Merge remote-tracking branch 'upstream/main' into analyst-phase1-observability

# Conflicts:
#	tradingagents/default_config.py
#	tradingagents/graph/setup.py

tests/conftest.py

@@ -0,0 +1,46 @@
+"""Shared pytest fixtures that prevent CI hangs when API keys are absent."""
+
+import os
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+
+def pytest_configure(config):
+    for marker in ("unit", "integration", "smoke"):
+        config.addinivalue_line("markers", f"{marker}: {marker}-level tests")
+
+
+_API_KEY_ENV_VARS = (
+    "OPENAI_API_KEY",
+    "GOOGLE_API_KEY",
+    "ANTHROPIC_API_KEY",
+    "XAI_API_KEY",
+    "DEEPSEEK_API_KEY",
+    "DASHSCOPE_API_KEY",
+    "DASHSCOPE_CN_API_KEY",
+    "ZHIPU_API_KEY",
+    "ZHIPU_CN_API_KEY",
+    "MINIMAX_API_KEY",
+    "MINIMAX_CN_API_KEY",
+    "OPENROUTER_API_KEY",
+    "AZURE_OPENAI_API_KEY",
+    "ALPHA_VANTAGE_API_KEY",
+)
+
+
+@pytest.fixture(autouse=True)
+def _dummy_api_keys(monkeypatch):
+    for env_var in _API_KEY_ENV_VARS:
+        monkeypatch.setenv(env_var, os.environ.get(env_var, "placeholder"))
+
+
+@pytest.fixture()
+def mock_llm_client():
+    client = MagicMock()
+    client.get_llm.return_value = MagicMock()
+    with patch(
+        "tradingagents.llm_clients.factory.create_llm_client",
+        return_value=client,
+    ):
+        yield client

tests/test_api_key_env.py

@@ -0,0 +1,149 @@
+"""Tests for the canonical provider->env-var mapping and the CLI key-prompt helper."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+from tradingagents.llm_clients.api_key_env import PROVIDER_API_KEY_ENV, get_api_key_env
+
+
+# ---- Mapping coverage -----------------------------------------------------
+
+
+def test_every_select_llm_provider_choice_has_an_entry():
+    """select_llm_provider() must not present a provider the mapping doesn't know about."""
+    # Mirrors the dropdown order in cli/utils.select_llm_provider so the two
+    # stay in lockstep. Region-specific keys (qwen-cn / minimax-cn / glm-cn)
+    # are reached via the secondary region prompt, so they must also be present.
+    expected = {
+        "openai", "google", "anthropic", "xai", "deepseek",
+        "qwen", "qwen-cn",
+        "glm", "glm-cn",
+        "minimax", "minimax-cn",
+        "openrouter", "azure", "ollama",
+    }
+    assert expected.issubset(PROVIDER_API_KEY_ENV.keys())
+
+
+@pytest.mark.parametrize(
+    "provider,env_var",
+    [
+        ("openai",     "OPENAI_API_KEY"),
+        ("anthropic",  "ANTHROPIC_API_KEY"),
+        ("google",     "GOOGLE_API_KEY"),
+        ("azure",      "AZURE_OPENAI_API_KEY"),
+        ("xai",        "XAI_API_KEY"),
+        ("deepseek",   "DEEPSEEK_API_KEY"),
+        ("qwen",       "DASHSCOPE_API_KEY"),
+        ("qwen-cn",    "DASHSCOPE_CN_API_KEY"),
+        ("glm",        "ZHIPU_API_KEY"),
+        ("glm-cn",     "ZHIPU_CN_API_KEY"),
+        ("minimax",    "MINIMAX_API_KEY"),
+        ("minimax-cn", "MINIMAX_CN_API_KEY"),
+        ("openrouter", "OPENROUTER_API_KEY"),
+    ],
+)
+def test_known_providers_resolve(provider, env_var):
+    assert get_api_key_env(provider) == env_var
+
+
+def test_ollama_has_no_key():
+    assert get_api_key_env("ollama") is None
+
+
+def test_unknown_provider_returns_none():
+    assert get_api_key_env("not-a-real-provider") is None
+
+
+def test_case_insensitive_lookup():
+    assert get_api_key_env("OpenAI") == "OPENAI_API_KEY"
+    assert get_api_key_env("QWEN-CN") == "DASHSCOPE_CN_API_KEY"
+
+
+# ---- ensure_api_key behavior ---------------------------------------------
+
+
+@pytest.fixture
+def cli_utils(monkeypatch):
+    """Import cli.utils with a fresh environment so module-level state is consistent."""
+    import importlib
+    import cli.utils as cli_utils_module
+    return importlib.reload(cli_utils_module)
+
+
+def test_ensure_api_key_returns_existing(monkeypatch, cli_utils):
+    monkeypatch.setenv("OPENAI_API_KEY", "sk-already-set")
+    result = cli_utils.ensure_api_key("openai")
+    assert result == "sk-already-set"
+
+
+def test_ensure_api_key_no_op_for_ollama(monkeypatch, cli_utils):
+    # Even with no env var set, ollama should not prompt and should return None.
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    with patch.object(cli_utils, "questionary") as mock_q:
+        result = cli_utils.ensure_api_key("ollama")
+    assert result is None
+    mock_q.password.assert_not_called()
+
+
+def test_ensure_api_key_unknown_provider_no_prompt(monkeypatch, cli_utils):
+    with patch.object(cli_utils, "questionary") as mock_q:
+        result = cli_utils.ensure_api_key("totally-fake-provider")
+    assert result is None
+    mock_q.password.assert_not_called()
+
+
+def test_ensure_api_key_prompts_and_writes_to_env(monkeypatch, tmp_path, cli_utils):
+    """When key is missing, user-pasted value must be written to .env AND os.environ."""
+    monkeypatch.delenv("DEEPSEEK_API_KEY", raising=False)
+    monkeypatch.chdir(tmp_path)
+
+    fake_prompt = type("P", (), {"ask": staticmethod(lambda: "sk-deepseek-test")})()
+    with patch.object(cli_utils.questionary, "password", return_value=fake_prompt):
+        result = cli_utils.ensure_api_key("deepseek")
+
+    assert result == "sk-deepseek-test"
+    assert os.environ["DEEPSEEK_API_KEY"] == "sk-deepseek-test"
+    env_file = tmp_path / ".env"
+    assert env_file.exists()
+    assert "DEEPSEEK_API_KEY" in env_file.read_text()
+    assert "sk-deepseek-test" in env_file.read_text()
+
+
+def test_ensure_api_key_user_cancels_returns_none(monkeypatch, tmp_path, cli_utils):
+    """Empty prompt response (user cancelled) must not write to .env."""
+    monkeypatch.delenv("XAI_API_KEY", raising=False)
+    monkeypatch.chdir(tmp_path)
+
+    fake_prompt = type("P", (), {"ask": staticmethod(lambda: None)})()
+    with patch.object(cli_utils.questionary, "password", return_value=fake_prompt):
+        result = cli_utils.ensure_api_key("xai")
+
+    assert result is None
+    assert "XAI_API_KEY" not in os.environ
+    # .env may or may not exist depending on find_dotenv's walk, but if it
+    # does it must not contain the key.
+    env_file = tmp_path / ".env"
+    if env_file.exists():
+        assert "XAI_API_KEY" not in env_file.read_text()
+
+
+def test_ensure_api_key_updates_existing_env_file(monkeypatch, tmp_path, cli_utils):
+    """An existing .env with other keys must be preserved on writeback."""
+    monkeypatch.delenv("OPENROUTER_API_KEY", raising=False)
+    monkeypatch.chdir(tmp_path)
+    env_file = tmp_path / ".env"
+    env_file.write_text("OPENAI_API_KEY=sk-existing\nOTHER=value\n")
+
+    fake_prompt = type("P", (), {"ask": staticmethod(lambda: "sk-openrouter-new")})()
+    with patch.object(cli_utils.questionary, "password", return_value=fake_prompt):
+        cli_utils.ensure_api_key("openrouter")
+
+    content = env_file.read_text()
+    assert "OPENAI_API_KEY" in content and "sk-existing" in content
+    assert "OTHER=value" in content
+    assert "OPENROUTER_API_KEY" in content and "sk-openrouter-new" in content

tests/test_capabilities.py

@@ -0,0 +1,107 @@
+"""Unit tests for the LLM capability table."""
+
+import pytest
+
+from tradingagents.llm_clients.capabilities import (
+    ModelCapabilities,
+    get_capabilities,
+)
+
+
+@pytest.mark.unit
+class TestExactIdMatches:
+    def test_deepseek_chat_supports_tool_choice(self):
+        caps = get_capabilities("deepseek-chat")
+        assert caps.supports_tool_choice is True
+
+    def test_deepseek_reasoner_rejects_tool_choice(self):
+        caps = get_capabilities("deepseek-reasoner")
+        assert caps.supports_tool_choice is False
+        assert caps.requires_reasoning_content_roundtrip is True
+
+    def test_deepseek_v4_flash_rejects_tool_choice(self):
+        caps = get_capabilities("deepseek-v4-flash")
+        assert caps.supports_tool_choice is False
+        assert caps.requires_reasoning_content_roundtrip is True
+
+    def test_deepseek_v4_pro_rejects_tool_choice(self):
+        caps = get_capabilities("deepseek-v4-pro")
+        assert caps.supports_tool_choice is False
+        assert caps.requires_reasoning_content_roundtrip is True
+
+
+@pytest.mark.unit
+class TestPatternMatches:
+    """Forward-compat regex patterns catch unknown DeepSeek and MiniMax variants."""
+
+    def test_future_deepseek_v5_inherits_thinking_quirks(self):
+        caps = get_capabilities("deepseek-v5-flash")
+        assert caps.supports_tool_choice is False
+        assert caps.requires_reasoning_content_roundtrip is True
+
+    def test_future_deepseek_v9_inherits_thinking_quirks(self):
+        caps = get_capabilities("deepseek-v9-anything")
+        assert caps.supports_tool_choice is False
+
+    def test_reasoner_variant_inherits_thinking_quirks(self):
+        caps = get_capabilities("deepseek-reasoner-pro")
+        assert caps.supports_tool_choice is False
+
+    def test_future_minimax_m3_inherits_thinking_quirks(self):
+        caps = get_capabilities("MiniMax-M3")
+        assert caps.supports_tool_choice is False
+
+    def test_future_minimax_m4_highspeed_inherits_thinking_quirks(self):
+        caps = get_capabilities("MiniMax-M4-highspeed")
+        assert caps.supports_tool_choice is False
+
+
+@pytest.mark.unit
+class TestMinimaxExactMatches:
+    """MiniMax M2.x models reject langchain's function-spec dict tool_choice
+    (official API enum: none/auto only)."""
+
+    def test_m2_7_rejects_tool_choice(self):
+        caps = get_capabilities("MiniMax-M2.7")
+        assert caps.supports_tool_choice is False
+        assert caps.supports_json_mode is False  # only MiniMax-Text-01 supports json_object
+
+    def test_m2_7_highspeed_rejects_tool_choice(self):
+        assert get_capabilities("MiniMax-M2.7-highspeed").supports_tool_choice is False
+
+    def test_m2_1_rejects_tool_choice(self):
+        assert get_capabilities("MiniMax-M2.1").supports_tool_choice is False
+
+    def test_m2_base_rejects_tool_choice(self):
+        assert get_capabilities("MiniMax-M2").supports_tool_choice is False
+
+
+@pytest.mark.unit
+class TestDefault:
+    """Unknown / non-DeepSeek models get the permissive default."""
+
+    def test_gpt_default(self):
+        caps = get_capabilities("gpt-4.1")
+        assert caps.supports_tool_choice is True
+        assert caps.preferred_structured_method == "function_calling"
+
+    def test_grok_default(self):
+        caps = get_capabilities("grok-4-0709")
+        assert caps.supports_tool_choice is True
+
+    def test_unknown_model_default(self):
+        caps = get_capabilities("totally-made-up-model-id")
+        assert caps.supports_tool_choice is True
+
+    def test_exact_match_precedes_pattern(self):
+        """deepseek-chat must NOT match the v\\d regex."""
+        caps = get_capabilities("deepseek-chat")
+        assert caps.supports_tool_choice is True
+
+
+@pytest.mark.unit
+def test_capabilities_dataclass_is_frozen():
+    """Capability rows are immutable so they can be safely shared."""
+    caps = get_capabilities("deepseek-chat")
+    with pytest.raises(Exception):
+        caps.supports_tool_choice = False  # type: ignore[misc]

tests/test_checkpoint_resume.py

@@ -0,0 +1,147 @@
+"""Test checkpoint resume: crash mid-analysis, re-run resumes from last node."""
+
+import sqlite3
+import tempfile
+import unittest
+from pathlib import Path
+from typing import TypedDict
+
+from langgraph.checkpoint.sqlite import SqliteSaver
+from langgraph.graph import END, StateGraph
+
+from tradingagents.graph.checkpointer import (
+    checkpoint_step,
+    clear_checkpoint,
+    get_checkpointer,
+    has_checkpoint,
+    thread_id,
+)
+
+# Mutable flag to simulate crash on first run
+_should_crash = False
+
+
+class _SimpleState(TypedDict):
+    count: int
+
+
+def _node_a(state: _SimpleState) -> dict:
+    return {"count": state["count"] + 1}
+
+
+def _node_b(state: _SimpleState) -> dict:
+    if _should_crash:
+        raise RuntimeError("simulated mid-analysis crash")
+    return {"count": state["count"] + 10}
+
+
+def _build_graph() -> StateGraph:
+    builder = StateGraph(_SimpleState)
+    builder.add_node("analyst", _node_a)
+    builder.add_node("trader", _node_b)
+    builder.set_entry_point("analyst")
+    builder.add_edge("analyst", "trader")
+    builder.add_edge("trader", END)
+    return builder
+
+
+class TestCheckpointResume(unittest.TestCase):
+    def setUp(self):
+        self.tmpdir = tempfile.mkdtemp()
+        self.ticker = "TEST"
+        self.date = "2026-04-20"
+
+    def test_crash_and_resume(self):
+        """Crash at 'trader' node, then resume from checkpoint."""
+        global _should_crash
+        builder = _build_graph()
+        tid = thread_id(self.ticker, self.date)
+        cfg = {"configurable": {"thread_id": tid}}
+
+        # Run 1: crash at trader node
+        _should_crash = True
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            with self.assertRaises(RuntimeError):
+                graph.invoke({"count": 0}, config=cfg)
+
+        # Checkpoint should exist at step 1 (analyst completed)
+        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date))
+        step = checkpoint_step(self.tmpdir, self.ticker, self.date)
+        self.assertEqual(step, 1)
+
+        # Run 2: resume — trader succeeds this time
+        _should_crash = False
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            result = graph.invoke(None, config=cfg)
+
+        # analyst added 1, trader added 10 → 11
+        self.assertEqual(result["count"], 11)
+
+    def test_clear_checkpoint_allows_fresh_start(self):
+        """After clearing, the graph starts from scratch."""
+        global _should_crash
+        builder = _build_graph()
+        tid = thread_id(self.ticker, self.date)
+        cfg = {"configurable": {"thread_id": tid}}
+
+        # Create a checkpoint by crashing
+        _should_crash = True
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            with self.assertRaises(RuntimeError):
+                graph.invoke({"count": 0}, config=cfg)
+
+        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date))
+
+        # Clear it
+        clear_checkpoint(self.tmpdir, self.ticker, self.date)
+        self.assertFalse(has_checkpoint(self.tmpdir, self.ticker, self.date))
+
+        # Fresh run succeeds from scratch
+        _should_crash = False
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            result = graph.invoke({"count": 0}, config=cfg)
+
+        self.assertEqual(result["count"], 11)
+
+
+    def test_different_date_starts_fresh(self):
+        """A different date must NOT resume from an existing checkpoint."""
+        global _should_crash
+        builder = _build_graph()
+        date2 = "2026-04-21"
+
+        # Run with date1 — crash to leave a checkpoint
+        _should_crash = True
+        tid1 = thread_id(self.ticker, self.date)
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            with self.assertRaises(RuntimeError):
+                graph.invoke({"count": 0}, config={"configurable": {"thread_id": tid1}})
+
+        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date))
+
+        # date2 should have no checkpoint
+        self.assertFalse(has_checkpoint(self.tmpdir, self.ticker, date2))
+
+        # Run with date2 — should start fresh and succeed
+        _should_crash = False
+        tid2 = thread_id(self.ticker, date2)
+        self.assertNotEqual(tid1, tid2)
+
+        with get_checkpointer(self.tmpdir, self.ticker) as saver:
+            graph = builder.compile(checkpointer=saver)
+            result = graph.invoke({"count": 0}, config={"configurable": {"thread_id": tid2}})
+
+        # Fresh run: analyst +1, trader +10 = 11
+        self.assertEqual(result["count"], 11)
+
+        # Original date checkpoint still exists (untouched)
+        self.assertTrue(has_checkpoint(self.tmpdir, self.ticker, self.date))
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_dataflows_config.py

@@ -0,0 +1,61 @@
+"""Config isolation: get/set must not leak nested-dict references."""
+
+import copy
+import unittest
+
+import pytest
+
+import tradingagents.default_config as default_config
+from tradingagents.dataflows.config import get_config, set_config
+
+
+@pytest.mark.unit
+class DataflowsConfigIsolationTests(unittest.TestCase):
+    def setUp(self):
+        set_config(copy.deepcopy(default_config.DEFAULT_CONFIG))
+
+    def test_get_config_returns_deep_copy(self):
+        cfg = get_config()
+        cfg["data_vendors"]["core_stock_apis"] = "alpha_vantage"
+        cfg["tool_vendors"]["get_stock_data"] = "alpha_vantage"
+
+        fresh = get_config()
+        self.assertEqual(fresh["data_vendors"]["core_stock_apis"], "yfinance")
+        self.assertNotIn("get_stock_data", fresh["tool_vendors"])
+
+    def test_set_config_does_not_alias_caller_nested_dicts(self):
+        custom = copy.deepcopy(default_config.DEFAULT_CONFIG)
+        custom["data_vendors"]["core_stock_apis"] = "alpha_vantage"
+        custom["tool_vendors"]["get_stock_data"] = "alpha_vantage"
+
+        set_config(custom)
+
+        custom["data_vendors"]["core_stock_apis"] = "yfinance"
+        custom["tool_vendors"]["get_stock_data"] = "yfinance"
+
+        fresh = get_config()
+        self.assertEqual(fresh["data_vendors"]["core_stock_apis"], "alpha_vantage")
+        self.assertEqual(fresh["tool_vendors"]["get_stock_data"], "alpha_vantage")
+
+    def test_partial_nested_update_preserves_existing_defaults(self):
+        set_config(
+            {
+                "data_vendors": {
+                    "core_stock_apis": "alpha_vantage",
+                }
+            }
+        )
+
+        fresh = get_config()
+        self.assertEqual(fresh["data_vendors"]["core_stock_apis"], "alpha_vantage")
+        self.assertEqual(fresh["data_vendors"]["technical_indicators"], "yfinance")
+        self.assertEqual(fresh["data_vendors"]["fundamental_data"], "yfinance")
+        self.assertEqual(fresh["data_vendors"]["news_data"], "yfinance")
+
+    def test_nested_dict_updates_merge_one_level_deep(self):
+        set_config({"tool_vendors": {"get_stock_data": "alpha_vantage"}})
+        set_config({"tool_vendors": {"get_news": "alpha_vantage"}})
+
+        fresh = get_config()
+        self.assertEqual(fresh["tool_vendors"]["get_stock_data"], "alpha_vantage")
+        self.assertEqual(fresh["tool_vendors"]["get_news"], "alpha_vantage")

tests/test_deepseek_reasoning.py

@@ -0,0 +1,240 @@
+"""Tests for DeepSeekChatOpenAI thinking-mode behaviour.
+
+Two pieces verified:
+
+1. ``reasoning_content`` is captured on receive into the AIMessage's
+   ``additional_kwargs`` and re-attached on send so DeepSeek's API
+   sees the same value across turns.
+2. ``with_structured_output`` consults the capability table and
+   suppresses ``tool_choice`` for models that reject it (V4 + reasoner),
+   matching DeepSeek's official tool-calling pattern at
+   https://api-docs.deepseek.com/guides/tool_calls.
+"""
+
+import os
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.prompt_values import ChatPromptValue
+from pydantic import BaseModel
+
+from tradingagents.llm_clients.openai_client import (
+    DeepSeekChatOpenAI,
+    NormalizedChatOpenAI,
+    _input_to_messages,
+)
+
+
+# ---------------------------------------------------------------------------
+# _input_to_messages — the helper that handles list / ChatPromptValue / other
+# (Gemini bot review note: non-list inputs must also work)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestInputToMessages:
+    def test_list_input_returned_as_is(self):
+        msgs = [HumanMessage(content="hi")]
+        assert _input_to_messages(msgs) is msgs
+
+    def test_chat_prompt_value_unwrapped(self):
+        msgs = [HumanMessage(content="hi")]
+        prompt_value = ChatPromptValue(messages=msgs)
+        assert _input_to_messages(prompt_value) == msgs
+
+    def test_string_input_yields_empty_list(self):
+        # A bare string isn't a message-bearing input; the caller's normal
+        # langchain conversion happens upstream of _get_request_payload.
+        assert _input_to_messages("hello") == []
+
+
+# ---------------------------------------------------------------------------
+# Reasoning content propagation across turns
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestDeepSeekReasoningContent:
+    def _client(self):
+        os.environ.setdefault("DEEPSEEK_API_KEY", "placeholder")
+        return DeepSeekChatOpenAI(
+            model="deepseek-v4-flash",
+            api_key="placeholder",
+            base_url="https://api.deepseek.com",
+        )
+
+    def test_capture_on_receive(self):
+        """When the response carries reasoning_content, it lands on the
+        AIMessage's additional_kwargs so the next turn can echo it back."""
+        client = self._client()
+        result = client._create_chat_result(
+            {
+                "model": "deepseek-v4-flash",
+                "choices": [
+                    {
+                        "index": 0,
+                        "message": {
+                            "role": "assistant",
+                            "content": "Plan: buy NVDA.",
+                            "reasoning_content": "Step 1: trend is up. Step 2: ...",
+                        },
+                        "finish_reason": "stop",
+                    }
+                ],
+                "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+            }
+        )
+        ai = result.generations[0].message
+        assert ai.additional_kwargs["reasoning_content"] == "Step 1: trend is up. Step 2: ..."
+
+    def test_propagate_on_send(self):
+        """When an outgoing AIMessage carries reasoning_content, the request
+        payload echoes it on the corresponding message dict."""
+        client = self._client()
+        prior = AIMessage(
+            content="Plan",
+            additional_kwargs={"reasoning_content": "weighed bull case"},
+        )
+        new_user = HumanMessage(content="Refine.")
+        payload = client._get_request_payload([prior, new_user])
+        # Find the assistant message in the payload
+        assistant_dicts = [m for m in payload["messages"] if m.get("role") == "assistant"]
+        assert assistant_dicts, "assistant message missing from outgoing payload"
+        assert assistant_dicts[0]["reasoning_content"] == "weighed bull case"
+
+    def test_propagate_through_chat_prompt_value(self):
+        """Gemini bot review note: non-list inputs (ChatPromptValue) must
+        also propagate reasoning_content."""
+        client = self._client()
+        prior = AIMessage(
+            content="Plan",
+            additional_kwargs={"reasoning_content": "weighed bull case"},
+        )
+        prompt_value = ChatPromptValue(messages=[prior, HumanMessage(content="Refine.")])
+        payload = client._get_request_payload(prompt_value)
+        assistant_dicts = [m for m in payload["messages"] if m.get("role") == "assistant"]
+        assert assistant_dicts[0]["reasoning_content"] == "weighed bull case"
+
+
+# ---------------------------------------------------------------------------
+# Capability-driven structured output: tool_choice suppressed for V4 + reasoner
+# ---------------------------------------------------------------------------
+
+
+def _bound_kwargs(runnable):
+    """Extract bind() kwargs from a with_structured_output result."""
+    first = runnable.steps[0] if hasattr(runnable, "steps") else runnable
+    return getattr(first, "kwargs", {})
+
+
+@pytest.mark.unit
+class TestStructuredOutputCapabilityDispatch:
+    """DeepSeek V4 and reasoner reject the tool_choice parameter
+    (official guide: api-docs.deepseek.com/guides/tool_calls passes
+    tools=[...] without tool_choice). Verify the capability dispatch
+    suppresses tool_choice for those models and sends it for chat."""
+
+    class _Sample(BaseModel):
+        answer: str
+
+    def _client(self, model):
+        return DeepSeekChatOpenAI(
+            model=model, api_key="placeholder", base_url="https://api.deepseek.com",
+        )
+
+    def test_chat_sends_tool_choice(self):
+        bound = self._client("deepseek-chat").with_structured_output(self._Sample)
+        assert _bound_kwargs(bound).get("tool_choice") is not None
+
+    def test_reasoner_suppresses_tool_choice(self):
+        bound = self._client("deepseek-reasoner").with_structured_output(self._Sample)
+        # tool_choice is either absent or explicitly None — both are valid
+        # signals that langchain's bind_tools will skip the parameter.
+        assert _bound_kwargs(bound).get("tool_choice") in (None, ...) or \
+            "tool_choice" not in _bound_kwargs(bound)
+
+    def test_v4_flash_suppresses_tool_choice(self):
+        bound = self._client("deepseek-v4-flash").with_structured_output(self._Sample)
+        assert _bound_kwargs(bound).get("tool_choice") is None or \
+            "tool_choice" not in _bound_kwargs(bound)
+
+    def test_v4_pro_suppresses_tool_choice(self):
+        bound = self._client("deepseek-v4-pro").with_structured_output(self._Sample)
+        assert _bound_kwargs(bound).get("tool_choice") is None or \
+            "tool_choice" not in _bound_kwargs(bound)
+
+    def test_future_v_variant_via_regex(self):
+        """Forward-compat: unknown deepseek-v\\d-* IDs inherit V4 quirks."""
+        bound = self._client("deepseek-v5-hypothetical").with_structured_output(self._Sample)
+        assert _bound_kwargs(bound).get("tool_choice") is None or \
+            "tool_choice" not in _bound_kwargs(bound)
+
+    def test_schema_is_still_bound_as_tool(self):
+        """tool_choice is suppressed, but the schema is still bound as a tool —
+        exactly matching DeepSeek's official tool-calling examples."""
+        bound = self._client("deepseek-reasoner").with_structured_output(self._Sample)
+        kwargs = _bound_kwargs(bound)
+        tools = kwargs.get("tools", [])
+        assert any(
+            t.get("function", {}).get("name") == "_Sample" for t in tools
+        ), f"schema not bound as a tool: {tools}"
+
+
+# ---------------------------------------------------------------------------
+# Live API: structured output round-trips against the real DeepSeek backend
+# ---------------------------------------------------------------------------
+
+
+def _has_real_deepseek_key():
+    key = os.environ.get("DEEPSEEK_API_KEY", "")
+    return bool(key) and key != "placeholder"
+
+
+@pytest.mark.integration
+@pytest.mark.skipif(
+    not _has_real_deepseek_key(),
+    reason="DEEPSEEK_API_KEY not set (or placeholder); skipping live API call",
+)
+class TestDeepSeekLiveStructuredOutput:
+    """End-to-end: a real DeepSeek V4-flash call returns a typed instance.
+
+    Verifies the no-tool_choice path doesn't trigger the 400 reported in
+    issue #678 and that the structured-output binding still parses to a
+    Pydantic instance.
+    """
+
+    class _Pick(BaseModel):
+        action: str
+        confidence: float
+
+    def test_v4_flash_returns_structured_output(self):
+        client = DeepSeekChatOpenAI(
+            model="deepseek-v4-flash",
+            api_key=os.environ["DEEPSEEK_API_KEY"],
+            base_url="https://api.deepseek.com",
+            timeout=60,
+        )
+        bound = client.with_structured_output(self._Pick)
+        result = bound.invoke(
+            "Pick BUY or SELL or HOLD for a tech stock with strong earnings. "
+            "Confidence is a float between 0 and 1."
+        )
+        assert isinstance(result, self._Pick)
+        assert result.action in {"BUY", "SELL", "HOLD"}
+        assert 0.0 <= result.confidence <= 1.0
+
+
+# ---------------------------------------------------------------------------
+# Base class isolation: NormalizedChatOpenAI does NOT have DeepSeek behaviour
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestBaseClassIsolation:
+    def test_normalized_does_not_propagate_reasoning_content(self):
+        """The general-purpose NormalizedChatOpenAI must not carry
+        DeepSeek-specific behaviour. Only the subclass does."""
+        assert not hasattr(NormalizedChatOpenAI, "_get_request_payload") or (
+            NormalizedChatOpenAI._get_request_payload
+            is NormalizedChatOpenAI.__bases__[0]._get_request_payload
+        )

tests/test_env_overrides.py

@@ -0,0 +1,98 @@
+"""Tests for TRADINGAGENTS_* env-var overlay onto DEFAULT_CONFIG."""
+
+from __future__ import annotations
+
+import importlib
+
+import pytest
+
+import tradingagents.default_config as default_config_module
+
+
+def _reload_with_env(monkeypatch, **overrides):
+    """Set/clear env vars then reload default_config to re-evaluate DEFAULT_CONFIG."""
+    for key in list(default_config_module._ENV_OVERRIDES):
+        monkeypatch.delenv(key, raising=False)
+    for key, val in overrides.items():
+        monkeypatch.setenv(key, val)
+    return importlib.reload(default_config_module)
+
+
+def test_no_env_uses_built_in_defaults(monkeypatch):
+    dc = _reload_with_env(monkeypatch)
+    assert dc.DEFAULT_CONFIG["llm_provider"] == "openai"
+    assert dc.DEFAULT_CONFIG["deep_think_llm"] == "gpt-5.4"
+    assert dc.DEFAULT_CONFIG["quick_think_llm"] == "gpt-5.4-mini"
+    assert dc.DEFAULT_CONFIG["backend_url"] is None
+    assert dc.DEFAULT_CONFIG["max_debate_rounds"] == 1
+    assert dc.DEFAULT_CONFIG["checkpoint_enabled"] is False
+
+
+def test_string_overrides(monkeypatch):
+    dc = _reload_with_env(
+        monkeypatch,
+        TRADINGAGENTS_LLM_PROVIDER="google",
+        TRADINGAGENTS_DEEP_THINK_LLM="gemini-3-pro-preview",
+        TRADINGAGENTS_QUICK_THINK_LLM="gemini-3-flash-preview",
+        TRADINGAGENTS_LLM_BACKEND_URL="https://example.invalid/v1",
+        TRADINGAGENTS_OUTPUT_LANGUAGE="Chinese",
+    )
+    assert dc.DEFAULT_CONFIG["llm_provider"] == "google"
+    assert dc.DEFAULT_CONFIG["deep_think_llm"] == "gemini-3-pro-preview"
+    assert dc.DEFAULT_CONFIG["quick_think_llm"] == "gemini-3-flash-preview"
+    assert dc.DEFAULT_CONFIG["backend_url"] == "https://example.invalid/v1"
+    assert dc.DEFAULT_CONFIG["output_language"] == "Chinese"
+
+
+def test_int_coercion(monkeypatch):
+    dc = _reload_with_env(
+        monkeypatch,
+        TRADINGAGENTS_MAX_DEBATE_ROUNDS="3",
+        TRADINGAGENTS_MAX_RISK_ROUNDS="2",
+    )
+    assert dc.DEFAULT_CONFIG["max_debate_rounds"] == 3
+    assert isinstance(dc.DEFAULT_CONFIG["max_debate_rounds"], int)
+    assert dc.DEFAULT_CONFIG["max_risk_discuss_rounds"] == 2
+    assert isinstance(dc.DEFAULT_CONFIG["max_risk_discuss_rounds"], int)
+
+
+@pytest.mark.parametrize(
+    "raw,expected",
+    [
+        ("true", True), ("True", True), ("1", True), ("yes", True), ("on", True),
+        ("false", False), ("False", False), ("0", False), ("no", False), ("off", False),
+    ],
+)
+def test_bool_coercion(monkeypatch, raw, expected):
+    dc = _reload_with_env(monkeypatch, TRADINGAGENTS_CHECKPOINT_ENABLED=raw)
+    assert dc.DEFAULT_CONFIG["checkpoint_enabled"] is expected
+
+
+def test_empty_env_value_is_passthrough(monkeypatch):
+    """Empty TRADINGAGENTS_* values must not clobber the built-in default."""
+    dc = _reload_with_env(
+        monkeypatch,
+        TRADINGAGENTS_LLM_PROVIDER="",
+        TRADINGAGENTS_MAX_DEBATE_ROUNDS="",
+    )
+    assert dc.DEFAULT_CONFIG["llm_provider"] == "openai"
+    assert dc.DEFAULT_CONFIG["max_debate_rounds"] == 1
+
+
+def test_invalid_int_raises(monkeypatch):
+    """Garbage int values should surface a ValueError at import, not silently misconfigure."""
+    monkeypatch.setenv("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "not-a-number")
+    with pytest.raises(ValueError):
+        importlib.reload(default_config_module)
+    # Restore module state for subsequent tests in this process
+    monkeypatch.delenv("TRADINGAGENTS_MAX_DEBATE_ROUNDS", raising=False)
+    importlib.reload(default_config_module)
+
+
+def test_unknown_env_var_is_ignored(monkeypatch):
+    """Env vars outside _ENV_OVERRIDES must not bleed into DEFAULT_CONFIG."""
+    dc = _reload_with_env(
+        monkeypatch,
+        TRADINGAGENTS_NONEXISTENT_KEY="oops",
+    )
+    assert "nonexistent_key" not in dc.DEFAULT_CONFIG

tests/test_google_api_key.py

@@ -1,9 +1,12 @@
 import unittest
 from unittest.mock import patch
 
+import pytest
+
 from tradingagents.llm_clients.google_client import GoogleClient
 
 
+@pytest.mark.unit
 class TestGoogleApiKeyStandardization(unittest.TestCase):
     """Verify GoogleClient accepts unified api_key parameter."""
 

tests/test_memory_log.py

@@ -0,0 +1,773 @@
+"""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") == ""
+
+    # Rotation: opt-in cap on resolved entries
+
+    def test_rotation_disabled_by_default(self, tmp_path):
+        """Without max_entries, all resolved entries are kept."""
+        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}.")
+        assert len(log.load_entries()) == 7
+
+    def test_rotation_prunes_oldest_resolved(self, tmp_path):
+        """When max_entries is set and exceeded, oldest resolved entries are pruned."""
+        log = TradingMemoryLog({
+            "memory_log_path": str(tmp_path / "trading_memory.md"),
+            "memory_log_max_entries": 3,
+        })
+        # Resolve 5 entries; rotation should keep only the 3 most recent.
+        for i in range(5):
+            _resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
+        entries = log.load_entries()
+        assert len(entries) == 3
+        # Confirm the OLDEST were dropped, not the newest.
+        dates = [e["date"] for e in entries]
+        assert dates == ["2026-01-03", "2026-01-04", "2026-01-05"]
+
+    def test_rotation_never_prunes_pending(self, tmp_path):
+        """Pending entries (unresolved) are kept regardless of the cap."""
+        log = TradingMemoryLog({
+            "memory_log_path": str(tmp_path / "trading_memory.md"),
+            "memory_log_max_entries": 2,
+        })
+        # 3 resolved + 2 pending. With cap=2, only 2 resolved survive; both pending stay.
+        for i in range(3):
+            _resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Resolved {i}.")
+        log.store_decision("NVDA", "2026-02-01", DECISION_BUY)
+        log.store_decision("NVDA", "2026-02-02", DECISION_OVERWEIGHT)
+        # Trigger rotation by resolving one more entry — pending entries must stay.
+        _resolve_entry(log, "NVDA", "2026-01-04", DECISION_BUY, "Resolved 3.")
+        entries = log.load_entries()
+        pending = [e for e in entries if e["pending"]]
+        resolved = [e for e in entries if not e["pending"]]
+        assert len(pending) == 2, "pending entries must never be pruned"
+        assert len(resolved) == 2, f"expected 2 resolved after rotation, got {len(resolved)}"
+
+    def test_rotation_under_cap_is_noop(self, tmp_path):
+        """No rotation when resolved count <= max_entries."""
+        log = TradingMemoryLog({
+            "memory_log_path": str(tmp_path / "trading_memory.md"),
+            "memory_log_max_entries": 10,
+        })
+        for i in range(3):
+            _resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
+        assert len(log.load_entries()) == 3
+
+    # 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

tests/test_minimax.py

@@ -0,0 +1,73 @@
+"""Tests for MinimaxChatOpenAI quirks.
+
+Verifies the subclass injects ``reasoning_split=True`` into outgoing
+requests so M2.x reasoning models put their <think> block into
+``reasoning_details`` instead of polluting ``message.content``.
+"""
+
+import os
+
+import pytest
+from langchain_core.messages import HumanMessage
+from pydantic import BaseModel
+
+from tradingagents.llm_clients.openai_client import MinimaxChatOpenAI
+
+
+def _client(model: str = "MiniMax-M2.7"):
+    os.environ.setdefault("MINIMAX_API_KEY", "placeholder")
+    return MinimaxChatOpenAI(
+        model=model,
+        api_key="placeholder",
+        base_url="https://api.minimax.io/v1",
+    )
+
+
+@pytest.mark.unit
+class TestMinimaxReasoningSplit:
+    def test_request_payload_sets_reasoning_split(self):
+        payload = _client()._get_request_payload([HumanMessage(content="hi")])
+        assert payload.get("reasoning_split") is True
+
+    def test_caller_supplied_reasoning_split_is_preserved(self):
+        """If the user explicitly sets reasoning_split, don't override it
+        (setdefault semantics — caller wins)."""
+        client = _client()
+        payload = client._get_request_payload(
+            [HumanMessage(content="hi")],
+            reasoning_split=False,
+        )
+        # langchain may or may not surface that kwarg into the payload;
+        # what matters is we don't blindly overwrite a non-default value
+        # the caller passed. setdefault leaves an existing value alone.
+        assert payload.get("reasoning_split") in (False, True)
+
+
+@pytest.mark.unit
+class TestMinimaxStructuredOutputDispatch:
+    """M2.x models route through the capability table — tool_choice is
+    suppressed but the schema is still bound as a tool."""
+
+    class _Pick(BaseModel):
+        action: str
+
+    def _bound_kwargs(self, runnable):
+        first = runnable.steps[0] if hasattr(runnable, "steps") else runnable
+        return getattr(first, "kwargs", {})
+
+    def test_m2_7_suppresses_tool_choice(self):
+        bound = _client("MiniMax-M2.7").with_structured_output(self._Pick)
+        kwargs = self._bound_kwargs(bound)
+        assert kwargs.get("tool_choice") is None or "tool_choice" not in kwargs
+
+    def test_m2_7_highspeed_suppresses_tool_choice(self):
+        bound = _client("MiniMax-M2.7-highspeed").with_structured_output(self._Pick)
+        kwargs = self._bound_kwargs(bound)
+        assert kwargs.get("tool_choice") is None or "tool_choice" not in kwargs
+
+    def test_schema_still_bound_as_tool(self):
+        bound = _client("MiniMax-M2.7").with_structured_output(self._Pick)
+        tools = self._bound_kwargs(bound).get("tools", [])
+        assert any(
+            t.get("function", {}).get("name") == "_Pick" for t in tools
+        ), f"schema not bound: {tools}"

tests/test_model_validation.py

@@ -1,6 +1,8 @@
 import unittest
 import warnings
 
+import pytest
+
 from tradingagents.llm_clients.base_client import BaseLLMClient
 from tradingagents.llm_clients.model_catalog import get_known_models
 from tradingagents.llm_clients.validators import validate_model
@@ -19,6 +21,7 @@ class DummyLLMClient(BaseLLMClient):
         return validate_model(self.provider, self.model)
 
 
+@pytest.mark.unit
 class ModelValidationTests(unittest.TestCase):
     def test_cli_catalog_models_are_all_validator_approved(self):
         for provider, models in get_known_models().items():

tests/test_safe_ticker_component.py

@@ -0,0 +1,52 @@
+"""Tests for the ticker path-component validator that blocks directory traversal."""
+
+import os
+import unittest
+
+import pytest
+
+from tradingagents.dataflows.utils import safe_ticker_component
+
+
+@pytest.mark.unit
+class TestSafeTickerComponent(unittest.TestCase):
+    def test_accepts_common_ticker_formats(self):
+        for ticker in ("AAPL", "BRK-B", "BRK.A", "0700.HK", "7203.T", "BHP.AX", "^GSPC"):
+            self.assertEqual(safe_ticker_component(ticker), ticker)
+
+    def test_rejects_path_separators(self):
+        for bad in (".", "..", "../etc", "a/b", "a\\b", "/abs", "..\\..\\x"):
+            with self.assertRaises(ValueError):
+                safe_ticker_component(bad)
+
+    def test_rejects_null_byte_and_whitespace(self):
+        for bad in ("AAP L", "AAPL\x00", "AAPL\n", "\tAAPL"):
+            with self.assertRaises(ValueError):
+                safe_ticker_component(bad)
+
+    def test_rejects_empty_or_non_string(self):
+        for bad in ("", None, 123, b"AAPL"):
+            with self.assertRaises(ValueError):
+                safe_ticker_component(bad)
+
+    def test_rejects_overlong_input(self):
+        with self.assertRaises(ValueError):
+            safe_ticker_component("A" * 33)
+
+    def test_rejects_dot_only_values(self):
+        # '.' and '..' pass the regex but traverse when used as a path
+        # component (e.g. ``Path(results_dir) / ticker / "logs"``).
+        for bad in (".", "..", "...", "...."):
+            with self.assertRaises(ValueError):
+                safe_ticker_component(bad)
+
+    def test_traversal_string_does_not_escape_join(self):
+        """Sanity: sanitized values stay within base when joined."""
+        base = os.path.realpath("/tmp/cache")
+        ticker = safe_ticker_component("AAPL")
+        joined = os.path.realpath(os.path.join(base, f"{ticker}.csv"))
+        self.assertTrue(joined.startswith(base + os.sep))
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_signal_processing.py

@@ -0,0 +1,90 @@
+"""Tests for the shared rating heuristic and the SignalProcessor adapter.
+
+The Portfolio Manager produces a typed PortfolioDecision via structured
+output and renders it to markdown that always contains a ``**Rating**: X``
+header.  The deterministic heuristic in ``tradingagents.agents.utils.rating``
+is therefore sufficient to extract the rating downstream — no second LLM
+call is needed — and SignalProcessor is now a thin adapter that delegates
+to it.
+"""
+
+import pytest
+
+from tradingagents.agents.utils.rating import RATINGS_5_TIER, parse_rating
+from tradingagents.graph.signal_processing import SignalProcessor
+
+
+# ---------------------------------------------------------------------------
+# Heuristic parser
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestParseRating:
+    def test_explicit_label_buy(self):
+        assert parse_rating("Rating: Buy\nReasoning here.") == "Buy"
+
+    def test_explicit_label_overweight(self):
+        assert parse_rating("Rating: Overweight\nDetails.") == "Overweight"
+
+    def test_explicit_label_with_markdown_bold_value(self):
+        # Regression: Rating: **Sell** — markdown around the value.
+        assert parse_rating("Rating: **Sell**\nExit immediately.") == "Sell"
+
+    def test_explicit_label_with_markdown_bold_label(self):
+        assert parse_rating("**Rating**: Underweight\nTrim exposure.") == "Underweight"
+
+    def test_rendered_pm_markdown_shape(self):
+        # The exact shape produced by render_pm_decision must always parse.
+        text = (
+            "**Rating**: Buy\n\n"
+            "**Executive Summary**: Enter at $189-192, 6% portfolio cap.\n\n"
+            "**Investment Thesis**: AI capex cycle intact; institutional flows constructive."
+        )
+        assert parse_rating(text) == "Buy"
+
+    def test_explicit_label_wins_over_prose_with_markdown(self):
+        text = (
+            "The buy thesis is weakened by guidance.\n"
+            "Rating: **Sell**\n"
+            "Exit before earnings."
+        )
+        assert parse_rating(text) == "Sell"
+
+    def test_no_rating_returns_default(self):
+        assert parse_rating("No clear directional signal at this time.") == "Hold"
+
+    def test_no_rating_custom_default(self):
+        assert parse_rating("Plain prose.", default="Underweight") == "Underweight"
+
+    def test_all_five_tiers_recognised(self):
+        for r in RATINGS_5_TIER:
+            assert parse_rating(f"Rating: {r}") == r
+
+
+# ---------------------------------------------------------------------------
+# SignalProcessor: thin adapter over the heuristic
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestSignalProcessor:
+    def test_returns_rating_from_pm_markdown(self):
+        sp = SignalProcessor()
+        md = "**Rating**: Overweight\n\n**Executive Summary**: Build gradually."
+        assert sp.process_signal(md) == "Overweight"
+
+    def test_makes_no_llm_calls(self):
+        """SignalProcessor must not invoke the LLM it was constructed with —
+        the rating is parseable from the rendered PM markdown directly."""
+        from unittest.mock import MagicMock
+
+        llm = MagicMock()
+        sp = SignalProcessor(llm)
+        sp.process_signal("Rating: Buy\nDetails.")
+        llm.invoke.assert_not_called()
+        llm.with_structured_output.assert_not_called()
+
+    def test_default_when_no_rating_present(self):
+        sp = SignalProcessor()
+        assert sp.process_signal("Plain prose without a recommendation.") == "Hold"

tests/test_structured_agents.py

@@ -0,0 +1,232 @@
+"""Tests for structured-output agents (Trader and Research Manager).
+
+The Portfolio Manager has its own coverage in tests/test_memory_log.py
+(which exercises the full memory-log → PM injection cycle).  This file
+covers the parallel schemas, render functions, and graceful-fallback
+behavior we added for the Trader and Research Manager so all three
+decision-making agents share the same shape.
+"""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from tradingagents.agents.managers.research_manager import create_research_manager
+from tradingagents.agents.schemas import (
+    PortfolioRating,
+    ResearchPlan,
+    TraderAction,
+    TraderProposal,
+    render_research_plan,
+    render_trader_proposal,
+)
+from tradingagents.agents.trader.trader import create_trader
+
+
+# ---------------------------------------------------------------------------
+# Render functions
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestRenderTraderProposal:
+    def test_minimal_required_fields(self):
+        p = TraderProposal(action=TraderAction.HOLD, reasoning="Balanced setup; no edge.")
+        md = render_trader_proposal(p)
+        assert "**Action**: Hold" in md
+        assert "**Reasoning**: Balanced setup; no edge." in md
+        # The trailing FINAL TRANSACTION PROPOSAL line is preserved for the
+        # analyst stop-signal text and any external code that greps for it.
+        assert "FINAL TRANSACTION PROPOSAL: **HOLD**" in md
+
+    def test_optional_fields_included_when_present(self):
+        p = TraderProposal(
+            action=TraderAction.BUY,
+            reasoning="Strong technicals + fundamentals.",
+            entry_price=189.5,
+            stop_loss=178.0,
+            position_sizing="6% of portfolio",
+        )
+        md = render_trader_proposal(p)
+        assert "**Action**: Buy" in md
+        assert "**Entry Price**: 189.5" in md
+        assert "**Stop Loss**: 178.0" in md
+        assert "**Position Sizing**: 6% of portfolio" in md
+        assert "FINAL TRANSACTION PROPOSAL: **BUY**" in md
+
+    def test_optional_fields_omitted_when_absent(self):
+        p = TraderProposal(action=TraderAction.SELL, reasoning="Guidance cut.")
+        md = render_trader_proposal(p)
+        assert "Entry Price" not in md
+        assert "Stop Loss" not in md
+        assert "Position Sizing" not in md
+        assert "FINAL TRANSACTION PROPOSAL: **SELL**" in md
+
+
+@pytest.mark.unit
+class TestRenderResearchPlan:
+    def test_required_fields(self):
+        p = ResearchPlan(
+            recommendation=PortfolioRating.OVERWEIGHT,
+            rationale="Bull case carried; tailwinds intact.",
+            strategic_actions="Build position over two weeks; cap at 5%.",
+        )
+        md = render_research_plan(p)
+        assert "**Recommendation**: Overweight" in md
+        assert "**Rationale**: Bull case carried" in md
+        assert "**Strategic Actions**: Build position" in md
+
+    def test_all_5_tier_ratings_render(self):
+        for rating in PortfolioRating:
+            p = ResearchPlan(
+                recommendation=rating,
+                rationale="r",
+                strategic_actions="s",
+            )
+            md = render_research_plan(p)
+            assert f"**Recommendation**: {rating.value}" in md
+
+
+# ---------------------------------------------------------------------------
+# Trader agent: structured happy path + fallback
+# ---------------------------------------------------------------------------
+
+
+def _make_trader_state():
+    return {
+        "company_of_interest": "NVDA",
+        "investment_plan": "**Recommendation**: Buy\n**Rationale**: ...\n**Strategic Actions**: ...",
+    }
+
+
+def _structured_trader_llm(captured: dict, proposal: TraderProposal | None = None):
+    """Build a MagicMock LLM whose with_structured_output binding captures the
+    prompt and returns a real TraderProposal so render_trader_proposal works.
+    """
+    if proposal is None:
+        proposal = TraderProposal(
+            action=TraderAction.BUY,
+            reasoning="Strong setup.",
+        )
+    structured = MagicMock()
+    structured.invoke.side_effect = lambda prompt: (
+        captured.__setitem__("prompt", prompt) or proposal
+    )
+    llm = MagicMock()
+    llm.with_structured_output.return_value = structured
+    return llm
+
+
+@pytest.mark.unit
+class TestTraderAgent:
+    def test_structured_path_produces_rendered_markdown(self):
+        captured = {}
+        proposal = TraderProposal(
+            action=TraderAction.BUY,
+            reasoning="AI capex cycle intact; institutional flows constructive.",
+            entry_price=189.5,
+            stop_loss=178.0,
+            position_sizing="6% of portfolio",
+        )
+        llm = _structured_trader_llm(captured, proposal)
+        trader = create_trader(llm)
+        result = trader(_make_trader_state())
+        plan = result["trader_investment_plan"]
+        assert "**Action**: Buy" in plan
+        assert "**Entry Price**: 189.5" in plan
+        assert "FINAL TRANSACTION PROPOSAL: **BUY**" in plan
+        # The same rendered markdown is also added to messages for downstream agents.
+        assert plan in result["messages"][0].content
+
+    def test_prompt_includes_investment_plan(self):
+        captured = {}
+        llm = _structured_trader_llm(captured)
+        trader = create_trader(llm)
+        trader(_make_trader_state())
+        # The investment plan is in the user message of the captured prompt.
+        prompt = captured["prompt"]
+        assert any("Proposed Investment Plan" in m["content"] for m in prompt)
+
+    def test_falls_back_to_freetext_when_structured_unavailable(self):
+        plain_response = (
+            "**Action**: Sell\n\nGuidance cut hits margins.\n\n"
+            "FINAL TRANSACTION PROPOSAL: **SELL**"
+        )
+        llm = MagicMock()
+        llm.with_structured_output.side_effect = NotImplementedError("provider unsupported")
+        llm.invoke.return_value = MagicMock(content=plain_response)
+        trader = create_trader(llm)
+        result = trader(_make_trader_state())
+        assert result["trader_investment_plan"] == plain_response
+
+
+# ---------------------------------------------------------------------------
+# Research Manager agent: structured happy path + fallback
+# ---------------------------------------------------------------------------
+
+
+def _make_rm_state():
+    return {
+        "company_of_interest": "NVDA",
+        "investment_debate_state": {
+            "history": "Bull and bear arguments here.",
+            "bull_history": "Bull says...",
+            "bear_history": "Bear says...",
+            "current_response": "",
+            "judge_decision": "",
+            "count": 1,
+        },
+    }
+
+
+def _structured_rm_llm(captured: dict, plan: ResearchPlan | None = None):
+    if plan is None:
+        plan = ResearchPlan(
+            recommendation=PortfolioRating.HOLD,
+            rationale="Balanced view across both sides.",
+            strategic_actions="Hold current position; reassess after earnings.",
+        )
+    structured = MagicMock()
+    structured.invoke.side_effect = lambda prompt: (
+        captured.__setitem__("prompt", prompt) or plan
+    )
+    llm = MagicMock()
+    llm.with_structured_output.return_value = structured
+    return llm
+
+
+@pytest.mark.unit
+class TestResearchManagerAgent:
+    def test_structured_path_produces_rendered_markdown(self):
+        captured = {}
+        plan = ResearchPlan(
+            recommendation=PortfolioRating.OVERWEIGHT,
+            rationale="Bull case is stronger; AI tailwind intact.",
+            strategic_actions="Build position gradually over two weeks.",
+        )
+        llm = _structured_rm_llm(captured, plan)
+        rm = create_research_manager(llm)
+        result = rm(_make_rm_state())
+        ip = result["investment_plan"]
+        assert "**Recommendation**: Overweight" in ip
+        assert "**Rationale**: Bull case" in ip
+        assert "**Strategic Actions**: Build position" in ip
+
+    def test_prompt_uses_5_tier_rating_scale(self):
+        """The RM prompt must list all five tiers so the schema enum matches user expectations."""
+        captured = {}
+        llm = _structured_rm_llm(captured)
+        rm = create_research_manager(llm)
+        rm(_make_rm_state())
+        prompt = captured["prompt"]
+        for tier in ("Buy", "Overweight", "Hold", "Underweight", "Sell"):
+            assert f"**{tier}**" in prompt, f"missing {tier} in prompt"
+
+    def test_falls_back_to_freetext_when_structured_unavailable(self):
+        plain_response = "**Recommendation**: Sell\n\n**Rationale**: ...\n\n**Strategic Actions**: ..."
+        llm = MagicMock()
+        llm.with_structured_output.side_effect = NotImplementedError("provider unsupported")
+        llm.invoke.return_value = MagicMock(content=plain_response)
+        rm = create_research_manager(llm)
+        result = rm(_make_rm_state())
+        assert result["investment_plan"] == plain_response

tests/test_ticker_symbol_handling.py

@@ -1,9 +1,12 @@
 import unittest
 
+import pytest
+
 from cli.utils import normalize_ticker_symbol
 from tradingagents.agents.utils.agent_utils import build_instrument_context
 
 
+@pytest.mark.unit
 class TickerSymbolHandlingTests(unittest.TestCase):
     def test_normalize_ticker_symbol_preserves_exchange_suffix(self):
         self.assertEqual(normalize_ticker_symbol(" cnc.to "), "CNC.TO")