docs: consolidate fixes into v0.4.1 release

Merged v0.4.2 changes into v0.4.1:
- IF_TRADE initialization fix
- Native ChatDeepSeek integration for DeepSeek models
- Model factory pattern implementation
- Removed obsolete wrapper code

Preparing v0.4.1-alpha for deployment testing.

CHANGELOG.md

@@ -7,7 +7,25 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
-## [0.4.0] - 2025-11-04
+## [0.4.1] - 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
+- Root cause: OpenAI compatibility layer did not consistently parse DeepSeek's tool_calls arguments from JSON strings to dicts
+- Solution: Use native `langchain-deepseek` package which properly handles DeepSeek's response format
+
+### Added
+- Added `agent/model_factory.py` for provider-specific model creation
+- Added `langchain-deepseek>=0.1.20` 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` for native tool calling support
+- Removed obsolete `ToolCallArgsParsingWrapper` and related tests
+
+## [0.4.0] - 2025-11-05
 
 ### BREAKING CHANGES
 
@@ -130,6 +148,49 @@ New `/results?reasoning=full` returns:
 - Test coverage increased with 36+ new comprehensive tests
 - Documentation updated with complete API reference and database schema details
 
+### Fixed
+- **Critical:** Intra-day position tracking for sell-then-buy trades (e20dce7)
+  - Sell proceeds now immediately available for subsequent buy orders within same trading session
+  - ContextInjector maintains in-memory position state during trading sessions
+  - Position updates accumulate after each successful trade
+  - Enables agents to rebalance portfolios (sell + buy) in single session
+  - Added 13 comprehensive tests for position tracking
+- **Critical:** Tool message extraction in conversation history (462de3a, abb9cd0)
+  - Fixed bug where tool messages (buy/sell trades) were not captured when agent completed in single step
+  - Tool extraction now happens BEFORE finish signal check
+  - Reasoning summaries now accurately reflect actual trades executed
+  - Resolves issue where summarizer saw 0 tools despite multiple trades
+- Reasoning summary generation improvements (6d126db)
+  - Summaries now explicitly mention specific trades executed (symbols, quantities, actions)
+  - Added TRADES EXECUTED section highlighting tool calls
+  - Example: 'sold 1 GOOGL and 1 AMZN to reduce exposure' instead of 'maintain core holdings'
+- Final holdings calculation accuracy (a8d912b)
+  - Final positions now calculated from actions instead of querying incomplete database records
+  - Correctly handles first trading day with multiple trades
+  - New `_calculate_final_position_from_actions()` method applies all trades to calculate final state
+  - Holdings now persist correctly across all trading days
+  - Added 3 comprehensive tests for final position calculation
+- Holdings persistence between trading days (aa16480)
+  - Query now retrieves previous day's ending position as current day's starting position
+  - Changed query from `date <=` to `date <` to prevent returning incomplete current-day records
+  - Fixes empty starting_position/final_position in API responses despite successful trades
+  - Updated tests to verify correct previous-day retrieval
+- Context injector trading_day_id synchronization (05620fa)
+  - ContextInjector now updated with trading_day_id after record creation
+  - Fixes "Trade failed: trading_day_id not found in runtime config" error
+  - MCP tools now correctly receive trading_day_id via context injection
+- Schema migration compatibility fixes (7c71a04)
+  - Updated position queries to use new trading_days schema instead of obsolete positions table
+  - Removed obsolete add_no_trade_record_to_db function calls
+  - Fixes "no such table: positions" error
+  - Simplified _handle_trading_result logic
+- Database referential integrity (9da65c2)
+  - Corrected Database default path from "data/trading.db" to "data/jobs.db"
+  - Ensures all components use same database file
+  - Fixes FOREIGN KEY constraint failures when creating trading_day records
+- Debug logging cleanup (1e7bdb5)
+  - Removed verbose debug logging from ContextInjector for cleaner output
+
 ## [0.3.1] - 2025-11-03
 
 ### Fixed

CLAUDE.md

@@ -167,12 +167,18 @@ bash main.sh
 ### Agent System
 
 **BaseAgent Key Methods:**
-- `initialize()`: Connect to MCP services, create AI model
+- `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:**
 1. Create new class inheriting from `BaseAgent`
 2. Add to `AGENT_REGISTRY` in `main.py`:
@@ -452,4 +458,10 @@ The project uses a well-organized documentation structure:
 **Agent Doesn't Stop Trading:**
 - Agent must output `<FINISH_SIGNAL>` within `max_steps`
 - Increase `max_steps` if agent needs more reasoning time
-- Check `log.jsonl` for errors preventing completion
+- Check `log.jsonl` for errors preventing completion
+
+**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

agent/base_agent/base_agent.py

@@ -12,7 +12,6 @@ from typing import Dict, List, Optional, Any
 from pathlib import Path
 
 from langchain_mcp_adapters.client import MultiServerMCPClient
-from langchain_openai import ChatOpenAI
 from langchain.agents import create_agent
 from dotenv import load_dotenv
 
@@ -33,6 +32,7 @@ from tools.deployment_config import (
 from agent.context_injector import ContextInjector
 from agent.pnl_calculator import DailyPnLCalculator
 from agent.reasoning_summarizer import ReasoningSummarizer
+from agent.model_factory import create_model
 
 # Load environment variables
 load_dotenv()
@@ -126,7 +126,7 @@ class BaseAgent:
         # Initialize components
         self.client: Optional[MultiServerMCPClient] = None
         self.tools: Optional[List] = None
-        self.model: Optional[ChatOpenAI] = None
+        self.model: Optional[Any] = None
         self.agent: Optional[Any] = None
 
         # Context injector for MCP tools
@@ -211,14 +211,18 @@ class BaseAgent:
                 self.model = MockChatModel(date="2025-01-01")  # Date will be updated per session
                 print(f"🤖 Using MockChatModel (DEV mode)")
             else:
-                self.model = ChatOpenAI(
-                    model=self.basemodel,
-                    base_url=self.openai_base_url,
+                # Use model factory for provider-specific implementations
+                self.model = create_model(
+                    basemodel=self.basemodel,
                     api_key=self.openai_api_key,
-                    max_retries=3,
+                    base_url=self.openai_base_url,
+                    temperature=0.7,
                     timeout=30
                 )
-                print(f"🤖 Using {self.basemodel} (PROD mode)")
+
+                # Determine model type for logging
+                model_class = self.model.__class__.__name__
+                print(f"🤖 Using {self.basemodel} via {model_class} (PROD mode)")
         except Exception as e:
             raise RuntimeError(f"❌ Failed to initialize AI model: {e}")
 
@@ -319,6 +323,60 @@ class BaseAgent:
             print(f"⚠️ Could not get position from database: {e}")
             return {}, self.initial_cash
 
+    def _calculate_final_position_from_actions(
+        self,
+        trading_day_id: int,
+        starting_cash: float
+    ) -> tuple[Dict[str, int], float]:
+        """
+        Calculate final holdings and cash from starting position + actions.
+
+        This is the correct way to get end-of-day position: start with the
+        starting position and apply all trades from the actions table.
+
+        Args:
+            trading_day_id: The trading day ID
+            starting_cash: Cash at start of day
+
+        Returns:
+            (holdings_dict, final_cash) where holdings_dict maps symbol -> quantity
+        """
+        from api.database import Database
+
+        db = Database()
+
+        # 1. Get starting holdings (from previous day's ending)
+        starting_holdings_list = db.get_starting_holdings(trading_day_id)
+        holdings = {h["symbol"]: h["quantity"] for h in starting_holdings_list}
+
+        # 2. Initialize cash
+        cash = starting_cash
+
+        # 3. Get all actions for this trading day
+        actions = db.get_actions(trading_day_id)
+
+        # 4. Apply each action to calculate final state
+        for action in actions:
+            symbol = action["symbol"]
+            quantity = action["quantity"]
+            price = action["price"]
+            action_type = action["action_type"]
+
+            if action_type == "buy":
+                # Add to holdings
+                holdings[symbol] = holdings.get(symbol, 0) + quantity
+                # Deduct from cash
+                cash -= quantity * price
+
+            elif action_type == "sell":
+                # Remove from holdings
+                holdings[symbol] = holdings.get(symbol, 0) - quantity
+                # Add to cash
+                cash += quantity * price
+
+        # 5. Return final state
+        return holdings, cash
+
     def _calculate_portfolio_value(
         self,
         holdings: Dict[str, int],
@@ -365,7 +423,7 @@ class BaseAgent:
         }
 
         if tool_name:
-            message["tool_name"] = tool_name
+            message["name"] = tool_name  # Use "name" not "tool_name" for consistency with summarizer
         if tool_input:
             message["tool_input"] = tool_input
 
@@ -479,6 +537,8 @@ Summary:"""
         # Update context injector with current trading date
         if self.context_injector:
             self.context_injector.today_date = today_date
+            # Reset position state for new trading day (enables intra-day tracking)
+            self.context_injector.reset_position()
 
         # Clear conversation history for new trading day
         self.clear_conversation_history()
@@ -538,6 +598,10 @@ Summary:"""
         from tools.general_tools import write_config_value
         write_config_value('TRADING_DAY_ID', trading_day_id)
 
+        # Update context_injector with trading_day_id for MCP tools
+        if self.context_injector:
+            self.context_injector.trading_day_id = trading_day_id
+
         # 6. Run AI trading session
         action_count = 0
 
@@ -575,21 +639,28 @@ Summary:"""
                 # Capture assistant response
                 self._capture_message("assistant", agent_response)
 
-                # Check stop signal
-                if STOP_SIGNAL in agent_response:
-                    print("✅ Received stop signal, trading session ended")
-                    print(agent_response)
-                    break
-
-                # Extract tool messages and count trade actions
+                # Extract tool messages BEFORE checking stop signal
+                # (agent may call tools AND return FINISH_SIGNAL in same response)
                 tool_msgs = extract_tool_messages(response)
+                print(f"[DEBUG] Extracted {len(tool_msgs)} tool messages from response")
                 for tool_msg in tool_msgs:
                     tool_name = getattr(tool_msg, 'name', None) or tool_msg.get('name') if isinstance(tool_msg, dict) else None
+                    tool_content = getattr(tool_msg, 'content', '') or tool_msg.get('content', '') if isinstance(tool_msg, dict) else str(tool_msg)
+
+                    # Capture tool message to conversation history
+                    self._capture_message("tool", tool_content, tool_name=tool_name)
+
                     if tool_name in ['buy', 'sell']:
                         action_count += 1
 
                 tool_response = '\n'.join([msg.content for msg in tool_msgs])
 
+                # Check stop signal AFTER processing tools
+                if STOP_SIGNAL in agent_response:
+                    print("✅ Received stop signal, trading session ended")
+                    print(agent_response)
+                    break
+
                 # Prepare new messages
                 new_messages = [
                     {"role": "assistant", "content": agent_response},
@@ -607,11 +678,26 @@ Summary:"""
         session_duration = time.time() - session_start
 
         # 7. Generate reasoning summary
+        # Debug: Log conversation history size
+        print(f"\n[DEBUG] Generating summary from {len(self.conversation_history)} messages")
+        assistant_msgs = [m for m in self.conversation_history if m.get('role') == 'assistant']
+        tool_msgs = [m for m in self.conversation_history if m.get('role') == 'tool']
+        print(f"[DEBUG] Assistant messages: {len(assistant_msgs)}, Tool messages: {len(tool_msgs)}")
+        if assistant_msgs:
+            first_assistant = assistant_msgs[0]
+            print(f"[DEBUG] First assistant message preview: {first_assistant.get('content', '')[:200]}...")
+
         summarizer = ReasoningSummarizer(model=self.model)
         summary = await summarizer.generate_summary(self.conversation_history)
 
-        # 8. Get current portfolio state from database
-        current_holdings, current_cash = self._get_current_portfolio_state(today_date, job_id)
+        # 8. Calculate final portfolio state from starting position + actions
+        # NOTE: We must calculate from actions, not query database, because:
+        # - On first day, database query returns empty (no previous day)
+        # - This method applies all trades to get accurate final state
+        current_holdings, current_cash = self._calculate_final_position_from_actions(
+            trading_day_id=trading_day_id,
+            starting_cash=starting_cash
+        )
 
         # 9. Save final holdings to database
         for symbol, quantity in current_holdings.items():
@@ -660,8 +746,6 @@ Summary:"""
     
     async def _handle_trading_result(self, today_date: str) -> None:
         """Handle trading results with database writes."""
-        from tools.price_tools import add_no_trade_record_to_db
-
         if_trade = get_config_value("IF_TRADE")
 
         if if_trade:
@@ -669,23 +753,10 @@ Summary:"""
             print("✅ Trading completed")
         else:
             print("📊 No trading, maintaining positions")
-
-            # Get context from runtime config
-            job_id = get_config_value("JOB_ID")
-            session_id = self.context_injector.session_id if self.context_injector else None
-
-            if not job_id or not session_id:
-                raise ValueError("Missing JOB_ID or session_id for no-trade record")
-
-            # Write no-trade record to database
-            add_no_trade_record_to_db(
-                today_date,
-                self.signature,
-                job_id,
-                session_id
-            )
-
             write_config_value("IF_TRADE", False)
+
+        # Note: In new schema, trading_day record is created at session start
+        # and updated at session end, so no separate no-trade record needed
     
     def register_agent(self) -> None:
         """Register new agent, create initial positions"""

agent/context_injector.py

@@ -3,15 +3,22 @@ Tool interceptor for injecting runtime context into MCP tool calls.
 
 This interceptor automatically injects `signature` and `today_date` parameters
 into buy/sell tool calls to support concurrent multi-model simulations.
+
+It also maintains in-memory position state to track cumulative changes within
+a single trading session, ensuring sell proceeds are immediately available for
+subsequent buy orders.
 """
 
-from typing import Any, Callable, Awaitable
+from typing import Any, Callable, Awaitable, Dict, Optional
 
 
 class ContextInjector:
     """
     Intercepts tool calls to inject runtime context (signature, today_date).
 
+    Also maintains cumulative position state during trading session to ensure
+    sell proceeds are immediately available for subsequent buys.
+
     Usage:
         interceptor = ContextInjector(signature="gpt-5", today_date="2025-10-01")
         client = MultiServerMCPClient(config, tool_interceptors=[interceptor])
@@ -34,6 +41,13 @@ class ContextInjector:
         self.job_id = job_id
         self.session_id = session_id  # Deprecated but kept for compatibility
         self.trading_day_id = trading_day_id
+        self._current_position: Optional[Dict[str, float]] = None
+
+    def reset_position(self) -> None:
+        """
+        Reset position state (call at start of each trading day).
+        """
+        self._current_position = None
 
     async def __call__(
         self,
@@ -43,6 +57,9 @@ class ContextInjector:
         """
         Intercept tool call and inject context parameters.
 
+        For buy/sell operations, maintains cumulative position state to ensure
+        sell proceeds are immediately available for subsequent buys.
+
         Args:
             request: Tool call request containing name and arguments
             handler: Async callable to execute the actual tool
@@ -52,10 +69,6 @@ class ContextInjector:
         """
         # Inject context parameters for trade tools
         if request.name in ["buy", "sell"]:
-            # Debug: Log self attributes BEFORE injection
-            print(f"[ContextInjector.__call__] ENTRY: id={id(self)}, self.signature={self.signature}, self.today_date={self.today_date}, self.job_id={self.job_id}, self.session_id={self.session_id}, self.trading_day_id={self.trading_day_id}")
-            print(f"[ContextInjector.__call__] Args BEFORE injection: {request.args}")
-
             # ALWAYS inject/override context parameters (don't trust AI-provided values)
             request.args["signature"] = self.signature
             request.args["today_date"] = self.today_date
@@ -66,8 +79,18 @@ class ContextInjector:
             if self.trading_day_id:
                 request.args["trading_day_id"] = self.trading_day_id
 
-            # Debug logging
-            print(f"[ContextInjector] Tool: {request.name}, Args after injection: {request.args}")
+            # Inject current position if we're tracking it
+            if self._current_position is not None:
+                request.args["_current_position"] = self._current_position
 
         # Call the actual tool handler
-        return await handler(request)
+        result = await handler(request)
+
+        # Update position state after successful trade
+        if request.name in ["buy", "sell"]:
+            # Check if result is a valid position dict (not an error)
+            if isinstance(result, dict) and "error" not in result and "CASH" in result:
+                # Update our tracked position with the new state
+                self._current_position = result.copy()
+
+        return result

agent/model_factory.py

@@ -0,0 +1,68 @@
+"""
+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
+        )

agent/reasoning_summarizer.py

@@ -36,15 +36,17 @@ class ReasoningSummarizer:
             summary_prompt = f"""You are reviewing your own trading decisions for the day.
 Summarize your trading strategy and key decisions in 2-3 sentences.
 
+IMPORTANT: Explicitly state what trades you executed (e.g., "sold 2 GOOGL shares" or "bought 10 NVDA shares"). If you made no trades, state that clearly.
+
 Focus on:
-- What you analyzed
-- Why you made the trades you did
+- What specific trades you executed (buy/sell, symbols, quantities)
+- Why you made those trades
 - Your overall strategy for the day
 
 Trading session log:
 {log_text}
 
-Provide a concise summary:"""
+Provide a concise summary that includes the actual trades executed:"""
 
             response = await self.model.ainvoke([
                 {"role": "user", "content": summary_prompt}
@@ -67,21 +69,39 @@ Provide a concise summary:"""
             reasoning_log: List of message dicts
 
         Returns:
-            Formatted text representation
+            Formatted text representation with emphasis on trades
         """
+        # Debug: Log what we're formatting
+        print(f"[DEBUG ReasoningSummarizer] Formatting {len(reasoning_log)} messages")
+        assistant_count = sum(1 for m in reasoning_log if m.get('role') == 'assistant')
+        tool_count = sum(1 for m in reasoning_log if m.get('role') == 'tool')
+        print(f"[DEBUG ReasoningSummarizer] Breakdown: {assistant_count} assistant, {tool_count} tool")
+
         formatted_parts = []
+        trades_executed = []
 
         for msg in reasoning_log:
             role = msg.get("role", "")
             content = msg.get("content", "")
+            tool_name = msg.get("name", "")
 
             if role == "assistant":
                 # AI's thoughts
                 formatted_parts.append(f"AI: {content[:200]}")
             elif role == "tool":
-                # Tool results
-                tool_name = msg.get("name", "tool")
-                formatted_parts.append(f"{tool_name}: {content[:100]}")
+                # Highlight trade tool calls
+                if tool_name in ["buy", "sell"]:
+                    trades_executed.append(f"{tool_name.upper()}: {content[:150]}")
+                    formatted_parts.append(f"TRADE - {tool_name.upper()}: {content[:150]}")
+                else:
+                    # Other tool results (search, price, etc.)
+                    formatted_parts.append(f"{tool_name}: {content[:100]}")
+
+        # Add summary of trades at the top
+        if trades_executed:
+            trade_summary = f"TRADES EXECUTED ({len(trades_executed)}):\n" + "\n".join(trades_executed)
+            formatted_parts.insert(0, trade_summary)
+            formatted_parts.insert(1, "\n--- FULL LOG ---")
 
         return "\n".join(formatted_parts)
 

agent_tools/tool_trade.py

@@ -28,16 +28,17 @@ def get_current_position_from_db(
     initial_cash: float = 10000.0
 ) -> Tuple[Dict[str, float], int]:
     """
-    Get current position from database (new schema).
+    Get starting position for current trading day from database (new schema).
 
-    Queries most recent trading_day record for this job+model up to date.
-    Returns ending holdings and cash from that day.
+    Queries most recent trading_day record BEFORE the given date (previous day's ending).
+    Returns ending holdings and cash from that previous day, which becomes the
+    starting position for the current day.
 
     Args:
         job_id: Job UUID
         model: Model signature
-        date: Current trading date
-        initial_cash: Initial cash if no prior data
+        date: Current trading date (will query for date < this)
+        initial_cash: Initial cash if no prior data (first trading day)
 
     Returns:
         (position_dict, action_count) where:
@@ -49,11 +50,11 @@ def get_current_position_from_db(
     cursor = conn.cursor()
 
     try:
-        # Query most recent trading_day up to date
+        # Query most recent trading_day BEFORE current date (previous day's ending position)
         cursor.execute("""
             SELECT id, ending_cash
             FROM trading_days
-            WHERE job_id = ? AND model = ? AND date <= ?
+            WHERE job_id = ? AND model = ? AND date < ?
             ORDER BY date DESC
             LIMIT 1
         """, (job_id, model, date))
@@ -90,7 +91,8 @@ def get_current_position_from_db(
 
 
 def _buy_impl(symbol: str, amount: int, signature: str = None, today_date: str = None,
-              job_id: str = None, session_id: int = None, trading_day_id: int = None) -> Dict[str, Any]:
+              job_id: str = None, session_id: int = None, trading_day_id: int = None,
+              _current_position: Dict[str, float] = None) -> Dict[str, Any]:
     """
     Internal buy implementation - accepts injected context parameters.
 
@@ -102,9 +104,13 @@ def _buy_impl(symbol: str, amount: int, signature: str = None, today_date: str =
         job_id: Job ID (injected)
         session_id: Session ID (injected, DEPRECATED)
         trading_day_id: Trading day ID (injected)
+        _current_position: Current position state (injected by ContextInjector)
 
     This function is not exposed to the AI model. It receives runtime context
     (signature, today_date, job_id, session_id, trading_day_id) from the ContextInjector.
+
+    The _current_position parameter enables intra-day position tracking, ensuring
+    sell proceeds are immediately available for subsequent buys.
     """
     # Validate required parameters
     if not job_id:
@@ -120,7 +126,13 @@ def _buy_impl(symbol: str, amount: int, signature: str = None, today_date: str =
 
     try:
         # Step 1: Get current position
-        current_position, next_action_id = get_current_position_from_db(job_id, signature, today_date)
+        # Use injected position if available (for intra-day tracking),
+        # otherwise query database for starting position
+        if _current_position is not None:
+            current_position = _current_position
+            next_action_id = 0  # Not used in new schema
+        else:
+            current_position, next_action_id = get_current_position_from_db(job_id, signature, today_date)
 
         # Step 2: Get stock price
         try:
@@ -185,7 +197,8 @@ def _buy_impl(symbol: str, amount: int, signature: str = None, today_date: str =
 
 @mcp.tool()
 def buy(symbol: str, amount: int, signature: str = None, today_date: str = None,
-        job_id: str = None, session_id: int = None, trading_day_id: int = None) -> Dict[str, Any]:
+        job_id: str = None, session_id: int = None, trading_day_id: int = None,
+        _current_position: Dict[str, float] = None) -> Dict[str, Any]:
     """
     Buy stock shares.
 
@@ -198,14 +211,15 @@ def buy(symbol: str, amount: int, signature: str = None, today_date: str = None,
           - Success: {"CASH": remaining_cash, "SYMBOL": shares, ...}
           - Failure: {"error": error_message, ...}
 
-    Note: signature, today_date, job_id, session_id, trading_day_id are
-    automatically injected by the system. Do not provide these parameters.
+    Note: signature, today_date, job_id, session_id, trading_day_id, _current_position
+    are automatically injected by the system. Do not provide these parameters.
     """
-    return _buy_impl(symbol, amount, signature, today_date, job_id, session_id, trading_day_id)
+    return _buy_impl(symbol, amount, signature, today_date, job_id, session_id, trading_day_id, _current_position)
 
 
 def _sell_impl(symbol: str, amount: int, signature: str = None, today_date: str = None,
-               job_id: str = None, session_id: int = None, trading_day_id: int = None) -> Dict[str, Any]:
+               job_id: str = None, session_id: int = None, trading_day_id: int = None,
+               _current_position: Dict[str, float] = None) -> Dict[str, Any]:
     """
     Sell stock function - writes to SQLite database.
 
@@ -217,11 +231,15 @@ def _sell_impl(symbol: str, amount: int, signature: str = None, today_date: str
         job_id: Job UUID (injected by ContextInjector)
         session_id: Trading session ID (injected by ContextInjector, DEPRECATED)
         trading_day_id: Trading day ID (injected by ContextInjector)
+        _current_position: Current position state (injected by ContextInjector)
 
     Returns:
         Dict[str, Any]:
           - Success: {"CASH": amount, symbol: quantity, ...}
           - Failure: {"error": message, ...}
+
+    The _current_position parameter enables intra-day position tracking, ensuring
+    sell proceeds are immediately available for subsequent buys.
     """
     # Validate required parameters
     if not job_id:
@@ -237,7 +255,13 @@ def _sell_impl(symbol: str, amount: int, signature: str = None, today_date: str
 
     try:
         # Step 1: Get current position
-        current_position, next_action_id = get_current_position_from_db(job_id, signature, today_date)
+        # Use injected position if available (for intra-day tracking),
+        # otherwise query database for starting position
+        if _current_position is not None:
+            current_position = _current_position
+            next_action_id = 0  # Not used in new schema
+        else:
+            current_position, next_action_id = get_current_position_from_db(job_id, signature, today_date)
 
         # Step 2: Validate position exists
         if symbol not in current_position:
@@ -297,7 +321,8 @@ def _sell_impl(symbol: str, amount: int, signature: str = None, today_date: str
 
 @mcp.tool()
 def sell(symbol: str, amount: int, signature: str = None, today_date: str = None,
-         job_id: str = None, session_id: int = None, trading_day_id: int = None) -> Dict[str, Any]:
+         job_id: str = None, session_id: int = None, trading_day_id: int = None,
+         _current_position: Dict[str, float] = None) -> Dict[str, Any]:
     """
     Sell stock shares.
 
@@ -310,10 +335,10 @@ def sell(symbol: str, amount: int, signature: str = None, today_date: str = None
           - Success: {"CASH": remaining_cash, "SYMBOL": shares, ...}
           - Failure: {"error": error_message, ...}
 
-    Note: signature, today_date, job_id, session_id, trading_day_id are
-    automatically injected by the system. Do not provide these parameters.
+    Note: signature, today_date, job_id, session_id, trading_day_id, _current_position
+    are automatically injected by the system. Do not provide these parameters.
     """
-    return _sell_impl(symbol, amount, signature, today_date, job_id, session_id, trading_day_id)
+    return _sell_impl(symbol, amount, signature, today_date, job_id, session_id, trading_day_id, _current_position)
 
 
 if __name__ == "__main__":

api/runtime_manager.py

@@ -80,7 +80,7 @@ class RuntimeConfigManager:
         initial_config = {
             "TODAY_DATE": date,
             "SIGNATURE": model_sig,
-            "IF_TRADE": False,
+            "IF_TRADE": True,  # FIX: Trades are expected by default
             "JOB_ID": job_id,
             "TRADING_DAY_ID": trading_day_id
         }

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

requirements.txt

@@ -1,6 +1,7 @@
 langchain==1.0.2
 langchain-openai==1.0.1
 langchain-mcp-adapters>=0.1.0
+langchain-deepseek>=0.1.20
 fastmcp==2.12.5
 fastapi>=0.120.0
 uvicorn[standard]>=0.27.0

tests/integration/test_deepseek_tool_calls.py

@@ -0,0 +1,118 @@
+"""
+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

tests/unit/test_calculate_final_position.py

@@ -0,0 +1,219 @@
+"""Test _calculate_final_position_from_actions method."""
+
+import pytest
+from unittest.mock import patch
+from agent.base_agent.base_agent import BaseAgent
+from api.database import Database
+
+
+@pytest.fixture
+def test_db():
+    """Create test database with schema."""
+    db = Database(":memory:")
+
+    # Create jobs record
+    db.connection.execute("""
+        INSERT INTO jobs (job_id, config_path, status, date_range, models, created_at)
+        VALUES ('test-job', 'test.json', 'running', '2025-10-07 to 2025-10-07', 'gpt-5', '2025-10-07T00:00:00Z')
+    """)
+    db.connection.commit()
+
+    return db
+
+
+def test_calculate_final_position_first_day_with_trades(test_db):
+    """Test calculating final position on first trading day with multiple trades."""
+
+    # Create trading_day for first day
+    trading_day_id = test_db.create_trading_day(
+        job_id='test-job',
+        model='gpt-5',
+        date='2025-10-07',
+        starting_cash=10000.0,
+        starting_portfolio_value=10000.0,
+        daily_profit=0.0,
+        daily_return_pct=0.0,
+        ending_cash=10000.0,  # Not yet calculated
+        ending_portfolio_value=10000.0,  # Not yet calculated
+        days_since_last_trading=1
+    )
+
+    # Add 15 buy actions (matching your real data)
+    actions_data = [
+        ("MSFT", 3, 528.285, "buy"),
+        ("GOOGL", 6, 248.27, "buy"),
+        ("NVDA", 10, 186.23, "buy"),
+        ("LRCX", 6, 149.23, "buy"),
+        ("AVGO", 2, 337.025, "buy"),
+        ("AMZN", 5, 220.88, "buy"),
+        ("MSFT", 2, 528.285, "buy"),  # Additional MSFT
+        ("AMD", 4, 214.85, "buy"),
+        ("CRWD", 1, 497.0, "buy"),
+        ("QCOM", 4, 169.9, "buy"),
+        ("META", 1, 717.72, "buy"),
+        ("NVDA", 20, 186.23, "buy"),  # Additional NVDA
+        ("NVDA", 13, 186.23, "buy"),  # Additional NVDA
+        ("NVDA", 20, 186.23, "buy"),  # Additional NVDA
+        ("NVDA", 53, 186.23, "buy"),  # Additional NVDA
+    ]
+
+    for symbol, quantity, price, action_type in actions_data:
+        test_db.create_action(
+            trading_day_id=trading_day_id,
+            action_type=action_type,
+            symbol=symbol,
+            quantity=quantity,
+            price=price
+        )
+
+    test_db.connection.commit()
+
+    # Create BaseAgent instance
+    agent = BaseAgent(signature="gpt-5", basemodel="anthropic/claude-sonnet-4", stock_symbols=[])
+
+    # Mock Database() to return our test_db
+    with patch('api.database.Database', return_value=test_db):
+        # Calculate final position
+        holdings, cash = agent._calculate_final_position_from_actions(
+            trading_day_id=trading_day_id,
+            starting_cash=10000.0
+        )
+
+    # Verify holdings
+    assert holdings["MSFT"] == 5, f"Expected 5 MSFT (3+2) but got {holdings.get('MSFT', 0)}"
+    assert holdings["GOOGL"] == 6, f"Expected 6 GOOGL but got {holdings.get('GOOGL', 0)}"
+    assert holdings["NVDA"] == 116, f"Expected 116 NVDA (10+20+13+20+53) but got {holdings.get('NVDA', 0)}"
+    assert holdings["LRCX"] == 6, f"Expected 6 LRCX but got {holdings.get('LRCX', 0)}"
+    assert holdings["AVGO"] == 2, f"Expected 2 AVGO but got {holdings.get('AVGO', 0)}"
+    assert holdings["AMZN"] == 5, f"Expected 5 AMZN but got {holdings.get('AMZN', 0)}"
+    assert holdings["AMD"] == 4, f"Expected 4 AMD but got {holdings.get('AMD', 0)}"
+    assert holdings["CRWD"] == 1, f"Expected 1 CRWD but got {holdings.get('CRWD', 0)}"
+    assert holdings["QCOM"] == 4, f"Expected 4 QCOM but got {holdings.get('QCOM', 0)}"
+    assert holdings["META"] == 1, f"Expected 1 META but got {holdings.get('META', 0)}"
+
+    # Verify cash (should be less than starting)
+    assert cash < 10000.0, f"Cash should be less than $10,000 but got ${cash}"
+
+    # Calculate expected cash
+    total_spent = sum(qty * price for _, qty, price, _ in actions_data)
+    expected_cash = 10000.0 - total_spent
+    assert abs(cash - expected_cash) < 0.01, f"Expected cash ${expected_cash} but got ${cash}"
+
+
+def test_calculate_final_position_with_previous_holdings(test_db):
+    """Test calculating final position when starting with existing holdings."""
+
+    # Create day 1 with ending holdings
+    day1_id = test_db.create_trading_day(
+        job_id='test-job',
+        model='gpt-5',
+        date='2025-10-06',
+        starting_cash=10000.0,
+        starting_portfolio_value=10000.0,
+        daily_profit=0.0,
+        daily_return_pct=0.0,
+        ending_cash=8000.0,
+        ending_portfolio_value=9500.0,
+        days_since_last_trading=1
+    )
+
+    # Add day 1 ending holdings
+    test_db.create_holding(day1_id, "AAPL", 10)
+    test_db.create_holding(day1_id, "MSFT", 5)
+
+    # Create day 2
+    day2_id = test_db.create_trading_day(
+        job_id='test-job',
+        model='gpt-5',
+        date='2025-10-07',
+        starting_cash=8000.0,
+        starting_portfolio_value=9500.0,
+        daily_profit=0.0,
+        daily_return_pct=0.0,
+        ending_cash=8000.0,
+        ending_portfolio_value=9500.0,
+        days_since_last_trading=1
+    )
+
+    # Add day 2 actions (buy more AAPL, sell some MSFT)
+    test_db.create_action(day2_id, "buy", "AAPL", 5, 150.0)
+    test_db.create_action(day2_id, "sell", "MSFT", 2, 500.0)
+
+    test_db.connection.commit()
+
+    # Create BaseAgent instance
+    agent = BaseAgent(signature="gpt-5", basemodel="anthropic/claude-sonnet-4", stock_symbols=[])
+
+    # Mock Database() to return our test_db
+    with patch('api.database.Database', return_value=test_db):
+        # Calculate final position for day 2
+        holdings, cash = agent._calculate_final_position_from_actions(
+            trading_day_id=day2_id,
+            starting_cash=8000.0
+        )
+
+    # Verify holdings
+    assert holdings["AAPL"] == 15, f"Expected 15 AAPL (10+5) but got {holdings.get('AAPL', 0)}"
+    assert holdings["MSFT"] == 3, f"Expected 3 MSFT (5-2) but got {holdings.get('MSFT', 0)}"
+
+    # Verify cash
+    # Started: 8000
+    # Buy 5 AAPL @ 150 = -750
+    # Sell 2 MSFT @ 500 = +1000
+    # Final: 8000 - 750 + 1000 = 8250
+    expected_cash = 8000.0 - (5 * 150.0) + (2 * 500.0)
+    assert abs(cash - expected_cash) < 0.01, f"Expected cash ${expected_cash} but got ${cash}"
+
+
+def test_calculate_final_position_no_trades(test_db):
+    """Test calculating final position when no trades were executed."""
+
+    # Create day 1 with ending holdings
+    day1_id = test_db.create_trading_day(
+        job_id='test-job',
+        model='gpt-5',
+        date='2025-10-06',
+        starting_cash=10000.0,
+        starting_portfolio_value=10000.0,
+        daily_profit=0.0,
+        daily_return_pct=0.0,
+        ending_cash=9000.0,
+        ending_portfolio_value=10000.0,
+        days_since_last_trading=1
+    )
+
+    test_db.create_holding(day1_id, "AAPL", 10)
+
+    # Create day 2 with NO actions
+    day2_id = test_db.create_trading_day(
+        job_id='test-job',
+        model='gpt-5',
+        date='2025-10-07',
+        starting_cash=9000.0,
+        starting_portfolio_value=10000.0,
+        daily_profit=0.0,
+        daily_return_pct=0.0,
+        ending_cash=9000.0,
+        ending_portfolio_value=10000.0,
+        days_since_last_trading=1
+    )
+
+    # No actions added
+    test_db.connection.commit()
+
+    # Create BaseAgent instance
+    agent = BaseAgent(signature="gpt-5", basemodel="anthropic/claude-sonnet-4", stock_symbols=[])
+
+    # Mock Database() to return our test_db
+    with patch('api.database.Database', return_value=test_db):
+        # Calculate final position
+        holdings, cash = agent._calculate_final_position_from_actions(
+            trading_day_id=day2_id,
+            starting_cash=9000.0
+        )
+
+    # Verify holdings unchanged
+    assert holdings["AAPL"] == 10, f"Expected 10 AAPL but got {holdings.get('AAPL', 0)}"
+
+    # Verify cash unchanged
+    assert abs(cash - 9000.0) < 0.01, f"Expected cash $9000 but got ${cash}"

tests/unit/test_context_injector.py

@@ -0,0 +1,192 @@
+"""Test ContextInjector position tracking functionality."""
+
+import pytest
+from agent.context_injector import ContextInjector
+
+
+@pytest.fixture
+def injector():
+    """Create a ContextInjector instance for testing."""
+    return ContextInjector(
+        signature="test-model",
+        today_date="2025-01-15",
+        job_id="test-job-123",
+        trading_day_id=1
+    )
+
+
+class MockRequest:
+    """Mock MCP tool request."""
+    def __init__(self, name, args=None):
+        self.name = name
+        self.args = args or {}
+
+
+async def mock_handler_success(request):
+    """Mock handler that returns a successful position update."""
+    # Simulate a successful trade returning updated position
+    if request.name == "sell":
+        return {
+            "CASH": 1100.0,
+            "AAPL": 7,
+            "MSFT": 5
+        }
+    elif request.name == "buy":
+        return {
+            "CASH": 50.0,
+            "AAPL": 7,
+            "MSFT": 12
+        }
+    return {}
+
+
+async def mock_handler_error(request):
+    """Mock handler that returns an error."""
+    return {"error": "Insufficient cash"}
+
+
+@pytest.mark.asyncio
+async def test_context_injector_initializes_with_no_position(injector):
+    """Test that ContextInjector starts with no position state."""
+    assert injector._current_position is None
+
+
+@pytest.mark.asyncio
+async def test_context_injector_reset_position(injector):
+    """Test that reset_position() clears position state."""
+    # Set some position state
+    injector._current_position = {"CASH": 5000.0, "AAPL": 10}
+
+    # Reset
+    injector.reset_position()
+
+    assert injector._current_position is None
+
+
+@pytest.mark.asyncio
+async def test_context_injector_injects_parameters(injector):
+    """Test that context parameters are injected into buy/sell requests."""
+    request = MockRequest("buy", {"symbol": "AAPL", "amount": 10})
+
+    # Mock handler that just returns the request args
+    async def handler(req):
+        return req.args
+
+    result = await injector(request, handler)
+
+    # Verify context was injected
+    assert result["signature"] == "test-model"
+    assert result["today_date"] == "2025-01-15"
+    assert result["job_id"] == "test-job-123"
+    assert result["trading_day_id"] == 1
+
+
+@pytest.mark.asyncio
+async def test_context_injector_tracks_position_after_successful_trade(injector):
+    """Test that position state is updated after successful trades."""
+    assert injector._current_position is None
+
+    # Execute a sell trade
+    request = MockRequest("sell", {"symbol": "AAPL", "amount": 3})
+    result = await injector(request, mock_handler_success)
+
+    # Verify position was updated
+    assert injector._current_position is not None
+    assert injector._current_position["CASH"] == 1100.0
+    assert injector._current_position["AAPL"] == 7
+    assert injector._current_position["MSFT"] == 5
+
+
+@pytest.mark.asyncio
+async def test_context_injector_injects_current_position_on_subsequent_trades(injector):
+    """Test that current position is injected into subsequent trade requests."""
+    # First trade - establish position
+    request1 = MockRequest("sell", {"symbol": "AAPL", "amount": 3})
+    await injector(request1, mock_handler_success)
+
+    # Second trade - should receive current position
+    request2 = MockRequest("buy", {"symbol": "MSFT", "amount": 7})
+
+    async def verify_injection_handler(req):
+        # Verify that _current_position was injected
+        assert "_current_position" in req.args
+        assert req.args["_current_position"]["CASH"] == 1100.0
+        assert req.args["_current_position"]["AAPL"] == 7
+        return mock_handler_success(req)
+
+    await injector(request2, verify_injection_handler)
+
+
+@pytest.mark.asyncio
+async def test_context_injector_does_not_update_position_on_error(injector):
+    """Test that position state is NOT updated when trade fails."""
+    # First successful trade
+    request1 = MockRequest("sell", {"symbol": "AAPL", "amount": 3})
+    await injector(request1, mock_handler_success)
+
+    original_position = injector._current_position.copy()
+
+    # Second trade that fails
+    request2 = MockRequest("buy", {"symbol": "MSFT", "amount": 100})
+    result = await injector(request2, mock_handler_error)
+
+    # Verify position was NOT updated
+    assert injector._current_position == original_position
+    assert "error" in result
+
+
+@pytest.mark.asyncio
+async def test_context_injector_does_not_inject_position_for_non_trade_tools(injector):
+    """Test that position is not injected for non-buy/sell tools."""
+    # Set up position state
+    injector._current_position = {"CASH": 5000.0, "AAPL": 10}
+
+    # Call a non-trade tool
+    request = MockRequest("search", {"query": "market news"})
+
+    async def verify_no_injection_handler(req):
+        assert "_current_position" not in req.args
+        return {"results": []}
+
+    await injector(request, verify_no_injection_handler)
+
+
+@pytest.mark.asyncio
+async def test_context_injector_full_trading_session_simulation(injector):
+    """Test full trading session with multiple trades and position tracking."""
+    # Reset position at start of day
+    injector.reset_position()
+    assert injector._current_position is None
+
+    # Trade 1: Sell AAPL
+    request1 = MockRequest("sell", {"symbol": "AAPL", "amount": 3})
+
+    async def handler1(req):
+        # First trade should NOT have injected position
+        assert req.args.get("_current_position") is None
+        return {"CASH": 1100.0, "AAPL": 7}
+
+    result1 = await injector(request1, handler1)
+    assert injector._current_position == {"CASH": 1100.0, "AAPL": 7}
+
+    # Trade 2: Buy MSFT (should use position from trade 1)
+    request2 = MockRequest("buy", {"symbol": "MSFT", "amount": 7})
+
+    async def handler2(req):
+        # Second trade SHOULD have injected position from trade 1
+        assert req.args["_current_position"]["CASH"] == 1100.0
+        assert req.args["_current_position"]["AAPL"] == 7
+        return {"CASH": 50.0, "AAPL": 7, "MSFT": 7}
+
+    result2 = await injector(request2, handler2)
+    assert injector._current_position == {"CASH": 50.0, "AAPL": 7, "MSFT": 7}
+
+    # Trade 3: Failed trade (should not update position)
+    request3 = MockRequest("buy", {"symbol": "GOOGL", "amount": 100})
+
+    async def handler3(req):
+        return {"error": "Insufficient cash", "cash_available": 50.0}
+
+    result3 = await injector(request3, handler3)
+    # Position should remain unchanged after failed trade
+    assert injector._current_position == {"CASH": 50.0, "AAPL": 7, "MSFT": 7}

tests/unit/test_get_position_new_schema.py

@@ -6,7 +6,7 @@ from api.database import Database
 
 
 def test_get_position_from_new_schema():
-    """Test position retrieval from trading_days + holdings."""
+    """Test position retrieval from trading_days + holdings (previous day)."""
 
     # Create test database
     db = Database(":memory:")
@@ -14,11 +14,11 @@ def test_get_position_from_new_schema():
     # Create prerequisite: jobs record
     db.connection.execute("""
         INSERT INTO jobs (job_id, config_path, status, date_range, models, created_at)
-        VALUES ('test-job-123', 'test_config.json', 'running', '2025-01-15 to 2025-01-15', 'test-model', '2025-01-15T10:00:00Z')
+        VALUES ('test-job-123', 'test_config.json', 'running', '2025-01-14 to 2025-01-16', 'test-model', '2025-01-14T10:00:00Z')
     """)
     db.connection.commit()
 
-    # Create trading_day with holdings
+    # Create trading_day with holdings for 2025-01-15
     trading_day_id = db.create_trading_day(
         job_id='test-job-123',
         model='test-model',
@@ -32,7 +32,7 @@ def test_get_position_from_new_schema():
         days_since_last_trading=0
     )
 
-    # Add ending holdings
+    # Add ending holdings for 2025-01-15
     db.create_holding(trading_day_id, 'AAPL', 10)
     db.create_holding(trading_day_id, 'MSFT', 5)
 
@@ -48,18 +48,19 @@ def test_get_position_from_new_schema():
     trade_module.get_db_connection = mock_get_db_connection
 
     try:
-        # Query position
+        # Query position for NEXT day (2025-01-16)
+        # Should retrieve previous day's (2025-01-15) ending position
         position, action_id = get_current_position_from_db(
             job_id='test-job-123',
             model='test-model',
-            date='2025-01-15'
+            date='2025-01-16'  # Query for day AFTER the trading_day record
         )
 
-        # Verify
-        assert position['AAPL'] == 10
-        assert position['MSFT'] == 5
-        assert position['CASH'] == 8000.0
-        assert action_id == 2  # 2 holdings = 2 actions
+        # Verify we got the previous day's ending position
+        assert position['AAPL'] == 10, f"Expected 10 AAPL but got {position.get('AAPL', 0)}"
+        assert position['MSFT'] == 5, f"Expected 5 MSFT but got {position.get('MSFT', 0)}"
+        assert position['CASH'] == 8000.0, f"Expected cash $8000 but got ${position['CASH']}"
+        assert action_id == 2, f"Expected 2 holdings but got {action_id}"
     finally:
         # Restore original function
         trade_module.get_db_connection = original_get_db_connection
@@ -95,3 +96,99 @@ def test_get_position_first_day():
         # Restore original function
         trade_module.get_db_connection = original_get_db_connection
         db.connection.close()
+
+
+def test_get_position_retrieves_previous_day_not_current():
+    """Test that get_current_position_from_db queries PREVIOUS day's ending, not current day.
+
+    This is the critical fix: when querying for day 2's starting position,
+    it should return day 1's ending position, NOT day 2's (incomplete) position.
+    """
+
+    db = Database(":memory:")
+
+    # Create prerequisite: jobs record
+    db.connection.execute("""
+        INSERT INTO jobs (job_id, config_path, status, date_range, models, created_at)
+        VALUES ('test-job-123', 'test_config.json', 'running', '2025-10-01 to 2025-10-03', 'gpt-5', '2025-10-01T10:00:00Z')
+    """)
+    db.connection.commit()
+
+    # Day 1: Create complete trading day with holdings
+    day1_id = db.create_trading_day(
+        job_id='test-job-123',
+        model='gpt-5',
+        date='2025-10-02',
+        starting_cash=10000.0,
+        starting_portfolio_value=10000.0,
+        daily_profit=0.0,
+        daily_return_pct=0.0,
+        ending_cash=2500.0,  # After buying stocks
+        ending_portfolio_value=10000.0,
+        days_since_last_trading=1
+    )
+
+    # Day 1 ending holdings (7 AMZN, 5 GOOGL, 6 MU, 3 QCOM, 4 MSFT, 1 CRWD, 10 NVDA, 3 AVGO)
+    db.create_holding(day1_id, 'AMZN', 7)
+    db.create_holding(day1_id, 'GOOGL', 5)
+    db.create_holding(day1_id, 'MU', 6)
+    db.create_holding(day1_id, 'QCOM', 3)
+    db.create_holding(day1_id, 'MSFT', 4)
+    db.create_holding(day1_id, 'CRWD', 1)
+    db.create_holding(day1_id, 'NVDA', 10)
+    db.create_holding(day1_id, 'AVGO', 3)
+
+    # Day 2: Create incomplete trading day (just started, no holdings yet)
+    day2_id = db.create_trading_day(
+        job_id='test-job-123',
+        model='gpt-5',
+        date='2025-10-03',
+        starting_cash=2500.0,  # From day 1 ending
+        starting_portfolio_value=10000.0,
+        daily_profit=0.0,
+        daily_return_pct=0.0,
+        ending_cash=2500.0,  # Not finalized yet
+        ending_portfolio_value=10000.0,  # Not finalized yet
+        days_since_last_trading=1
+    )
+    # NOTE: No holdings created for day 2 yet (trading in progress)
+
+    db.connection.commit()
+
+    # Mock get_db_connection to return our test db
+    import agent_tools.tool_trade as trade_module
+    original_get_db_connection = trade_module.get_db_connection
+
+    def mock_get_db_connection(path):
+        return db.connection
+
+    trade_module.get_db_connection = mock_get_db_connection
+
+    try:
+        # Query starting position for day 2 (2025-10-03)
+        # This should return day 1's ending position, NOT day 2's incomplete position
+        position, action_id = get_current_position_from_db(
+            job_id='test-job-123',
+            model='gpt-5',
+            date='2025-10-03'
+        )
+
+        # Verify we got day 1's ending position (8 holdings)
+        assert position['CASH'] == 2500.0, f"Expected cash $2500 but got ${position['CASH']}"
+        assert position['AMZN'] == 7, f"Expected 7 AMZN but got {position.get('AMZN', 0)}"
+        assert position['GOOGL'] == 5, f"Expected 5 GOOGL but got {position.get('GOOGL', 0)}"
+        assert position['MU'] == 6, f"Expected 6 MU but got {position.get('MU', 0)}"
+        assert position['QCOM'] == 3, f"Expected 3 QCOM but got {position.get('QCOM', 0)}"
+        assert position['MSFT'] == 4, f"Expected 4 MSFT but got {position.get('MSFT', 0)}"
+        assert position['CRWD'] == 1, f"Expected 1 CRWD but got {position.get('CRWD', 0)}"
+        assert position['NVDA'] == 10, f"Expected 10 NVDA but got {position.get('NVDA', 0)}"
+        assert position['AVGO'] == 3, f"Expected 3 AVGO but got {position.get('AVGO', 0)}"
+        assert action_id == 8, f"Expected 8 holdings but got {action_id}"
+
+        # Verify total holdings count (should NOT include day 2's empty holdings)
+        assert len(position) == 9, f"Expected 9 items (8 stocks + CASH) but got {len(position)}"
+
+    finally:
+        # Restore original function
+        trade_module.get_db_connection = original_get_db_connection
+        db.connection.close()

tests/unit/test_model_factory.py

@@ -0,0 +1,108 @@
+"""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"

tests/unit/test_runtime_manager.py

@@ -63,7 +63,7 @@ class TestRuntimeConfigCreation:
 
             assert config["TODAY_DATE"] == "2025-01-16"
             assert config["SIGNATURE"] == "gpt-5"
-            assert config["IF_TRADE"] is False
+            assert config["IF_TRADE"] is True
             assert config["JOB_ID"] == "test-job-123"
 
     def test_create_runtime_config_unique_paths(self):
@@ -108,6 +108,32 @@ class TestRuntimeConfigCreation:
             # Config file should exist
             assert os.path.exists(config_path)
 
+    def test_create_runtime_config_if_trade_defaults_true(self):
+        """Test that IF_TRADE initializes to True (trades expected by default)"""
+        from api.runtime_manager import RuntimeConfigManager
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            manager = RuntimeConfigManager(data_dir=temp_dir)
+
+            config_path = manager.create_runtime_config(
+                job_id="test-job-123",
+                model_sig="test-model",
+                date="2025-01-16",
+                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)
+
 
 @pytest.mark.unit
 class TestRuntimeConfigCleanup:

tests/unit/test_trade_tools_new_schema.py

@@ -295,3 +295,190 @@ def test_sell_writes_to_actions_table(test_db, monkeypatch):
     assert row[1] == 'AAPL'
     assert row[2] == 5
     assert row[3] == 160.0
+
+
+def test_intraday_position_tracking_sell_then_buy(test_db, monkeypatch):
+    """Test that sell proceeds are immediately available for subsequent buys."""
+    db, trading_day_id = test_db
+
+    # Setup: Create starting position with AAPL shares and limited cash
+    db.create_holding(trading_day_id, 'AAPL', 10)
+    db.connection.commit()
+
+    # Create a mock connection wrapper
+    class MockConnection:
+        def __init__(self, real_conn):
+            self.real_conn = real_conn
+
+        def cursor(self):
+            return self.real_conn.cursor()
+
+        def commit(self):
+            return self.real_conn.commit()
+
+        def rollback(self):
+            return self.real_conn.rollback()
+
+        def close(self):
+            pass
+
+    mock_conn = MockConnection(db.connection)
+    monkeypatch.setattr('agent_tools.tool_trade.get_db_connection',
+                       lambda x: mock_conn)
+
+    # Mock get_current_position_from_db to return starting position
+    monkeypatch.setattr('agent_tools.tool_trade.get_current_position_from_db',
+                       lambda job_id, sig, date: ({'CASH': 500.0, 'AAPL': 10}, 0))
+
+    monkeypatch.setenv('RUNTIME_ENV_PATH', '/tmp/test_runtime_intraday.json')
+
+    import json
+    with open('/tmp/test_runtime_intraday.json', 'w') as f:
+        json.dump({
+            'TODAY_DATE': '2025-01-15',
+            'SIGNATURE': 'test-model',
+            'JOB_ID': 'test-job-123',
+            'TRADING_DAY_ID': trading_day_id
+        }, f)
+
+    # Mock prices: AAPL sells for 200, MSFT costs 150
+    def mock_get_prices(date, symbols):
+        if 'AAPL' in symbols:
+            return {'AAPL_price': 200.0}
+        elif 'MSFT' in symbols:
+            return {'MSFT_price': 150.0}
+        return {}
+
+    monkeypatch.setattr('agent_tools.tool_trade.get_open_prices', mock_get_prices)
+
+    # Step 1: Sell 3 shares of AAPL for 600.0
+    # Starting cash: 500.0, proceeds: 600.0, new cash: 1100.0
+    result_sell = _sell_impl(
+        symbol='AAPL',
+        amount=3,
+        signature='test-model',
+        today_date='2025-01-15',
+        job_id='test-job-123',
+        trading_day_id=trading_day_id,
+        _current_position=None  # Use database position (starting position)
+    )
+
+    assert 'error' not in result_sell, f"Sell should succeed: {result_sell}"
+    assert result_sell['CASH'] == 1100.0, "Cash should be 500 + (3 * 200) = 1100"
+    assert result_sell['AAPL'] == 7, "AAPL shares should be 10 - 3 = 7"
+
+    # Step 2: Buy 7 shares of MSFT for 1050.0 using the position from the sell
+    # This should work because we pass the updated position from step 1
+    result_buy = _buy_impl(
+        symbol='MSFT',
+        amount=7,
+        signature='test-model',
+        today_date='2025-01-15',
+        job_id='test-job-123',
+        trading_day_id=trading_day_id,
+        _current_position=result_sell  # Use position from sell
+    )
+
+    assert 'error' not in result_buy, f"Buy should succeed with sell proceeds: {result_buy}"
+    assert result_buy['CASH'] == 50.0, "Cash should be 1100 - (7 * 150) = 50"
+    assert result_buy['MSFT'] == 7, "MSFT shares should be 7"
+    assert result_buy['AAPL'] == 7, "AAPL shares should still be 7"
+
+    # Verify both actions were recorded
+    cursor = db.connection.execute("""
+        SELECT action_type, symbol, quantity, price
+        FROM actions
+        WHERE trading_day_id = ?
+        ORDER BY created_at
+    """, (trading_day_id,))
+
+    actions = cursor.fetchall()
+    assert len(actions) == 2, "Should have 2 actions (sell + buy)"
+    assert actions[0][0] == 'sell' and actions[0][1] == 'AAPL'
+    assert actions[1][0] == 'buy' and actions[1][1] == 'MSFT'
+
+
+def test_intraday_tracking_without_position_injection_fails(test_db, monkeypatch):
+    """Test that without position injection, sell proceeds are NOT available for subsequent buys."""
+    db, trading_day_id = test_db
+
+    # Setup: Create starting position with AAPL shares and limited cash
+    db.create_holding(trading_day_id, 'AAPL', 10)
+    db.connection.commit()
+
+    # Create a mock connection wrapper
+    class MockConnection:
+        def __init__(self, real_conn):
+            self.real_conn = real_conn
+
+        def cursor(self):
+            return self.real_conn.cursor()
+
+        def commit(self):
+            return self.real_conn.commit()
+
+        def rollback(self):
+            return self.real_conn.rollback()
+
+        def close(self):
+            pass
+
+    mock_conn = MockConnection(db.connection)
+    monkeypatch.setattr('agent_tools.tool_trade.get_db_connection',
+                       lambda x: mock_conn)
+
+    # Mock get_current_position_from_db to ALWAYS return starting position
+    # (simulating the old buggy behavior)
+    monkeypatch.setattr('agent_tools.tool_trade.get_current_position_from_db',
+                       lambda job_id, sig, date: ({'CASH': 500.0, 'AAPL': 10}, 0))
+
+    monkeypatch.setenv('RUNTIME_ENV_PATH', '/tmp/test_runtime_no_injection.json')
+
+    import json
+    with open('/tmp/test_runtime_no_injection.json', 'w') as f:
+        json.dump({
+            'TODAY_DATE': '2025-01-15',
+            'SIGNATURE': 'test-model',
+            'JOB_ID': 'test-job-123',
+            'TRADING_DAY_ID': trading_day_id
+        }, f)
+
+    # Mock prices
+    def mock_get_prices(date, symbols):
+        if 'AAPL' in symbols:
+            return {'AAPL_price': 200.0}
+        elif 'MSFT' in symbols:
+            return {'MSFT_price': 150.0}
+        return {}
+
+    monkeypatch.setattr('agent_tools.tool_trade.get_open_prices', mock_get_prices)
+
+    # Step 1: Sell 3 shares of AAPL
+    result_sell = _sell_impl(
+        symbol='AAPL',
+        amount=3,
+        signature='test-model',
+        today_date='2025-01-15',
+        job_id='test-job-123',
+        trading_day_id=trading_day_id,
+        _current_position=None  # Don't inject position (old behavior)
+    )
+
+    assert 'error' not in result_sell, "Sell should succeed"
+
+    # Step 2: Try to buy 7 shares of MSFT WITHOUT passing updated position
+    # This should FAIL because it will query the database and get the original 500.0 cash
+    result_buy = _buy_impl(
+        symbol='MSFT',
+        amount=7,
+        signature='test-model',
+        today_date='2025-01-15',
+        job_id='test-job-123',
+        trading_day_id=trading_day_id,
+        _current_position=None  # Don't inject position (old behavior)
+    )
+
+    # This should fail with insufficient cash
+    assert 'error' in result_buy, "Buy should fail without position injection"
+    assert result_buy['error'] == 'Insufficient cash', f"Expected insufficient cash error, got: {result_buy}"
+    assert result_buy['cash_available'] == 500.0, "Should see original cash, not updated cash"

tools/price_tools.py

@@ -337,12 +337,12 @@ def get_today_init_position_from_db(
     cursor = conn.cursor()
 
     try:
-        # Get most recent position before today
+        # Get most recent trading day before today
         cursor.execute("""
-            SELECT p.id, p.cash
-            FROM positions p
-            WHERE p.job_id = ? AND p.model = ? AND p.date < ?
-            ORDER BY p.date DESC, p.action_id DESC
+            SELECT id, ending_cash
+            FROM trading_days
+            WHERE job_id = ? AND model = ? AND date < ?
+            ORDER BY date DESC
             LIMIT 1
         """, (job_id, modelname, today_date))
 
@@ -353,15 +353,15 @@ def get_today_init_position_from_db(
             logger.info(f"No previous position found for {modelname}, returning initial cash")
             return {"CASH": 10000.0}
 
-        position_id, cash = row
+        trading_day_id, cash = row
         position_dict = {"CASH": cash}
 
-        # Get holdings for this position
+        # Get holdings for this trading day
         cursor.execute("""
             SELECT symbol, quantity
             FROM holdings
-            WHERE position_id = ?
-        """, (position_id,))
+            WHERE trading_day_id = ?
+        """, (trading_day_id,))
 
         for symbol, quantity in cursor.fetchall():
             position_dict[symbol] = quantity