"""Tests for TradingMemoryLog — storage, deferred reflection, PM injection, legacy removal."""

import pytest
import pandas as pd
from unittest.mock import MagicMock, patch

from tradingagents.agents.utils.memory import TradingMemoryLog
from tradingagents.agents.schemas import PortfolioDecision, PortfolioRating
from tradingagents.graph.reflection import Reflector
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.graph.propagation import Propagator
from tradingagents.agents.managers.portfolio_manager import create_portfolio_manager

_SEP = TradingMemoryLog._SEPARATOR

DECISION_BUY = "Rating: Buy\nEnter at $189-192, 6% portfolio cap."
DECISION_OVERWEIGHT = (
    "Rating: Overweight\n"
    "Executive Summary: Moderate position, await confirmation.\n"
    "Investment Thesis: Strong fundamentals but near-term headwinds."
)
DECISION_SELL = "Rating: Sell\nExit position immediately."
DECISION_NO_RATING = (
    "Executive Summary: Complex situation with multiple competing factors.\n"
    "Investment Thesis: No clear directional signal at this time."
)


# ---------------------------------------------------------------------------
# Shared helpers
# ---------------------------------------------------------------------------

def make_log(tmp_path, filename="trading_memory.md"):
    config = {"memory_log_path": str(tmp_path / filename)}
    return TradingMemoryLog(config)


def _seed_completed(tmp_path, ticker, date, decision_text, reflection_text, filename="trading_memory.md"):
    """Write a completed entry directly to file, bypassing the API."""
    entry = (
        f"[{date} | {ticker} | Buy | +1.0% | +0.5% | 5d]\n\n"
        f"DECISION:\n{decision_text}\n\n"
        f"REFLECTION:\n{reflection_text}"
        + _SEP
    )
    with open(tmp_path / filename, "a", encoding="utf-8") as f:
        f.write(entry)


def _resolve_entry(log, ticker, date, decision, reflection="Good call."):
    """Store a decision then immediately resolve it via the API."""
    log.store_decision(ticker, date, decision)
    log.update_with_outcome(ticker, date, 0.05, 0.02, 5, reflection)


def _price_df(prices):
    """Minimal DataFrame matching yfinance .history() output shape."""
    return pd.DataFrame({"Close": prices})


def _make_pm_state(past_context=""):
    """Minimal AgentState dict for portfolio_manager_node."""
    return {
        "company_of_interest": "NVDA",
        "past_context": past_context,
        "risk_debate_state": {
            "history": "Risk debate history.",
            "aggressive_history": "",
            "conservative_history": "",
            "neutral_history": "",
            "judge_decision": "",
            "current_aggressive_response": "",
            "current_conservative_response": "",
            "current_neutral_response": "",
            "count": 1,
        },
        "market_report": "Market report.",
        "sentiment_report": "Sentiment report.",
        "news_report": "News report.",
        "fundamentals_report": "Fundamentals report.",
        "investment_plan": "Research plan.",
        "trader_investment_plan": "Trader plan.",
    }


def _structured_pm_llm(captured: dict, decision: PortfolioDecision | None = None):
    """Build a MagicMock LLM whose with_structured_output binding captures the
    prompt and returns a real PortfolioDecision (so render_pm_decision works).
    """
    if decision is None:
        decision = PortfolioDecision(
            rating=PortfolioRating.HOLD,
            executive_summary="Hold the position; await catalyst.",
            investment_thesis="Balanced view; neither side carried the debate.",
        )
    structured = MagicMock()
    structured.invoke.side_effect = lambda prompt: (
        captured.__setitem__("prompt", prompt) or decision
    )
    llm = MagicMock()
    llm.with_structured_output.return_value = structured
    return llm


# ---------------------------------------------------------------------------
# Core: storage and read path
# ---------------------------------------------------------------------------

class TestTradingMemoryLogCore:

    def test_store_creates_file(self, tmp_path):
        log = make_log(tmp_path)
        assert not (tmp_path / "trading_memory.md").exists()
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        assert (tmp_path / "trading_memory.md").exists()

    def test_store_appends_not_overwrites(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        log.store_decision("AAPL", "2026-01-11", DECISION_OVERWEIGHT)
        entries = log.load_entries()
        assert len(entries) == 2
        assert entries[0]["ticker"] == "NVDA"
        assert entries[1]["ticker"] == "AAPL"

    def test_store_decision_idempotent(self, tmp_path):
        """Calling store_decision twice with same (ticker, date) stores only one entry."""
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        assert len(log.load_entries()) == 1

    def test_batch_update_resolves_multiple_entries(self, tmp_path):
        """batch_update_with_outcomes resolves multiple pending entries in one write."""
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-05", DECISION_BUY)
        log.store_decision("NVDA", "2026-01-12", DECISION_SELL)

        updates = [
            {"ticker": "NVDA", "trade_date": "2026-01-05",
             "raw_return": 0.05, "alpha_return": 0.02, "holding_days": 5,
             "reflection": "First correct."},
            {"ticker": "NVDA", "trade_date": "2026-01-12",
             "raw_return": -0.03, "alpha_return": -0.01, "holding_days": 5,
             "reflection": "Second correct."},
        ]
        log.batch_update_with_outcomes(updates)

        entries = log.load_entries()
        assert len(entries) == 2
        assert all(not e["pending"] for e in entries)
        assert entries[0]["reflection"] == "First correct."
        assert entries[1]["reflection"] == "Second correct."

    def test_pending_tag_format(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        text = (tmp_path / "trading_memory.md").read_text(encoding="utf-8")
        assert "[2026-01-10 | NVDA | Buy | pending]" in text

    # Rating parsing

    def test_rating_parsed_buy(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        assert log.load_entries()[0]["rating"] == "Buy"

    def test_rating_parsed_overweight(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("AAPL", "2026-01-11", DECISION_OVERWEIGHT)
        assert log.load_entries()[0]["rating"] == "Overweight"

    def test_rating_fallback_hold(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("MSFT", "2026-01-12", DECISION_NO_RATING)
        assert log.load_entries()[0]["rating"] == "Hold"

    def test_rating_priority_over_prose(self, tmp_path):
        """'Rating: X' label wins even when an opposing rating word appears earlier in prose."""
        decision = (
            "The sell thesis is weak. The hold case is marginal.\n\n"
            "Rating: Buy\n\n"
            "Executive Summary: Strong fundamentals support the position."
        )
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", decision)
        assert log.load_entries()[0]["rating"] == "Buy"

    # Delimiter robustness

    def test_decision_with_markdown_separator(self, tmp_path):
        """LLM decision containing '---' must not corrupt the entry."""
        decision = "Rating: Buy\n\n---\n\nRisk: elevated volatility."
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", decision)
        entries = log.load_entries()
        assert len(entries) == 1
        assert "Risk: elevated volatility" in entries[0]["decision"]

    # load_entries

    def test_load_entries_empty_file(self, tmp_path):
        log = make_log(tmp_path)
        assert log.load_entries() == []

    def test_load_entries_single(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        entries = log.load_entries()
        assert len(entries) == 1
        e = entries[0]
        assert e["date"] == "2026-01-10"
        assert e["ticker"] == "NVDA"
        assert e["rating"] == "Buy"
        assert e["pending"] is True
        assert e["raw"] is None

    def test_load_entries_multiple(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        log.store_decision("AAPL", "2026-01-11", DECISION_OVERWEIGHT)
        log.store_decision("MSFT", "2026-01-12", DECISION_NO_RATING)
        entries = log.load_entries()
        assert len(entries) == 3
        assert [e["ticker"] for e in entries] == ["NVDA", "AAPL", "MSFT"]

    def test_decision_content_preserved(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        assert log.load_entries()[0]["decision"] == DECISION_BUY.strip()

    # get_pending_entries

    def test_get_pending_returns_pending_only(self, tmp_path):
        log = make_log(tmp_path)
        _seed_completed(tmp_path, "NVDA", "2026-01-05", "Buy NVDA.", "Correct.")
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        pending = log.get_pending_entries()
        assert len(pending) == 1
        assert pending[0]["ticker"] == "NVDA"
        assert pending[0]["date"] == "2026-01-10"

    # get_past_context

    def test_get_past_context_empty(self, tmp_path):
        log = make_log(tmp_path)
        assert log.get_past_context("NVDA") == ""

    def test_get_past_context_pending_excluded(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        assert log.get_past_context("NVDA") == ""

    def test_get_past_context_same_ticker(self, tmp_path):
        log = make_log(tmp_path)
        _seed_completed(tmp_path, "NVDA", "2026-01-05", "Buy NVDA — AI capex thesis intact.", "Directionally correct.")
        ctx = log.get_past_context("NVDA")
        assert "Past analyses of NVDA" in ctx
        assert "Buy NVDA" in ctx

    def test_get_past_context_cross_ticker(self, tmp_path):
        log = make_log(tmp_path)
        _seed_completed(tmp_path, "AAPL", "2026-01-05", "Buy AAPL — Services growth.", "Correct.")
        ctx = log.get_past_context("NVDA")
        assert "Recent cross-ticker lessons" in ctx
        assert "Past analyses of NVDA" not in ctx

    def test_n_same_limit_respected(self, tmp_path):
        """Only the n_same most recent same-ticker entries are included."""
        log = make_log(tmp_path)
        for i in range(6):
            _seed_completed(tmp_path, "NVDA", f"2026-01-{i+1:02d}", f"Buy entry {i}.", "Correct.")
        ctx = log.get_past_context("NVDA", n_same=5)
        assert "Buy entry 0" not in ctx
        assert "Buy entry 5" in ctx

    def test_n_cross_limit_respected(self, tmp_path):
        """Only the n_cross most recent cross-ticker entries are included."""
        log = make_log(tmp_path)
        for i, ticker in enumerate(["AAPL", "MSFT", "GOOG", "META"]):
            _seed_completed(tmp_path, ticker, f"2026-01-{i+1:02d}", f"Buy {ticker}.", "Correct.")
        ctx = log.get_past_context("NVDA", n_cross=3)
        assert "AAPL" not in ctx
        assert "META" in ctx

    # No-op when config is None

    def test_no_log_path_is_noop(self):
        log = TradingMemoryLog(config=None)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        assert log.load_entries() == []
        assert log.get_past_context("NVDA") == ""

    # Rating parsing: markdown bold and numbered list formats

    def test_rating_parsed_from_bold_markdown(self, tmp_path):
        """**Rating**: Buy — markdown bold around the label must not prevent parsing."""
        decision = "**Rating**: Buy\nEnter at $190."
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", decision)
        assert log.load_entries()[0]["rating"] == "Buy"

    def test_rating_parsed_from_bold_value(self, tmp_path):
        """Rating: **Sell** — markdown bold around the value must not prevent parsing."""
        decision = "Rating: **Sell**\nExit immediately."
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", decision)
        assert log.load_entries()[0]["rating"] == "Sell"

    def test_rating_label_wins_over_prose_with_markdown(self, tmp_path):
        """Rating: **Sell** must win even when prose contains a conflicting rating word."""
        decision = (
            "The buy thesis is weakened by guidance.\n"
            "Rating: **Sell**\n"
            "Exit before earnings."
        )
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", decision)
        assert log.load_entries()[0]["rating"] == "Sell"

    def test_rating_parsed_from_numbered_list(self, tmp_path):
        """1. Rating: Buy — numbered list prefix must not prevent parsing."""
        decision = "1. Rating: Buy\nEnter at $190."
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", decision)
        assert log.load_entries()[0]["rating"] == "Buy"


# ---------------------------------------------------------------------------
# Deferred reflection: update_with_outcome, Reflector, _fetch_returns
# ---------------------------------------------------------------------------

class TestDeferredReflection:

    # update_with_outcome

    def test_update_replaces_pending_tag(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        log.update_with_outcome("NVDA", "2026-01-10", 0.042, 0.021, 5, "Momentum confirmed.")
        text = (tmp_path / "trading_memory.md").read_text(encoding="utf-8")
        assert "[2026-01-10 | NVDA | Buy | pending]" not in text
        assert "+4.2%" in text
        assert "+2.1%" in text
        assert "5d" in text

    def test_update_appends_reflection(self, tmp_path):
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        log.update_with_outcome("NVDA", "2026-01-10", 0.042, 0.021, 5, "Momentum confirmed.")
        entries = log.load_entries()
        assert len(entries) == 1
        e = entries[0]
        assert e["pending"] is False
        assert e["reflection"] == "Momentum confirmed."
        assert e["decision"] == DECISION_BUY.strip()

    def test_update_preserves_other_entries(self, tmp_path):
        """Only the matching entry is modified; all other entries remain unchanged."""
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        log.store_decision("AAPL", "2026-01-11", "Rating: Hold\nHold AAPL.")
        log.store_decision("MSFT", "2026-01-12", DECISION_SELL)
        log.update_with_outcome("AAPL", "2026-01-11", 0.01, -0.01, 5, "Neutral result.")
        entries = log.load_entries()
        assert len(entries) == 3
        nvda, aapl, msft = entries
        assert nvda["ticker"] == "NVDA" and nvda["pending"] is True
        assert aapl["ticker"] == "AAPL" and aapl["pending"] is False
        assert aapl["reflection"] == "Neutral result."
        assert msft["ticker"] == "MSFT" and msft["pending"] is True

    def test_update_atomic_write(self, tmp_path):
        """A pre-existing .tmp file is overwritten; the log is correctly updated."""
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        stale_tmp = tmp_path / "trading_memory.tmp"
        stale_tmp.write_text("GARBAGE CONTENT — should be overwritten", encoding="utf-8")
        log.update_with_outcome("NVDA", "2026-01-10", 0.042, 0.021, 5, "Correct.")
        assert not stale_tmp.exists()
        entries = log.load_entries()
        assert len(entries) == 1
        assert entries[0]["reflection"] == "Correct."
        assert entries[0]["pending"] is False

    def test_update_noop_when_no_log_path(self):
        log = TradingMemoryLog(config=None)
        log.update_with_outcome("NVDA", "2026-01-10", 0.05, 0.02, 5, "Reflection")

    def test_formatting_roundtrip_after_update(self, tmp_path):
        """All fields intact and blank line between tag and DECISION preserved after update."""
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-10", DECISION_BUY)
        log.update_with_outcome("NVDA", "2026-01-10", 0.042, 0.021, 5, "Momentum confirmed.")
        entries = log.load_entries()
        assert len(entries) == 1
        e = entries[0]
        assert e["pending"] is False
        assert e["decision"] == DECISION_BUY.strip()
        assert e["reflection"] == "Momentum confirmed."
        assert e["raw"] == "+4.2%"
        assert e["alpha"] == "+2.1%"
        assert e["holding"] == "5d"
        raw_text = (tmp_path / "trading_memory.md").read_text(encoding="utf-8")
        assert "[2026-01-10 | NVDA | Buy | +4.2% | +2.1% | 5d]\n\nDECISION:" in raw_text

    # Reflector.reflect_on_final_decision

    def test_reflect_on_final_decision_returns_llm_output(self):
        mock_llm = MagicMock()
        mock_llm.invoke.return_value.content = "Directionally correct. Thesis confirmed."
        reflector = Reflector(mock_llm)
        result = reflector.reflect_on_final_decision(
            final_decision=DECISION_BUY, raw_return=0.042, alpha_return=0.021
        )
        assert result == "Directionally correct. Thesis confirmed."
        mock_llm.invoke.assert_called_once()

    def test_reflect_on_final_decision_includes_returns_in_prompt(self):
        """Return figures are present in the human message sent to the LLM."""
        mock_llm = MagicMock()
        mock_llm.invoke.return_value.content = "Incorrect call."
        reflector = Reflector(mock_llm)
        reflector.reflect_on_final_decision(
            final_decision=DECISION_SELL, raw_return=-0.08, alpha_return=-0.05
        )
        messages = mock_llm.invoke.call_args[0][0]
        human_content = next(content for role, content in messages if role == "human")
        assert "-8.0%" in human_content
        assert "-5.0%" in human_content
        assert "Exit position immediately." in human_content

    # TradingAgentsGraph._fetch_returns

    def test_fetch_returns_valid_ticker(self):
        stock_prices = [100.0, 102.0, 104.0, 103.0, 105.0, 106.0]
        spy_prices   = [400.0, 402.0, 404.0, 403.0, 405.0, 406.0]
        mock_graph = MagicMock(spec=TradingAgentsGraph)
        with patch("yfinance.Ticker") as mock_ticker_cls:
            def _make_ticker(sym):
                m = MagicMock()
                m.history.return_value = _price_df(spy_prices if sym == "SPY" else stock_prices)
                return m
            mock_ticker_cls.side_effect = _make_ticker
            raw, alpha, days = TradingAgentsGraph._fetch_returns(mock_graph, "NVDA", "2026-01-05")
        assert raw is not None and alpha is not None and days is not None
        assert isinstance(raw, float) and isinstance(alpha, float) and isinstance(days, int)
        assert days == 5

    def test_fetch_returns_too_recent(self):
        """Only 1 data point available → returns (None, None, None), no crash."""
        mock_graph = MagicMock(spec=TradingAgentsGraph)
        with patch("yfinance.Ticker") as mock_ticker_cls:
            m = MagicMock()
            m.history.return_value = _price_df([100.0])
            mock_ticker_cls.return_value = m
            raw, alpha, days = TradingAgentsGraph._fetch_returns(mock_graph, "NVDA", "2026-04-19")
        assert raw is None and alpha is None and days is None

    def test_fetch_returns_delisted(self):
        """Empty DataFrame → returns (None, None, None), no crash."""
        mock_graph = MagicMock(spec=TradingAgentsGraph)
        with patch("yfinance.Ticker") as mock_ticker_cls:
            m = MagicMock()
            m.history.return_value = pd.DataFrame({"Close": []})
            mock_ticker_cls.return_value = m
            raw, alpha, days = TradingAgentsGraph._fetch_returns(mock_graph, "XXXXXFAKE", "2026-01-10")
        assert raw is None and alpha is None and days is None

    def test_fetch_returns_spy_shorter_than_stock(self):
        """SPY having fewer rows than the stock must not raise IndexError."""
        stock_prices = [100.0, 102.0, 104.0, 103.0, 105.0, 106.0]
        spy_prices   = [400.0, 402.0, 403.0]
        mock_graph = MagicMock(spec=TradingAgentsGraph)
        with patch("yfinance.Ticker") as mock_ticker_cls:
            def _make_ticker(sym):
                m = MagicMock()
                m.history.return_value = _price_df(spy_prices if sym == "SPY" else stock_prices)
                return m
            mock_ticker_cls.side_effect = _make_ticker
            raw, alpha, days = TradingAgentsGraph._fetch_returns(mock_graph, "NVDA", "2026-01-05")
        assert raw is not None and alpha is not None and days is not None
        assert days == 2

    # TradingAgentsGraph._resolve_pending_entries

    def test_resolve_skips_other_tickers(self, tmp_path):
        """Pending AAPL entry is not resolved when the run is for NVDA."""
        log = make_log(tmp_path)
        log.store_decision("AAPL", "2026-01-10", DECISION_BUY)
        mock_graph = MagicMock(spec=TradingAgentsGraph)
        mock_graph.memory_log = log
        mock_graph._fetch_returns = MagicMock(return_value=(0.05, 0.02, 5))
        TradingAgentsGraph._resolve_pending_entries(mock_graph, "NVDA")
        mock_graph._fetch_returns.assert_not_called()
        assert len(log.get_pending_entries()) == 1

    def test_resolve_marks_entry_completed(self, tmp_path):
        """After resolve, get_pending_entries() is empty and the entry has a REFLECTION."""
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-05", DECISION_BUY)
        mock_reflector = MagicMock()
        mock_reflector.reflect_on_final_decision.return_value = "Momentum confirmed."
        mock_graph = MagicMock(spec=TradingAgentsGraph)
        mock_graph.memory_log = log
        mock_graph.reflector = mock_reflector
        mock_graph._fetch_returns = MagicMock(return_value=(0.05, 0.02, 5))
        TradingAgentsGraph._resolve_pending_entries(mock_graph, "NVDA")
        assert log.get_pending_entries() == []
        entries = log.load_entries()
        assert len(entries) == 1
        assert entries[0]["pending"] is False
        assert entries[0]["reflection"] == "Momentum confirmed."
        assert "+5.0%" in entries[0]["raw"]
        assert "+2.0%" in entries[0]["alpha"]


# ---------------------------------------------------------------------------
# Portfolio Manager injection: past_context in state and prompt
# ---------------------------------------------------------------------------

class TestPortfolioManagerInjection:

    # past_context in initial state

    def test_past_context_in_initial_state(self):
        propagator = Propagator()
        state = propagator.create_initial_state("NVDA", "2026-01-10", past_context="some context")
        assert "past_context" in state
        assert state["past_context"] == "some context"

    def test_past_context_defaults_to_empty(self):
        propagator = Propagator()
        state = propagator.create_initial_state("NVDA", "2026-01-10")
        assert state["past_context"] == ""

    # PM prompt

    def test_pm_prompt_includes_past_context(self):
        captured = {}
        llm = _structured_pm_llm(captured)
        pm_node = create_portfolio_manager(llm)
        state = _make_pm_state(past_context="[2026-01-05 | NVDA | Buy | +5.0% | +2.0% | 5d]\nGreat call.")
        pm_node(state)
        assert "Lessons from prior decisions and outcomes" in captured["prompt"]
        assert "Great call." in captured["prompt"]

    def test_pm_no_past_context_no_section(self):
        """PM prompt omits the lessons section entirely when past_context is empty."""
        captured = {}
        llm = _structured_pm_llm(captured)
        pm_node = create_portfolio_manager(llm)
        state = _make_pm_state(past_context="")
        pm_node(state)
        assert "Lessons from prior decisions" not in captured["prompt"]

    def test_pm_returns_rendered_markdown_with_rating(self):
        """The structured PortfolioDecision is rendered to markdown that
        downstream consumers (memory log, signal processor, CLI display)
        can parse without any extra LLM call."""
        captured = {}
        decision = PortfolioDecision(
            rating=PortfolioRating.OVERWEIGHT,
            executive_summary="Build position gradually over the next two weeks.",
            investment_thesis="AI capex cycle remains intact; institutional flows constructive.",
            price_target=215.0,
            time_horizon="3-6 months",
        )
        llm = _structured_pm_llm(captured, decision)
        pm_node = create_portfolio_manager(llm)
        result = pm_node(_make_pm_state())
        md = result["final_trade_decision"]
        assert "**Rating**: Overweight" in md
        assert "**Executive Summary**: Build position gradually" in md
        assert "**Investment Thesis**: AI capex cycle" in md
        assert "**Price Target**: 215.0" in md
        assert "**Time Horizon**: 3-6 months" in md

    def test_pm_falls_back_to_freetext_when_structured_unavailable(self):
        """If a provider does not support with_structured_output, the agent
        falls back to a plain invoke and returns whatever prose the model
        produced, so the pipeline never blocks."""
        plain_response = "**Rating**: Sell\n\nExit ahead of guidance."
        llm = MagicMock()
        llm.with_structured_output.side_effect = NotImplementedError("provider unsupported")
        llm.invoke.return_value = MagicMock(content=plain_response)
        pm_node = create_portfolio_manager(llm)
        result = pm_node(_make_pm_state())
        assert result["final_trade_decision"] == plain_response

    # get_past_context ordering and limits

    def test_same_ticker_prioritised(self, tmp_path):
        """Same-ticker entries in same-ticker section; cross-ticker entries in cross-ticker section."""
        log = make_log(tmp_path)
        _resolve_entry(log, "NVDA", "2026-01-05", DECISION_BUY, "Momentum confirmed.")
        _resolve_entry(log, "AAPL", "2026-01-06", DECISION_SELL, "Overvalued.")
        result = log.get_past_context("NVDA")
        assert "Past analyses of NVDA" in result
        assert "Recent cross-ticker lessons" in result
        same_block, cross_block = result.split("Recent cross-ticker lessons")
        assert "NVDA" in same_block
        assert "AAPL" in cross_block

    def test_cross_ticker_reflection_only(self, tmp_path):
        """Cross-ticker entries show only the REFLECTION text, not the full DECISION."""
        log = make_log(tmp_path)
        _resolve_entry(log, "AAPL", "2026-01-06", DECISION_SELL, "Overvalued correction.")
        result = log.get_past_context("NVDA")
        assert "Overvalued correction." in result
        assert "Exit position immediately." not in result

    def test_n_same_limit_respected(self, tmp_path):
        """More than 5 same-ticker completed entries → only 5 injected."""
        log = make_log(tmp_path)
        for i in range(7):
            _resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
        result = log.get_past_context("NVDA", n_same=5)
        lessons_present = sum(1 for i in range(7) if f"Lesson {i}." in result)
        assert lessons_present == 5

    def test_n_cross_limit_respected(self, tmp_path):
        """More than 3 cross-ticker completed entries → only 3 injected."""
        log = make_log(tmp_path)
        tickers = ["AAPL", "MSFT", "TSLA", "AMZN", "GOOG"]
        for i, ticker in enumerate(tickers):
            _resolve_entry(log, ticker, f"2026-01-{i+1:02d}", DECISION_BUY, f"{ticker} lesson.")
        result = log.get_past_context("NVDA", n_cross=3)
        cross_count = sum(result.count(f"{t} lesson.") for t in tickers)
        assert cross_count == 3

    # Full A→B→C integration cycle

    def test_full_cycle_store_resolve_inject(self, tmp_path):
        """store pending → resolve with outcome → past_context non-empty for PM."""
        log = make_log(tmp_path)
        log.store_decision("NVDA", "2026-01-05", DECISION_BUY)
        assert len(log.get_pending_entries()) == 1
        assert log.get_past_context("NVDA") == ""
        log.update_with_outcome("NVDA", "2026-01-05", 0.05, 0.02, 5, "Correct call.")
        assert log.get_pending_entries() == []
        past_ctx = log.get_past_context("NVDA")
        assert past_ctx != ""
        assert "NVDA" in past_ctx
        assert "Correct call." in past_ctx
        assert "DECISION:" in past_ctx
        assert "REFLECTION:" in past_ctx


# ---------------------------------------------------------------------------
# Legacy removal: BM25 / FinancialSituationMemory fully gone
# ---------------------------------------------------------------------------

class TestLegacyRemoval:

    def test_financial_situation_memory_removed(self):
        """FinancialSituationMemory must not be importable from the memory module."""
        import tradingagents.agents.utils.memory as m
        assert not hasattr(m, "FinancialSituationMemory")

    def test_bm25_not_imported(self):
        """rank_bm25 must not be present in the memory module namespace."""
        import tradingagents.agents.utils.memory as m
        assert not hasattr(m, "BM25Okapi")

    def test_reflect_and_remember_removed(self):
        """TradingAgentsGraph must not expose reflect_and_remember."""
        assert not hasattr(TradingAgentsGraph, "reflect_and_remember")

    def test_portfolio_manager_no_memory_param(self):
        """create_portfolio_manager accepts only llm; passing memory= raises TypeError."""
        mock_llm = MagicMock()
        create_portfolio_manager(mock_llm)
        with pytest.raises(TypeError):
            create_portfolio_manager(mock_llm, memory=MagicMock())

    def test_full_pipeline_no_regression(self, tmp_path):
        """propagate() completes and stores the decision after the redesign."""
        import functools

        fake_state = {
            "final_trade_decision": "Rating: Buy\nBuy NVDA.",
            "company_of_interest": "NVDA",
            "trade_date": "2026-01-10",
            "market_report": "",
            "sentiment_report": "",
            "news_report": "",
            "fundamentals_report": "",
            "investment_debate_state": {
                "bull_history": "", "bear_history": "", "history": "",
                "current_response": "", "judge_decision": "",
            },
            "investment_plan": "",
            "trader_investment_plan": "",
            "risk_debate_state": {
                "aggressive_history": "", "conservative_history": "",
                "neutral_history": "", "history": "", "judge_decision": "",
                "current_aggressive_response": "", "current_conservative_response": "",
                "current_neutral_response": "", "count": 1, "latest_speaker": "",
            },
        }
        mock_graph = MagicMock()
        mock_graph.memory_log = TradingMemoryLog({"memory_log_path": str(tmp_path / "mem.md")})
        mock_graph.log_states_dict = {}
        mock_graph.debug = False
        mock_graph.config = {"results_dir": str(tmp_path)}
        mock_graph.graph.invoke.return_value = fake_state
        mock_graph.propagator.create_initial_state.return_value = fake_state
        mock_graph.propagator.get_graph_args.return_value = {}
        mock_graph.signal_processor.process_signal.return_value = "Buy"
        # Bind the real _run_graph so propagate's call to self._run_graph executes
        # the actual write path instead of the auto-MagicMock.
        mock_graph._run_graph = functools.partial(
            TradingAgentsGraph._run_graph, mock_graph
        )
        TradingAgentsGraph.propagate(mock_graph, "NVDA", "2026-01-10")
        entries = mock_graph.memory_log.load_entries()
        assert len(entries) == 1
        assert entries[0]["ticker"] == "NVDA"
        assert entries[0]["pending"] is True