the labelled rating decides, whatever dash separates it, and a scale the model echoed is not one
- prose naming several ratings is reviewed rather than read as the first word in the text
- an unreadable decision is tagged REVIEW everywhere instead of a tradeable Hold
- unrated decisions are counted apart from the backtest figures
This commit is contained in:
Yijia-Xiao
2026-09-17 05:00:08 +00:00
parent 3244a568ed
commit 8d30fee06b
6 changed files with 268 additions and 30 deletions

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"""A decision is recorded as the call that was made, or as needing review.
Two readers used to disagree about the same text: the signal said REVIEW while
the memory log wrote a fabricated Hold. Worse, prose that argued against a Buy
before concluding Underweight was read as Buy, because the parser took the first
rating word anywhere in the document. A wrong direction is worse than no
direction, so an unclear decision is REVIEW everywhere.
"""
from __future__ import annotations
import pytest
from tradingagents.agents.utils.rating import RATING_REVIEW, extract_rating, parse_rating
INVERTED = ("The aggressive analyst pushed hard for a Buy on the AI backlog, but the "
"conservative case on margin compression carried the debate. "
"Final rating — Underweight. Trim to half weight over the next two weeks.")
REFUSAL = "I'm sorry, I can't provide a rating for this security."
@pytest.mark.unit
@pytest.mark.parametrize("separator", [":", "-", "", "", "", ": **"])
def test_the_labelled_rating_wins_whatever_separates_it(separator):
text = f"Buy arguments were raised and rejected.\n\nRating{separator}Underweight\n\nTrim."
assert extract_rating(text) == "Underweight"
@pytest.mark.unit
def test_a_rating_argued_against_is_not_read_as_the_decision():
assert extract_rating(INVERTED) == "Underweight"
@pytest.mark.unit
def test_prose_naming_several_ratings_without_a_label_needs_review():
"""Nothing in the text says which one is the call, so guessing risks
reporting the opposite of the decision."""
text = "The bull wants Buy, the bear wants Sell, and the committee was split."
assert extract_rating(text) is None
@pytest.mark.unit
def test_prose_naming_one_rating_is_taken_as_the_call():
assert extract_rating("On balance we stay Underweight until margins recover.") == "Underweight"
@pytest.mark.unit
def test_a_refusal_has_no_rating_and_is_not_defaulted():
assert extract_rating(REFUSAL) is None
assert parse_rating(REFUSAL) == RATING_REVIEW
@pytest.mark.unit
def test_the_scale_quoted_in_a_prompt_does_not_become_the_rating():
"""A free-text answer that echoes the rating scale was read as the first
tier listed in it."""
text = ("**Rating Scale**: Buy, Overweight, Hold, Underweight, Sell.\n\n"
"**Rating**: Sell\n\nExit the position.")
assert extract_rating(text) == "Sell"
# --- the readers agree ------------------------------------------------------
@pytest.mark.unit
def test_the_memory_log_records_review_rather_than_a_tradeable_hold(tmp_path):
from tradingagents.agents.utils.memory import TradingMemoryLog
log = TradingMemoryLog({"memory_log_path": str(tmp_path / "m.md")})
log.store_decision("NVDA", "2026-01-05", REFUSAL)
entry = log.load_entries()[0]
assert entry["rating"] == RATING_REVIEW
@pytest.mark.unit
def test_the_signal_and_the_log_agree_on_the_same_decision(tmp_path):
from tradingagents.agents.utils.memory import TradingMemoryLog
from tradingagents.graph.signal_processing import SignalProcessor
log = TradingMemoryLog({"memory_log_path": str(tmp_path / "m.md")})
for text in (INVERTED, REFUSAL, "**Rating**: Buy\n\nAccumulate."):
log.store_decision("NVDA", f"2026-01-0{len(log.load_entries()) + 1}", text)
signals = [SignalProcessor.process_signal(None, text)
for text in (INVERTED, REFUSAL, "**Rating**: Buy\n\nAccumulate.")]
assert [e["rating"] for e in log.load_entries()] == signals
@pytest.mark.unit
def test_an_unscored_decision_is_left_out_of_the_backtest_figures(tmp_path):
"""REVIEW has no direction, so it cannot count for or against the system."""
from tradingagents.agents.utils.memory import TradingMemoryLog
from tradingagents.backtest import summarize
log = TradingMemoryLog({"memory_log_path": str(tmp_path / "m.md")})
log.store_decision("NVDA", "2026-01-05", "**Rating**: Buy\n\nx")
log.update_with_outcome("NVDA", "2026-01-05", 0.1, 0.04, 5, "note", "2026-02-01")
log.store_decision("AAPL", "2026-01-05", REFUSAL)
log.update_with_outcome("AAPL", "2026-01-05", 0.1, 0.04, 5, "note", "2026-02-01")
summary = summarize(log)
assert set(summary.by_rating) == {"Buy"}
@pytest.mark.unit
def test_the_cli_says_when_a_run_produced_no_usable_rating(monkeypatch, tmp_path, capsys):
"""The CLI is the primary entry point; an unreadable decision must be
visible there, not only in the log."""
import cli.main as m
from cli.models import AnalystType
printed = []
class _Graph:
graph = propagator = None
def create_run_state(self, *a, **k):
return {"messages": []}
def record_decision(self, *a, **k):
pass
def process_signal(self, text):
from tradingagents.graph.signal_processing import SignalProcessor
return SignalProcessor.process_signal(None, text)
def get_graph_args(self, callbacks=None):
return {}
def begin_checkpoint(self, *a, **k):
return None
def checkpoint_input(self, state):
return state
def clear_checkpoint_on_success(self, *a, **k):
pass
def end_checkpoint(self):
pass
def stream(self, *a, **k):
yield {"messages": [], "final_trade_decision": REFUSAL}
fake = _Graph()
fake.graph = fake
fake.propagator = fake
monkeypatch.setattr(m, "TradingAgentsGraph", lambda *a, **k: fake)
monkeypatch.setattr(m, "create_layout", lambda: None)
monkeypatch.setattr(m, "update_display", lambda *a, **k: None)
monkeypatch.setattr(m, "Live", type("L", (), {"__init__": lambda s, *a, **k: None,
"__enter__": lambda s: s,
"__exit__": lambda s, *a: False}))
monkeypatch.setattr(m.console, "print", lambda *a, **k: printed.append(" ".join(str(x) for x in a)))
monkeypatch.setattr(m, "display_complete_report", lambda *a, **k: None)
monkeypatch.setattr(m.typer, "prompt", lambda *a, **k: "N")
monkeypatch.setattr(m, "get_user_selections", lambda: {
"ticker": "NVDA", "analysis_date": "2026-01-10",
"analysts": [AnalystType.MARKET], "asset_type": "stock",
})
monkeypatch.setattr(m, "_build_run_config", lambda s, c: {
"data_cache_dir": str(tmp_path / "c"), "results_dir": str(tmp_path / "r")})
m.run_analysis()
assert any("review" in line.lower() for line in printed), printed[-5:]
@pytest.mark.unit
@pytest.mark.parametrize("module, factory, must_name", [
("tradingagents.agents.managers.portfolio_manager", "create_portfolio_manager", "Rating"),
("tradingagents.agents.managers.research_manager", "create_research_manager", "Recommendation"),
("tradingagents.agents.trader.trader", "create_trader", "Action"),
])
def test_a_decision_prompt_states_the_shape_of_its_answer(module, factory, must_name):
"""The field descriptions live in the schema, which a provider without
structured output never sees. Without the format in the prompt body, the
fallback answer is prose nobody can read a rating from."""
import importlib
from langchain_core.messages import AIMessage
mod = importlib.import_module(module)
seen = []
class _LLM:
def invoke(self, prompt, *a, **k):
seen.append(prompt if isinstance(prompt, str) else str(prompt))
return AIMessage("**Rating**: Hold\n\nnothing to do")
def with_structured_output(self, *a, **k):
raise NotImplementedError # force the free-text path
state = {
"company_of_interest": "NVDA", "trade_date": "2026-08-14", "asset_type": "stock",
"instrument_context": "", "market_report": "M", "sentiment_report": "S",
"news_report": "N", "fundamentals_report": "F", "investment_plan": "P",
"trader_investment_plan": "T", "past_context": "", "portfolio_context": "",
"investment_debate_state": {"bull_history": "b", "bear_history": "r", "history": "h",
"current_response": "", "judge_decision": "", "count": 2},
"risk_debate_state": {"history": "h", "latest_speaker": "", "count": 3,
"aggressive_history": "", "conservative_history": "", "neutral_history": "",
"current_aggressive_response": "", "current_conservative_response": "",
"current_neutral_response": "", "judge_decision": ""},
}
getattr(mod, factory)(_LLM())(state)
prompt = " ".join(seen)
assert "## Output" in prompt, "no output-format section in the prompt"
section = prompt.split("## Output", 1)[1]
assert f"**{must_name}**" in section, section[:300]