docs: finalize v0.3.0 changelog for release

Consolidated all unreleased changes into v0.3.0 release dated 2025-11-03.

Key additions:
- Development Mode with mock AI provider
- Config Override System for Docker
- Async Price Download (non-blocking)
- Resume Mode (idempotent execution)
- Reasoning Logs API (GET /reasoning)
- Project rebrand to AI-Trader-Server

Includes comprehensive bug fixes for context injection, simulation
re-runs, database reliability, and configuration handling.

CHANGELOG.md

@@ -7,13 +7,43 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
-### Fixed
-- **Dev Mode Warning in Docker** - DEV mode startup warning now displays correctly in Docker logs
-  - Added FastAPI `@app.on_event("startup")` handler to trigger warning on API server startup
-  - Previously only appeared when running `python api/main.py` directly (not via uvicorn)
-  - Docker compose now includes `DEPLOYMENT_MODE` and `PRESERVE_DEV_DATA` environment variables
+## [0.3.0] - 2025-11-03
 
-## [0.3.0] - 2025-10-31
+### Added - Development & Testing Features
+- **Development Mode** - Mock AI provider for cost-free testing
+  - `DEPLOYMENT_MODE=DEV` enables mock AI responses with deterministic stock rotation
+  - Isolated dev database (`trading_dev.db`) separate from production data
+  - `PRESERVE_DEV_DATA=true` option to prevent dev database reset on startup
+  - No AI API costs during development and testing
+  - All API responses include `deployment_mode` field
+  - Startup warning displayed when running in DEV mode
+- **Config Override System** - Docker configuration merging
+  - Place custom configs in `user-configs/` directory
+  - Startup merges user config with default config
+  - Comprehensive validation with clear error messages
+  - Volume mount: `./user-configs:/app/user-configs`
+
+### Added - Enhanced API Features
+- **Async Price Download** - Non-blocking data preparation
+  - `POST /simulate/trigger` no longer blocks on price downloads
+  - New job status: `downloading_data` during data preparation
+  - Warnings field in status response for download issues
+  - Better user experience for large date ranges
+- **Resume Mode** - Idempotent simulation execution
+  - Jobs automatically skip already-completed model-days
+  - Safe to re-run jobs without duplicating work
+  - `status="skipped"` for already-completed executions
+  - Error-free job completion when partial results exist
+- **Reasoning Logs API** - Access AI decision-making history
+  - `GET /reasoning` endpoint for querying reasoning logs
+  - Filter by job_id, model_name, date, include_full_conversation
+  - Includes conversation history and tool usage
+  - Database-only storage (no JSONL files)
+  - AI-powered summary generation for reasoning sessions
+- **Job Skip Status** - Enhanced job status tracking
+  - New status: `skipped` for already-completed model-days
+  - Better differentiation between pending, running, and skipped
+  - Accurate job completion detection
 
 ### Added - Price Data Management & On-Demand Downloads
 - **SQLite Price Data Storage** - Replaced JSONL files with relational database
@@ -83,13 +113,22 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - Windmill integration patterns and examples
 
 ### Changed
+- **Project Rebrand** - AI-Trader renamed to AI-Trader-Server
+  - Updated all documentation for new project name
+  - Updated Docker images to ghcr.io/xe138/ai-trader-server
+  - Updated GitHub Actions workflows
+  - Updated README, CHANGELOG, and all user guides
 - **Architecture** - Transformed from batch-only to API-first service with database persistence
 - **Data Storage** - Migrated from JSONL files to SQLite relational database
   - Price data now stored in `price_data` table instead of `merged.jsonl`
   - Tools/price_tools.py updated to query database
-  - Position data remains in database (already migrated in earlier versions)
+  - Position data fully migrated to database-only storage (removed JSONL dependencies)
+  - Trade tools now read/write from database tables with lazy context injection
 - **Deployment** - Simplified to single API-only Docker service (REST API is new in v0.3.0)
+- **Logging** - Removed duplicate MCP service log files for cleaner output
 - **Configuration** - Simplified environment variable configuration
+  - **Added:** `DEPLOYMENT_MODE` (PROD/DEV) for environment control
+  - **Added:** `PRESERVE_DEV_DATA` (default: false) to keep dev data between runs
   - **Added:** `AUTO_DOWNLOAD_PRICE_DATA` (default: true) - Enable on-demand downloads
   - **Added:** `MAX_SIMULATION_DAYS` (default: 30) - Maximum date range size
   - **Added:** `API_PORT` for host port mapping (default: 8080, customizable for port conflicts)
@@ -137,6 +176,35 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - **Monitoring** - Health checks and status tracking
 - **Persistence** - SQLite database survives container restarts
 
+### Fixed
+- **Context Injection** - Runtime parameters correctly injected into MCP tools
+  - ContextInjector always overrides AI-provided parameters (defense-in-depth)
+  - Hidden context parameters from AI tool schema to prevent hallucination
+  - Resolved database locking issues with concurrent tool calls
+  - Proper async handling of tool reloading after context injection
+- **Simulation Re-runs** - Prevent duplicate execution of completed model-days
+  - Fixed job hanging when re-running partially completed simulations
+  - `_execute_date()` now skips already-completed model-days
+  - Job completion status correctly reflects skipped items
+- **Agent Initialization** - Correct parameter passing in API mode
+  - Fixed BaseAgent initialization parameters in ModelDayExecutor
+  - Resolved async execution and position storage issues
+- **Database Reliability** - Various improvements for concurrent access
+  - Fixed column existence checks before creating indexes
+  - Proper database path resolution in dev mode (prevents recursive _dev suffix)
+  - Module-level database initialization for uvicorn reliability
+  - Fixed database locking during concurrent writes
+  - Improved error handling in buy/sell functions
+- **Configuration** - Improved config handling
+  - Use enabled field from config to determine which models run
+  - Use config models when empty models list provided
+  - Correct handling of merged runtime configs in containers
+  - Proper get_db_path() usage to pass base database path
+- **Docker** - Various deployment improvements
+  - Removed non-existent data scripts from Dockerfile
+  - Proper respect for dev mode in entrypoint database initialization
+  - Correct closure usage to capture db_path in lifespan context manager
+
 ### Breaking Changes
 - **Batch Mode Removed** - All simulations now run through REST API
   - v0.2.0 used sequential batch execution via Docker entrypoint
@@ -147,7 +215,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - `merged.jsonl` no longer used (replaced by `price_data` table)
   - Automatic on-demand downloads eliminate need for manual data fetching
 - **Configuration Variables Changed**
-  - Added: `AUTO_DOWNLOAD_PRICE_DATA`, `MAX_SIMULATION_DAYS`, `API_PORT`
+  - Added: `DEPLOYMENT_MODE`, `PRESERVE_DEV_DATA`, `AUTO_DOWNLOAD_PRICE_DATA`, `MAX_SIMULATION_DAYS`, `API_PORT`
   - Removed: `RUNTIME_ENV_PATH`, MCP service ports, `WEB_HTTP_PORT`
   - MCP services now use fixed internal ports (not exposed to host)
 

agent_tools/tool_trade.py

@@ -82,24 +82,13 @@ def get_current_position_from_db(job_id: str, model: str, date: str) -> Tuple[Di
         conn.close()
 
 
-@mcp.tool()
-def buy(symbol: str, amount: int, signature: str = None, today_date: str = None,
-        job_id: str = None, session_id: int = None) -> Dict[str, Any]:
+def _buy_impl(symbol: str, amount: int, signature: str = None, today_date: str = None,
+              job_id: str = None, session_id: int = None) -> Dict[str, Any]:
     """
-    Buy stock function - writes to SQLite database.
+    Internal buy implementation - accepts injected context parameters.
 
-    Args:
-        symbol: Stock symbol (e.g., "AAPL", "MSFT")
-        amount: Number of shares to buy (positive integer)
-        signature: Model signature (injected by ContextInjector)
-        today_date: Trading date YYYY-MM-DD (injected by ContextInjector)
-        job_id: Job UUID (injected by ContextInjector)
-        session_id: Trading session ID (injected by ContextInjector)
-
-    Returns:
-        Dict[str, Any]:
-          - Success: {"CASH": amount, symbol: quantity, ...}
-          - Failure: {"error": message, ...}
+    This function is not exposed to the AI model. It receives runtime context
+    (signature, today_date, job_id, session_id) from the ContextInjector.
     """
     # Validate required parameters
     if not job_id:
@@ -206,8 +195,29 @@ def buy(symbol: str, amount: int, signature: str = None, today_date: str = None,
 
 
 @mcp.tool()
-def sell(symbol: str, amount: int, signature: str = None, today_date: str = None,
-         job_id: str = None, session_id: int = None) -> Dict[str, Any]:
+def buy(symbol: str, amount: int, signature: str = None, today_date: str = None,
+        job_id: str = None, session_id: int = None) -> Dict[str, Any]:
+    """
+    Buy stock shares.
+
+    Args:
+        symbol: Stock symbol (e.g., "AAPL", "MSFT", "GOOGL")
+        amount: Number of shares to buy (positive integer)
+
+    Returns:
+        Dict[str, Any]:
+          - Success: {"CASH": remaining_cash, "SYMBOL": shares, ...}
+          - Failure: {"error": error_message, ...}
+
+    Note: signature, today_date, job_id, session_id are automatically injected by the system.
+    Do not provide these parameters - they will be added automatically.
+    """
+    # Delegate to internal implementation
+    return _buy_impl(symbol, amount, signature, today_date, job_id, session_id)
+
+
+def _sell_impl(symbol: str, amount: int, signature: str = None, today_date: str = None,
+               job_id: str = None, session_id: int = None) -> Dict[str, Any]:
     """
     Sell stock function - writes to SQLite database.
 
@@ -327,6 +337,28 @@ def sell(symbol: str, amount: int, signature: str = None, today_date: str = None
         conn.close()
 
 
+@mcp.tool()
+def sell(symbol: str, amount: int, signature: str = None, today_date: str = None,
+         job_id: str = None, session_id: int = None) -> Dict[str, Any]:
+    """
+    Sell stock shares.
+
+    Args:
+        symbol: Stock symbol (e.g., "AAPL", "MSFT", "GOOGL")
+        amount: Number of shares to sell (positive integer)
+
+    Returns:
+        Dict[str, Any]:
+          - Success: {"CASH": remaining_cash, "SYMBOL": shares, ...}
+          - Failure: {"error": error_message, ...}
+
+    Note: signature, today_date, job_id, session_id are automatically injected by the system.
+    Do not provide these parameters - they will be added automatically.
+    """
+    # Delegate to internal implementation
+    return _sell_impl(symbol, amount, signature, today_date, job_id, session_id)
+
+
 if __name__ == "__main__":
     port = int(os.getenv("TRADE_HTTP_PORT", "8002"))
     mcp.run(transport="streamable-http", port=port)

api/simulation_worker.py

@@ -90,7 +90,7 @@ class SimulationWorker:
             logger.info(f"Starting job {self.job_id}: {len(date_range)} dates, {len(models)} models")
 
             # NEW: Prepare price data (download if needed)
-            available_dates, warnings = self._prepare_data(date_range, models, config_path)
+            available_dates, warnings, completion_skips = self._prepare_data(date_range, models, config_path)
 
             if not available_dates:
                 error_msg = "No trading dates available after price data preparation"
@@ -100,7 +100,7 @@ class SimulationWorker:
             # Execute available dates only
             for date in available_dates:
                 logger.info(f"Processing date {date} with {len(models)} models")
-                self._execute_date(date, models, config_path)
+                self._execute_date(date, models, config_path, completion_skips)
 
             # Job completed - determine final status
             progress = self.job_manager.get_job_progress(self.job_id)
@@ -145,7 +145,8 @@ class SimulationWorker:
                 "error": error_msg
             }
 
-    def _execute_date(self, date: str, models: List[str], config_path: str) -> None:
+    def _execute_date(self, date: str, models: List[str], config_path: str,
+                      completion_skips: Dict[str, Set[str]] = None) -> None:
         """
         Execute all models for a single date in parallel.
 
@@ -153,14 +154,24 @@ class SimulationWorker:
             date: Trading date (YYYY-MM-DD)
             models: List of model signatures to execute
             config_path: Path to configuration file
+            completion_skips: {model: {dates}} of already-completed model-days to skip
 
         Uses ThreadPoolExecutor to run all models concurrently for this date.
         Waits for all models to complete before returning.
+        Skips models that have already completed this date.
         """
+        if completion_skips is None:
+            completion_skips = {}
+
         with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
             # Submit all model executions for this date
             futures = []
             for model in models:
+                # Skip if this model-day was already completed
+                if date in completion_skips.get(model, set()):
+                    logger.debug(f"Skipping {model} on {date} (already completed)")
+                    continue
+
                 future = executor.submit(
                     self._execute_model_day,
                     date,
@@ -397,7 +408,10 @@ class SimulationWorker:
             config_path: Path to configuration file
 
         Returns:
-            Tuple of (available_dates, warnings)
+            Tuple of (available_dates, warnings, completion_skips)
+            - available_dates: Dates to process
+            - warnings: Warning messages
+            - completion_skips: {model: {dates}} of already-completed model-days
         """
         from api.price_data_manager import PriceDataManager
 
@@ -456,7 +470,7 @@ class SimulationWorker:
         self.job_manager.update_job_status(self.job_id, "running")
         logger.info(f"Job {self.job_id}: Starting execution - {len(dates_to_process)} dates, {len(models)} models")
 
-        return dates_to_process, warnings
+        return dates_to_process, warnings, completion_skips
 
     def get_job_info(self) -> Dict[str, Any]:
         """