feat(worker): add _prepare_data method

Orchestrate data preparation phase:
- Check missing data
- Download if needed
- Filter completed dates
- Update job status

Co-Authored-By: Claude <noreply@anthropic.com>

api/simulation_worker.py

@@ -286,6 +286,72 @@ class SimulationWorker:
         """Store warnings in job metadata."""
         self.job_manager.add_job_warnings(self.job_id, warnings)
 
+    def _prepare_data(
+        self,
+        requested_dates: List[str],
+        models: List[str],
+        config_path: str
+    ) -> tuple:
+        """
+        Prepare price data for simulation.
+
+        Steps:
+        1. Update job status to "downloading_data"
+        2. Check what data is missing
+        3. Download missing data (with rate limit handling)
+        4. Determine available trading dates
+        5. Filter out already-completed model-days (idempotent)
+        6. Update job status to "running"
+
+        Args:
+            requested_dates: All dates requested for simulation
+            models: Model signatures to simulate
+            config_path: Path to configuration file
+
+        Returns:
+            Tuple of (available_dates, warnings)
+        """
+        from api.price_data_manager import PriceDataManager
+
+        warnings = []
+
+        # Update status
+        self.job_manager.update_job_status(self.job_id, "downloading_data")
+        logger.info(f"Job {self.job_id}: Checking price data availability...")
+
+        # Initialize price manager
+        price_manager = PriceDataManager(db_path=self.db_path)
+
+        # Check missing coverage
+        start_date = requested_dates[0]
+        end_date = requested_dates[-1]
+        missing_coverage = price_manager.get_missing_coverage(start_date, end_date)
+
+        # Download if needed
+        if missing_coverage:
+            logger.info(f"Job {self.job_id}: Missing data for {len(missing_coverage)} symbols")
+            self._download_price_data(price_manager, missing_coverage, requested_dates, warnings)
+        else:
+            logger.info(f"Job {self.job_id}: All price data available")
+
+        # Get available dates after download
+        available_dates = price_manager.get_available_trading_dates(start_date, end_date)
+
+        # Warn about skipped dates
+        skipped = set(requested_dates) - set(available_dates)
+        if skipped:
+            warnings.append(f"Skipped {len(skipped)} dates due to incomplete price data: {sorted(list(skipped))}")
+            logger.warning(f"Job {self.job_id}: {warnings[-1]}")
+
+        # Filter already-completed model-days (idempotent behavior)
+        available_dates = self._filter_completed_dates(available_dates, models)
+
+        # Update to running
+        self.job_manager.update_job_status(self.job_id, "running")
+        logger.info(f"Job {self.job_id}: Starting execution - {len(available_dates)} dates, {len(models)} models")
+
+        return available_dates, warnings
+
     def get_job_info(self) -> Dict[str, Any]:
         """
         Get job information.