chore: remove obsolete chat model wrapper

The ToolCallArgsParsingWrapper was replaced by the model factory
approach which uses native ChatDeepSeek for DeepSeek models.

Removed:
- agent/chat_model_wrapper.py (no longer used)
- tests/unit/test_chat_model_wrapper.py (tests for removed functionality)

The model factory provides a cleaner solution by using provider-specific
implementations rather than wrapping ChatOpenAI.

agent/chat_model_wrapper.py

@@ -1,51 +0,0 @@
-"""
-Chat model wrapper - Passthrough wrapper for ChatOpenAI models.
-
-Originally created to fix DeepSeek tool_calls arg parsing issues, but investigation
-revealed DeepSeek already returns the correct format (arguments as JSON strings).
-
-This wrapper is now a simple passthrough that proxies all calls to the underlying model.
-Kept for backward compatibility and potential future use.
-"""
-
-from typing import Any
-
-
-class ToolCallArgsParsingWrapper:
-    """
-    Passthrough wrapper around ChatOpenAI models.
-
-    After systematic debugging, determined that DeepSeek returns tool_calls.arguments
-    as JSON strings (correct format), so no parsing/conversion is needed.
-
-    This wrapper simply proxies all calls to the wrapped model.
-    """
-
-    def __init__(self, model: Any, **kwargs):
-        """
-        Initialize wrapper around a chat model.
-
-        Args:
-            model: The chat model to wrap
-            **kwargs: Additional parameters (ignored, for compatibility)
-        """
-        self.wrapped_model = model
-
-    @property
-    def _llm_type(self) -> str:
-        """Return identifier for this LLM type"""
-        if hasattr(self.wrapped_model, '_llm_type'):
-            return f"wrapped-{self.wrapped_model._llm_type}"
-        return "wrapped-chat-model"
-
-    def __getattr__(self, name: str):
-        """Proxy all attributes/methods to the wrapped model"""
-        return getattr(self.wrapped_model, name)
-
-    def bind_tools(self, tools: Any, **kwargs):
-        """Bind tools to the wrapped model"""
-        return self.wrapped_model.bind_tools(tools, **kwargs)
-
-    def bind(self, **kwargs):
-        """Bind settings to the wrapped model"""
-        return self.wrapped_model.bind(**kwargs)

docs/plans/2025-11-05-fix-trading-flag-and-deepseek-validation.md

@@ -0,0 +1,891 @@
+# Fix IF_TRADE Flag and DeepSeek Tool Calls Validation Implementation Plan
+
+> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
+
+**Goal:** Fix two bugs: (1) "No trading" message always displayed despite trading activity, and (2) sporadic Pydantic validation errors for DeepSeek tool_calls arguments.
+
+**Architecture:**
+- Issue #1: Change IF_TRADE initialization from False to True in runtime config manager
+- Issue #2: Replace ChatOpenAI with native ChatDeepSeek for DeepSeek models to eliminate OpenAI compatibility layer issues
+
+**Tech Stack:** Python 3.10+, LangChain 1.0.2, langchain-deepseek, FastAPI, SQLite
+
+---
+
+## Task 1: Fix IF_TRADE Initialization Bug
+
+**Files:**
+- Modify: `api/runtime_manager.py:80-86`
+- Test: `tests/unit/test_runtime_manager.py`
+- Verify: `agent/base_agent/base_agent.py:745-752`
+
+**Root Cause:**
+`IF_TRADE` is initialized to `False` but never updated when trades execute. The design documents show it should initialize to `True` (trades are expected by default).
+
+**Step 1: Write failing test for IF_TRADE initialization**
+
+Add to `tests/unit/test_runtime_manager.py` after existing tests:
+
+```python
+def test_create_runtime_config_if_trade_defaults_true(self):
+    """Test that IF_TRADE initializes to True (trades expected by default)"""
+    manager = RuntimeConfigManager()
+
+    config_path = manager.create_runtime_config(
+        date="2025-01-16",
+        model_sig="test-model",
+        job_id="test-job-123",
+        trading_day_id=1
+    )
+
+    try:
+        # Read the config file
+        with open(config_path, 'r') as f:
+            config = json.load(f)
+
+        # Verify IF_TRADE is True by default
+        assert config["IF_TRADE"] is True, "IF_TRADE should initialize to True"
+    finally:
+        # Cleanup
+        if os.path.exists(config_path):
+            os.remove(config_path)
+```
+
+**Step 2: Run test to verify it fails**
+
+Run: `./venv/bin/python -m pytest tests/unit/test_runtime_manager.py::TestRuntimeConfigManager::test_create_runtime_config_if_trade_defaults_true -v`
+
+Expected: FAIL with assertion error showing `IF_TRADE` is `False`
+
+**Step 3: Fix IF_TRADE initialization**
+
+In `api/runtime_manager.py`, change line 83:
+
+```python
+# BEFORE (line 80-86):
+initial_config = {
+    "TODAY_DATE": date,
+    "SIGNATURE": model_sig,
+    "IF_TRADE": False,  # BUG: Should be True
+    "JOB_ID": job_id,
+    "TRADING_DAY_ID": trading_day_id
+}
+
+# AFTER:
+initial_config = {
+    "TODAY_DATE": date,
+    "SIGNATURE": model_sig,
+    "IF_TRADE": True,  # FIX: Trades are expected by default
+    "JOB_ID": job_id,
+    "TRADING_DAY_ID": trading_day_id
+}
+```
+
+**Step 4: Run test to verify it passes**
+
+Run: `./venv/bin/python -m pytest tests/unit/test_runtime_manager.py::TestRuntimeConfigManager::test_create_runtime_config_if_trade_defaults_true -v`
+
+Expected: PASS
+
+**Step 5: Update existing test expectations**
+
+The existing test `test_create_runtime_config_creates_file` at line 66 expects `IF_TRADE` to be `False`. Update it:
+
+```python
+# In test_create_runtime_config_creates_file, change line 66:
+# BEFORE:
+assert config["IF_TRADE"] is False
+
+# AFTER:
+assert config["IF_TRADE"] is True
+```
+
+**Step 6: Run all runtime_manager tests**
+
+Run: `./venv/bin/python -m pytest tests/unit/test_runtime_manager.py -v`
+
+Expected: All tests PASS
+
+**Step 7: Verify integration test expectations**
+
+Check `tests/integration/test_agent_pnl_integration.py` line 63:
+
+```python
+# Current mock (line 62-66):
+mock_get_config.side_effect = lambda key: {
+    "IF_TRADE": False,  # This may need updating depending on test scenario
+    "JOB_ID": "test-job",
+    "TODAY_DATE": "2025-01-15",
+    "SIGNATURE": "test-model"
+}.get(key)
+```
+
+This test mocks a no-trade scenario, so `False` is correct here. No change needed.
+
+**Step 8: Commit IF_TRADE fix**
+
+```bash
+git add api/runtime_manager.py tests/unit/test_runtime_manager.py
+git commit -m "fix: initialize IF_TRADE to True (trades expected by default)
+
+Root cause: IF_TRADE was initialized to False and never updated when
+trades executed, causing 'No trading' message to always display.
+
+Design documents (2025-02-11-complete-schema-migration) specify
+IF_TRADE should start as True, with trades setting it to False only
+after completion.
+
+Fixes sporadic issue where all trading sessions reported 'No trading'
+despite successful buy/sell actions."
+```
+
+---
+
+## Task 2: Add langchain-deepseek Dependency
+
+**Files:**
+- Modify: `requirements.txt`
+- Verify: `venv/bin/pip list`
+
+**Step 1: Add langchain-deepseek to requirements.txt**
+
+Add after line 3 in `requirements.txt`:
+
+```txt
+langchain==1.0.2
+langchain-openai==1.0.1
+langchain-mcp-adapters>=0.1.0
+langchain-deepseek>=0.1.20
+```
+
+**Step 2: Install new dependency**
+
+Run: `./venv/bin/pip install -r requirements.txt`
+
+Expected: Successfully installs `langchain-deepseek` and its dependencies
+
+**Step 3: Verify installation**
+
+Run: `./venv/bin/pip show langchain-deepseek`
+
+Expected: Shows package info with version >= 0.1.20
+
+**Step 4: Commit dependency addition**
+
+```bash
+git add requirements.txt
+git commit -m "deps: add langchain-deepseek for native DeepSeek support
+
+Adds official LangChain DeepSeek integration to replace ChatOpenAI
+wrapper approach for DeepSeek models. Native integration provides:
+- Better tool_calls argument parsing
+- DeepSeek-specific error handling
+- No OpenAI compatibility layer issues
+
+Version 0.1.20+ includes tool calling support for deepseek-chat."
+```
+
+---
+
+## Task 3: Implement Model Provider Factory
+
+**Files:**
+- Create: `agent/model_factory.py`
+- Test: `tests/unit/test_model_factory.py`
+
+**Rationale:**
+Currently `base_agent.py` hardcodes model creation logic. Extract to factory pattern to support multiple providers (OpenAI, DeepSeek, Anthropic, etc.) with provider-specific handling.
+
+**Step 1: Write failing test for model factory**
+
+Create `tests/unit/test_model_factory.py`:
+
+```python
+"""Unit tests for model factory - provider-specific model creation"""
+
+import pytest
+from unittest.mock import Mock, patch
+from agent.model_factory import create_model
+
+
+class TestModelFactory:
+    """Tests for create_model factory function"""
+
+    @patch('agent.model_factory.ChatDeepSeek')
+    def test_create_model_deepseek(self, mock_deepseek_class):
+        """Test that DeepSeek models use ChatDeepSeek"""
+        mock_model = Mock()
+        mock_deepseek_class.return_value = mock_model
+
+        result = create_model(
+            basemodel="deepseek/deepseek-chat",
+            api_key="test-key",
+            base_url="https://api.deepseek.com",
+            temperature=0.7,
+            timeout=30
+        )
+
+        # Verify ChatDeepSeek was called with correct params
+        mock_deepseek_class.assert_called_once_with(
+            model="deepseek-chat",  # Extracted from "deepseek/deepseek-chat"
+            api_key="test-key",
+            base_url="https://api.deepseek.com",
+            temperature=0.7,
+            timeout=30
+        )
+        assert result == mock_model
+
+    @patch('agent.model_factory.ChatOpenAI')
+    def test_create_model_openai(self, mock_openai_class):
+        """Test that OpenAI models use ChatOpenAI"""
+        mock_model = Mock()
+        mock_openai_class.return_value = mock_model
+
+        result = create_model(
+            basemodel="openai/gpt-4",
+            api_key="test-key",
+            base_url="https://api.openai.com/v1",
+            temperature=0.7,
+            timeout=30
+        )
+
+        # Verify ChatOpenAI was called with correct params
+        mock_openai_class.assert_called_once_with(
+            model="openai/gpt-4",
+            api_key="test-key",
+            base_url="https://api.openai.com/v1",
+            temperature=0.7,
+            timeout=30
+        )
+        assert result == mock_model
+
+    @patch('agent.model_factory.ChatOpenAI')
+    def test_create_model_anthropic(self, mock_openai_class):
+        """Test that Anthropic models use ChatOpenAI (via compatibility)"""
+        mock_model = Mock()
+        mock_openai_class.return_value = mock_model
+
+        result = create_model(
+            basemodel="anthropic/claude-sonnet-4.5",
+            api_key="test-key",
+            base_url="https://api.anthropic.com/v1",
+            temperature=0.7,
+            timeout=30
+        )
+
+        # Verify ChatOpenAI was used (Anthropic via OpenAI-compatible endpoint)
+        mock_openai_class.assert_called_once()
+        assert result == mock_model
+
+    @patch('agent.model_factory.ChatOpenAI')
+    def test_create_model_generic_provider(self, mock_openai_class):
+        """Test that unknown providers default to ChatOpenAI"""
+        mock_model = Mock()
+        mock_openai_class.return_value = mock_model
+
+        result = create_model(
+            basemodel="custom/custom-model",
+            api_key="test-key",
+            base_url="https://api.custom.com",
+            temperature=0.7,
+            timeout=30
+        )
+
+        # Should fall back to ChatOpenAI for unknown providers
+        mock_openai_class.assert_called_once()
+        assert result == mock_model
+
+    def test_create_model_deepseek_extracts_model_name(self):
+        """Test that DeepSeek model name is extracted correctly"""
+        with patch('agent.model_factory.ChatDeepSeek') as mock_class:
+            create_model(
+                basemodel="deepseek/deepseek-chat-v3.1",
+                api_key="key",
+                base_url="url",
+                temperature=0,
+                timeout=30
+            )
+
+            # Check that model param is just "deepseek-chat-v3.1"
+            call_kwargs = mock_class.call_args[1]
+            assert call_kwargs['model'] == "deepseek-chat-v3.1"
+```
+
+**Step 2: Run test to verify it fails**
+
+Run: `./venv/bin/python -m pytest tests/unit/test_model_factory.py -v`
+
+Expected: FAIL with "ModuleNotFoundError: No module named 'agent.model_factory'"
+
+**Step 3: Implement model factory**
+
+Create `agent/model_factory.py`:
+
+```python
+"""
+Model factory for creating provider-specific chat models.
+
+Supports multiple AI providers with native integrations where available:
+- DeepSeek: Uses ChatDeepSeek for native tool calling support
+- OpenAI: Uses ChatOpenAI
+- Others: Fall back to ChatOpenAI (OpenAI-compatible endpoints)
+"""
+
+from typing import Any
+from langchain_openai import ChatOpenAI
+from langchain_deepseek import ChatDeepSeek
+
+
+def create_model(
+    basemodel: str,
+    api_key: str,
+    base_url: str,
+    temperature: float,
+    timeout: int
+) -> Any:
+    """
+    Create appropriate chat model based on provider.
+
+    Args:
+        basemodel: Model identifier (e.g., "deepseek/deepseek-chat", "openai/gpt-4")
+        api_key: API key for the provider
+        base_url: Base URL for API endpoint
+        temperature: Sampling temperature (0-1)
+        timeout: Request timeout in seconds
+
+    Returns:
+        Provider-specific chat model instance
+
+    Examples:
+        >>> model = create_model("deepseek/deepseek-chat", "key", "url", 0.7, 30)
+        >>> isinstance(model, ChatDeepSeek)
+        True
+
+        >>> model = create_model("openai/gpt-4", "key", "url", 0.7, 30)
+        >>> isinstance(model, ChatOpenAI)
+        True
+    """
+    # Extract provider from basemodel (format: "provider/model-name")
+    provider = basemodel.split("/")[0].lower() if "/" in basemodel else "unknown"
+
+    if provider == "deepseek":
+        # Use native ChatDeepSeek for DeepSeek models
+        # Extract model name without provider prefix
+        model_name = basemodel.split("/", 1)[1] if "/" in basemodel else basemodel
+
+        return ChatDeepSeek(
+            model=model_name,
+            api_key=api_key,
+            base_url=base_url,
+            temperature=temperature,
+            timeout=timeout
+        )
+    else:
+        # Use ChatOpenAI for OpenAI and OpenAI-compatible endpoints
+        # (Anthropic, Google, Qwen, etc. via compatibility layer)
+        return ChatOpenAI(
+            model=basemodel,
+            api_key=api_key,
+            base_url=base_url,
+            temperature=temperature,
+            timeout=timeout
+        )
+```
+
+**Step 4: Run tests to verify they pass**
+
+Run: `./venv/bin/python -m pytest tests/unit/test_model_factory.py -v`
+
+Expected: All tests PASS
+
+**Step 5: Commit model factory**
+
+```bash
+git add agent/model_factory.py tests/unit/test_model_factory.py
+git commit -m "feat: add model factory for provider-specific chat models
+
+Implements factory pattern to create appropriate chat model based on
+provider prefix in basemodel string.
+
+Supported providers:
+- deepseek/*: Uses ChatDeepSeek (native tool calling)
+- openai/*: Uses ChatOpenAI
+- others: Fall back to ChatOpenAI (OpenAI-compatible)
+
+This enables native DeepSeek integration while maintaining backward
+compatibility with existing OpenAI-compatible providers."
+```
+
+---
+
+## Task 4: Integrate Model Factory into BaseAgent
+
+**Files:**
+- Modify: `agent/base_agent/base_agent.py:146-220`
+- Test: `tests/unit/test_base_agent.py` (if exists) or manual verification
+
+**Step 1: Import model factory in base_agent.py**
+
+Add after line 33 in `agent/base_agent/base_agent.py`:
+
+```python
+from agent.reasoning_summarizer import ReasoningSummarizer
+from agent.model_factory import create_model  # ADD THIS
+```
+
+**Step 2: Replace model creation logic**
+
+Replace lines 208-220 in `agent/base_agent/base_agent.py`:
+
+```python
+# BEFORE (lines 208-220):
+if is_dev_mode():
+    from agent.mock_provider import MockChatModel
+    self.model = MockChatModel(date="2025-01-01")
+    print(f"🤖 Using MockChatModel (DEV mode)")
+else:
+    self.model = ChatOpenAI(
+        model=self.basemodel,
+        base_url=self.openai_base_url,
+        api_key=self.openai_api_key,
+        temperature=0.7,
+        timeout=30
+    )
+    print(f"🤖 Using {self.basemodel} (PROD mode)")
+
+# AFTER:
+if is_dev_mode():
+    from agent.mock_provider import MockChatModel
+    self.model = MockChatModel(date="2025-01-01")
+    print(f"🤖 Using MockChatModel (DEV mode)")
+else:
+    # Use model factory for provider-specific implementations
+    self.model = create_model(
+        basemodel=self.basemodel,
+        api_key=self.openai_api_key,
+        base_url=self.openai_base_url,
+        temperature=0.7,
+        timeout=30
+    )
+
+    # Determine model type for logging
+    model_class = self.model.__class__.__name__
+    print(f"🤖 Using {self.basemodel} via {model_class} (PROD mode)")
+```
+
+**Step 3: Remove ChatOpenAI import if no longer used**
+
+Check if `ChatOpenAI` is imported but no longer used in `base_agent.py`:
+
+```python
+# If line ~11 has:
+from langchain_openai import ChatOpenAI
+
+# And it's only used in the section we just replaced, remove it
+# (Keep if used elsewhere in file)
+```
+
+**Step 4: Manual verification test**
+
+Since this changes core agent initialization, test with actual execution:
+
+Run: `DEPLOYMENT_MODE=DEV python main.py configs/default_config.json`
+
+Expected:
+- Logs show "Using deepseek-chat-v3.1 via ChatDeepSeek (PROD mode)" for DeepSeek
+- Logs show "Using openai/gpt-5 via ChatOpenAI (PROD mode)" for OpenAI
+- No import errors or model creation failures
+
+**Step 5: Run existing agent tests**
+
+Run: `./venv/bin/python -m pytest tests/unit/ -k agent -v`
+
+Expected: All agent-related tests still PASS (factory is transparent to existing behavior)
+
+**Step 6: Commit model factory integration**
+
+```bash
+git add agent/base_agent/base_agent.py
+git commit -m "refactor: use model factory in BaseAgent
+
+Replaces direct ChatOpenAI instantiation with create_model() factory.
+
+Benefits:
+- DeepSeek models now use native ChatDeepSeek
+- Other models continue using ChatOpenAI
+- Provider-specific optimizations in one place
+- Easier to add new providers
+
+Logging now shows both model name and provider class for debugging."
+```
+
+---
+
+## Task 5: Add Integration Test for DeepSeek Tool Calls
+
+**Files:**
+- Create: `tests/integration/test_deepseek_tool_calls.py`
+- Reference: `agent_tools/tool_math.py` (math tool for testing)
+
+**Rationale:**
+Verify that DeepSeek's tool_calls arguments are properly parsed to dicts without Pydantic validation errors.
+
+**Step 1: Write integration test**
+
+Create `tests/integration/test_deepseek_tool_calls.py`:
+
+```python
+"""
+Integration test for DeepSeek tool calls argument parsing.
+
+Tests that ChatDeepSeek properly converts tool_calls.arguments (JSON string)
+to tool_calls.args (dict) without Pydantic validation errors.
+"""
+
+import pytest
+import os
+from unittest.mock import patch, AsyncMock
+from langchain_core.messages import AIMessage
+from agent.model_factory import create_model
+
+
+@pytest.mark.integration
+class TestDeepSeekToolCalls:
+    """Integration tests for DeepSeek tool calling"""
+
+    def test_create_model_returns_chat_deepseek_for_deepseek_models(self):
+        """Verify that DeepSeek models use ChatDeepSeek class"""
+        # Skip if no DeepSeek API key available
+        if not os.getenv("OPENAI_API_KEY"):
+            pytest.skip("OPENAI_API_KEY not available")
+
+        model = create_model(
+            basemodel="deepseek/deepseek-chat",
+            api_key=os.getenv("OPENAI_API_KEY"),
+            base_url=os.getenv("OPENAI_API_BASE"),
+            temperature=0,
+            timeout=30
+        )
+
+        # Verify it's a ChatDeepSeek instance
+        assert model.__class__.__name__ == "ChatDeepSeek"
+
+    @pytest.mark.asyncio
+    async def test_deepseek_tool_calls_args_are_dicts(self):
+        """Test that DeepSeek tool_calls.args are dicts, not strings"""
+        # Skip if no API key
+        if not os.getenv("OPENAI_API_KEY"):
+            pytest.skip("OPENAI_API_KEY not available")
+
+        # Create DeepSeek model
+        model = create_model(
+            basemodel="deepseek/deepseek-chat",
+            api_key=os.getenv("OPENAI_API_KEY"),
+            base_url=os.getenv("OPENAI_API_BASE"),
+            temperature=0,
+            timeout=30
+        )
+
+        # Bind a simple math tool
+        from langchain_core.tools import tool
+
+        @tool
+        def add(a: float, b: float) -> float:
+            """Add two numbers"""
+            return a + b
+
+        model_with_tools = model.bind_tools([add])
+
+        # Invoke with a query that should trigger tool call
+        result = await model_with_tools.ainvoke(
+            "What is 5 plus 3?"
+        )
+
+        # Verify response is AIMessage
+        assert isinstance(result, AIMessage)
+
+        # Verify tool_calls exist
+        assert len(result.tool_calls) > 0, "Expected at least one tool call"
+
+        # Verify args are dicts, not strings
+        for tool_call in result.tool_calls:
+            assert isinstance(tool_call['args'], dict), \
+                f"tool_calls.args should be dict, got {type(tool_call['args'])}"
+            assert 'a' in tool_call['args'], "Missing expected arg 'a'"
+            assert 'b' in tool_call['args'], "Missing expected arg 'b'"
+
+    @pytest.mark.asyncio
+    async def test_deepseek_no_pydantic_validation_errors(self):
+        """Test that DeepSeek doesn't produce Pydantic validation errors"""
+        # Skip if no API key
+        if not os.getenv("OPENAI_API_KEY"):
+            pytest.skip("OPENAI_API_KEY not available")
+
+        model = create_model(
+            basemodel="deepseek/deepseek-chat",
+            api_key=os.getenv("OPENAI_API_KEY"),
+            base_url=os.getenv("OPENAI_API_BASE"),
+            temperature=0,
+            timeout=30
+        )
+
+        from langchain_core.tools import tool
+
+        @tool
+        def multiply(a: float, b: float) -> float:
+            """Multiply two numbers"""
+            return a * b
+
+        model_with_tools = model.bind_tools([multiply])
+
+        # This should NOT raise Pydantic validation errors
+        try:
+            result = await model_with_tools.ainvoke(
+                "Calculate 7 times 8"
+            )
+            assert isinstance(result, AIMessage)
+        except Exception as e:
+            # Check that it's not a Pydantic validation error
+            error_msg = str(e).lower()
+            assert "validation error" not in error_msg, \
+                f"Pydantic validation error occurred: {e}"
+            assert "input should be a valid dictionary" not in error_msg, \
+                f"tool_calls.args validation error occurred: {e}"
+            # Re-raise if it's a different error
+            raise
+```
+
+**Step 2: Run test (may require API access)**
+
+Run: `./venv/bin/python -m pytest tests/integration/test_deepseek_tool_calls.py -v`
+
+Expected:
+- If API key available: Tests PASS
+- If no API key: Tests SKIPPED with message "OPENAI_API_KEY not available"
+
+**Step 3: Commit integration test**
+
+```bash
+git add tests/integration/test_deepseek_tool_calls.py
+git commit -m "test: add DeepSeek tool calls integration tests
+
+Verifies that ChatDeepSeek properly handles tool_calls arguments:
+- Returns ChatDeepSeek for deepseek/* models
+- tool_calls.args are dicts (not JSON strings)
+- No Pydantic validation errors on args
+
+Tests skip gracefully if API keys not available."
+```
+
+---
+
+## Task 6: Update Documentation
+
+**Files:**
+- Modify: `CLAUDE.md` (sections on model configuration and troubleshooting)
+- Modify: `CHANGELOG.md`
+
+**Step 1: Update CLAUDE.md architecture section**
+
+In `CLAUDE.md`, find the "Agent System" section (around line 125) and update:
+
+```markdown
+**BaseAgent Key Methods:**
+- `initialize()`: Connect to MCP services, create AI model via model factory
+- `run_trading_session(date)`: Execute single day's trading with retry logic
+- `run_date_range(init_date, end_date)`: Process all weekdays in range
+- `get_trading_dates()`: Resume from last date in position.jsonl
+- `register_agent()`: Create initial position file with $10,000 cash
+
+**Model Factory:**
+The `create_model()` factory automatically selects the appropriate chat model:
+- `deepseek/*` models → `ChatDeepSeek` (native tool calling support)
+- `openai/*` models → `ChatOpenAI`
+- Other providers → `ChatOpenAI` (OpenAI-compatible endpoint)
+
+**Adding Custom Agents:**
+[existing content remains the same]
+```
+
+**Step 2: Update CLAUDE.md common issues section**
+
+In `CLAUDE.md`, find "Common Issues" (around line 420) and add:
+
+```markdown
+**DeepSeek Pydantic Validation Errors:**
+- Error: "Input should be a valid dictionary [type=dict_type, input_value='...', input_type=str]"
+- Cause: Using `ChatOpenAI` for DeepSeek models (OpenAI compatibility layer issue)
+- Fix: Ensure `langchain-deepseek` is installed and basemodel uses `deepseek/` prefix
+- The model factory automatically uses `ChatDeepSeek` for native support
+```
+
+**Step 3: Update CHANGELOG.md**
+
+Add new version entry at top of `CHANGELOG.md`:
+
+```markdown
+## [0.4.2] - 2025-11-05
+
+### Fixed
+- Fixed "No trading" message always displaying despite trading activity by initializing `IF_TRADE` to `True` (trades expected by default)
+- Resolved sporadic Pydantic validation errors for DeepSeek tool_calls arguments by switching to native `ChatDeepSeek` integration
+
+### Added
+- Added `agent/model_factory.py` for provider-specific model creation
+- Added `langchain-deepseek` dependency for native DeepSeek support
+- Added integration tests for DeepSeek tool calls argument parsing
+
+### Changed
+- `BaseAgent` now uses model factory instead of direct `ChatOpenAI` instantiation
+- DeepSeek models (`deepseek/*`) now use `ChatDeepSeek` instead of OpenAI compatibility layer
+```
+
+**Step 4: Commit documentation updates**
+
+```bash
+git add CLAUDE.md CHANGELOG.md
+git commit -m "docs: update for IF_TRADE and DeepSeek fixes
+
+- Document model factory architecture
+- Add troubleshooting for DeepSeek validation errors
+- Update changelog with version 0.4.2 fixes"
+```
+
+---
+
+## Task 7: End-to-End Verification
+
+**Files:**
+- Test: Full simulation with DeepSeek model
+- Verify: Logs show correct messages and no errors
+
+**Step 1: Run simulation with DeepSeek in PROD mode**
+
+Run:
+```bash
+# Ensure API keys are set
+export OPENAI_API_KEY="your-deepseek-key"
+export OPENAI_API_BASE="https://api.deepseek.com/v1"
+export DEPLOYMENT_MODE=PROD
+
+# Run short simulation (1 day)
+python main.py configs/default_config.json
+```
+
+**Step 2: Verify expected behaviors**
+
+Check logs for:
+
+1. **Model initialization:**
+   - ✅ Should show: "🤖 Using deepseek/deepseek-chat-v3.1 via ChatDeepSeek (PROD mode)"
+   - ❌ Should NOT show: "via ChatOpenAI"
+
+2. **Tool calls execution:**
+   - ✅ Should show: "[DEBUG] Extracted X tool messages from response"
+   - ❌ Should NOT show: "⚠️ Attempt 1 failed" with Pydantic validation errors
+   - Note: If retries occur for other reasons (network, rate limits), that's OK
+
+3. **Trading completion:**
+   - ✅ Should show: "✅ Trading completed" (if trades occurred)
+   - ❌ Should NOT show: "📊 No trading, maintaining positions" (if trades occurred)
+
+**Step 3: Check database for trade records**
+
+Run:
+```bash
+sqlite3 data/jobs.db "SELECT job_id, date, model, status FROM trading_days ORDER BY created_at DESC LIMIT 5;"
+```
+
+Expected: Recent records show `status='completed'` for DeepSeek runs
+
+**Step 4: Verify position tracking**
+
+Run:
+```bash
+sqlite3 data/jobs.db "SELECT trading_day_id, action_type, symbol, quantity FROM actions WHERE trading_day_id IN (SELECT id FROM trading_days ORDER BY created_at DESC LIMIT 1);"
+```
+
+Expected: Shows buy/sell actions if AI made trades
+
+**Step 5: Run test suite**
+
+Run full test suite to ensure no regressions:
+
+```bash
+./venv/bin/python -m pytest tests/ -v --cov=. --cov-report=term-missing
+```
+
+Expected: All tests PASS, coverage >= 85%
+
+**Step 6: Final commit**
+
+```bash
+git add -A
+git commit -m "chore: verify end-to-end functionality after fixes
+
+Confirmed:
+- DeepSeek models use ChatDeepSeek (no validation errors)
+- Trading completion shows correct message
+- Database tracking works correctly
+- All tests pass with good coverage"
+```
+
+---
+
+## Summary of Changes
+
+### Files Modified:
+1. `api/runtime_manager.py` - IF_TRADE initialization fix
+2. `requirements.txt` - Added langchain-deepseek dependency
+3. `agent/base_agent/base_agent.py` - Integrated model factory
+4. `tests/unit/test_runtime_manager.py` - Updated test expectations
+5. `CLAUDE.md` - Architecture and troubleshooting updates
+6. `CHANGELOG.md` - Version 0.4.2 release notes
+
+### Files Created:
+1. `agent/model_factory.py` - Provider-specific model creation
+2. `tests/unit/test_model_factory.py` - Model factory tests
+3. `tests/integration/test_deepseek_tool_calls.py` - DeepSeek integration tests
+
+### Testing Strategy:
+- Unit tests for IF_TRADE initialization
+- Unit tests for model factory provider routing
+- Integration tests for DeepSeek tool calls
+- End-to-end verification with real simulation
+- Full test suite regression check
+
+### Verification Commands:
+```bash
+# Quick test (unit tests only)
+bash scripts/quick_test.sh
+
+# Full test suite
+bash scripts/run_tests.sh
+
+# End-to-end simulation
+DEPLOYMENT_MODE=PROD python main.py configs/default_config.json
+```
+
+---
+
+## Notes for Engineer
+
+**Key Architectural Changes:**
+- Factory pattern separates provider-specific logic from agent core
+- Native integrations preferred over compatibility layers
+- IF_TRADE semantics: True = trades expected, tools set to False after execution
+
+**Why These Fixes Work:**
+1. **IF_TRADE**: Design always intended True initialization, False was a typo
+2. **DeepSeek**: Native ChatDeepSeek handles tool_calls parsing correctly, eliminating sporadic OpenAI compatibility layer bugs
+
+**Testing Philosophy:**
+- @superpowers:test-driven-development - Write test first, watch it fail, implement, verify pass
+- @superpowers:verification-before-completion - Never claim "it works" without running verification
+- Each commit should leave the codebase in a working state
+
+**If You Get Stuck:**
+- Check logs for exact error messages
+- Run pytest with `-vv` for verbose output
+- Use `git diff` to verify changes match plan
+- @superpowers:systematic-debugging - Never guess, always investigate root cause

tests/unit/test_chat_model_wrapper.py

@@ -1,216 +0,0 @@
-"""
-Unit tests for ChatModelWrapper - tool_calls args parsing fix
-"""
-
-import json
-import pytest
-from unittest.mock import Mock, AsyncMock
-from langchain_core.messages import AIMessage
-from langchain_core.outputs import ChatResult, ChatGeneration
-
-from agent.chat_model_wrapper import ToolCallArgsParsingWrapper
-
-
-class TestToolCallArgsParsingWrapper:
-    """Tests for ToolCallArgsParsingWrapper"""
-
-    @pytest.fixture
-    def mock_model(self):
-        """Create a mock chat model"""
-        model = Mock()
-        model._llm_type = "mock-model"
-        return model
-
-    @pytest.fixture
-    def wrapper(self, mock_model):
-        """Create a wrapper around mock model"""
-        return ToolCallArgsParsingWrapper(model=mock_model)
-
-    def test_fix_tool_calls_with_string_args(self, wrapper):
-        """Test that string args are parsed to dict"""
-        # Create message with tool_calls where args is a JSON string
-        message = AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "name": "buy",
-                    "args": '{"symbol": "AAPL", "amount": 10}',  # String, not dict
-                    "id": "call_123"
-                }
-            ]
-        )
-
-        fixed_message = wrapper._fix_tool_calls(message)
-
-        # Check that args is now a dict
-        assert isinstance(fixed_message.tool_calls[0]['args'], dict)
-        assert fixed_message.tool_calls[0]['args'] == {"symbol": "AAPL", "amount": 10}
-
-    def test_fix_tool_calls_with_dict_args(self, wrapper):
-        """Test that dict args are left unchanged"""
-        # Create message with tool_calls where args is already a dict
-        message = AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "name": "buy",
-                    "args": {"symbol": "AAPL", "amount": 10},  # Already a dict
-                    "id": "call_123"
-                }
-            ]
-        )
-
-        fixed_message = wrapper._fix_tool_calls(message)
-
-        # Check that args is still a dict
-        assert isinstance(fixed_message.tool_calls[0]['args'], dict)
-        assert fixed_message.tool_calls[0]['args'] == {"symbol": "AAPL", "amount": 10}
-
-    def test_fix_tool_calls_with_invalid_json(self, wrapper):
-        """Test that invalid JSON string is left unchanged"""
-        # Create message with tool_calls where args is an invalid JSON string
-        message = AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "name": "buy",
-                    "args": 'invalid json {',  # Invalid JSON
-                    "id": "call_123"
-                }
-            ]
-        )
-
-        fixed_message = wrapper._fix_tool_calls(message)
-
-        # Check that args is still a string (parsing failed)
-        assert isinstance(fixed_message.tool_calls[0]['args'], str)
-        assert fixed_message.tool_calls[0]['args'] == 'invalid json {'
-
-    def test_fix_tool_calls_no_tool_calls(self, wrapper):
-        """Test that messages without tool_calls are left unchanged"""
-        message = AIMessage(content="Hello, world!")
-        fixed_message = wrapper._fix_tool_calls(message)
-
-        assert fixed_message == message
-
-    def test_generate_with_string_args(self, wrapper, mock_model):
-        """Test _generate method with string args"""
-        # Create a response with string args
-        original_message = AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "name": "buy",
-                    "args": '{"symbol": "MSFT", "amount": 5}',
-                    "id": "call_456"
-                }
-            ]
-        )
-
-        mock_result = ChatResult(
-            generations=[ChatGeneration(message=original_message)]
-        )
-        mock_model._generate.return_value = mock_result
-
-        # Call wrapper's _generate
-        result = wrapper._generate(messages=[], stop=None, run_manager=None)
-
-        # Check that args is now a dict
-        fixed_message = result.generations[0].message
-        assert isinstance(fixed_message.tool_calls[0]['args'], dict)
-        assert fixed_message.tool_calls[0]['args'] == {"symbol": "MSFT", "amount": 5}
-
-    @pytest.mark.asyncio
-    async def test_agenerate_with_string_args(self, wrapper, mock_model):
-        """Test _agenerate method with string args"""
-        # Create a response with string args
-        original_message = AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "name": "sell",
-                    "args": '{"symbol": "GOOGL", "amount": 3}',
-                    "id": "call_789"
-                }
-            ]
-        )
-
-        mock_result = ChatResult(
-            generations=[ChatGeneration(message=original_message)]
-        )
-        mock_model._agenerate = AsyncMock(return_value=mock_result)
-
-        # Call wrapper's _agenerate
-        result = await wrapper._agenerate(messages=[], stop=None, run_manager=None)
-
-        # Check that args is now a dict
-        fixed_message = result.generations[0].message
-        assert isinstance(fixed_message.tool_calls[0]['args'], dict)
-        assert fixed_message.tool_calls[0]['args'] == {"symbol": "GOOGL", "amount": 3}
-
-    def test_invoke_with_string_args(self, wrapper, mock_model):
-        """Test invoke method with string args"""
-        original_message = AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "name": "buy",
-                    "args": '{"symbol": "NVDA", "amount": 20}',
-                    "id": "call_999"
-                }
-            ]
-        )
-
-        mock_model.invoke.return_value = original_message
-
-        # Call wrapper's invoke
-        result = wrapper.invoke(input=[])
-
-        # Check that args is now a dict
-        assert isinstance(result.tool_calls[0]['args'], dict)
-        assert result.tool_calls[0]['args'] == {"symbol": "NVDA", "amount": 20}
-
-    @pytest.mark.asyncio
-    async def test_ainvoke_with_string_args(self, wrapper, mock_model):
-        """Test ainvoke method with string args"""
-        original_message = AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "name": "sell",
-                    "args": '{"symbol": "TSLA", "amount": 15}',
-                    "id": "call_111"
-                }
-            ]
-        )
-
-        mock_model.ainvoke = AsyncMock(return_value=original_message)
-
-        # Call wrapper's ainvoke
-        result = await wrapper.ainvoke(input=[])
-
-        # Check that args is now a dict
-        assert isinstance(result.tool_calls[0]['args'], dict)
-        assert result.tool_calls[0]['args'] == {"symbol": "TSLA", "amount": 15}
-
-    def test_bind_tools_returns_wrapper(self, wrapper, mock_model):
-        """Test that bind_tools returns a new wrapper"""
-        mock_bound = Mock()
-        mock_model.bind_tools.return_value = mock_bound
-
-        result = wrapper.bind_tools(tools=[], strict=True)
-
-        # Check that result is a wrapper around the bound model
-        assert isinstance(result, ToolCallArgsParsingWrapper)
-        assert result.wrapped_model == mock_bound
-
-    def test_bind_returns_wrapper(self, wrapper, mock_model):
-        """Test that bind returns a new wrapper"""
-        mock_bound = Mock()
-        mock_model.bind.return_value = mock_bound
-
-        result = wrapper.bind(max_tokens=100)
-
-        # Check that result is a wrapper around the bound model
-        assert isinstance(result, ToolCallArgsParsingWrapper)
-        assert result.wrapped_model == mock_bound