diff --git a/core/data_provider.py b/core/data_provider.py
index d6fa99a..12c7727 100644
--- a/core/data_provider.py
+++ b/core/data_provider.py
@@ -2590,8 +2590,15 @@ class DataProvider:
     def _should_refresh_pivot_bounds(self, symbol: str) -> bool:
         """Check if pivot bounds need refreshing"""
         try:
+            # Try to load from cache if not in memory
             if symbol not in self.pivot_bounds:
-                return True
+                cached_bounds = self._load_pivot_bounds_from_duckdb(symbol)
+                if cached_bounds:
+                    self.pivot_bounds[symbol] = cached_bounds
+                    logger.info(f"Loaded pivot bounds from cache for {symbol}")
+                    return False  # Cache is fresh, no need to refresh
+                else:
+                    return True  # No cache, need to refresh
             
             bounds = self.pivot_bounds[symbol]
             age = datetime.now() - bounds.created_timestamp
diff --git a/core/duckdb_storage.py b/core/duckdb_storage.py
index fd3ab49..9ac6a41 100644
--- a/core/duckdb_storage.py
+++ b/core/duckdb_storage.py
@@ -18,6 +18,7 @@ from datetime import datetime
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple, Any
 import json
+import threading
 
 logger = logging.getLogger(__name__)
 
@@ -37,6 +38,14 @@ class DuckDBStorage:
         # Connect to DuckDB
         self.conn = duckdb.connect(str(self.db_path))
         
+        # Batch logging for compact output
+        self._batch_buffer = []  # List of (symbol, timeframe, count, total) tuples
+        self._batch_lock = threading.Lock()
+        self._batch_flush_timer = None
+        self._batch_flush_delay = 0.5  # Flush after 0.5 seconds of inactivity
+        self._batch_timer_lock = threading.Lock()
+        self._flush_in_progress = False
+        
         # Initialize schema
         self._init_schema()
         
@@ -113,6 +122,52 @@ class DuckDBStorage:
         
         logger.info("DuckDB schema initialized (all data in tables)")
     
+    def _schedule_batch_flush(self):
+        """Schedule a batch flush after delay, resetting timer on each call"""
+        with self._batch_timer_lock:
+            # Cancel existing timer if any
+            if self._batch_flush_timer:
+                self._batch_flush_timer.cancel()
+            
+            # Start new timer that will flush after delay
+            self._batch_flush_timer = threading.Timer(self._batch_flush_delay, self._flush_batch_log)
+            self._batch_flush_timer.daemon = True
+            self._batch_flush_timer.start()
+    
+    def _flush_batch_log(self):
+        """Flush accumulated batch logs as a single compact line"""
+        with self._batch_lock:
+            if not self._batch_buffer or self._flush_in_progress:
+                return
+            
+            self._flush_in_progress = True
+            
+            # Group by symbol for better readability
+            symbol_groups = {}
+            for symbol, timeframe, count, total in self._batch_buffer:
+                if symbol not in symbol_groups:
+                    symbol_groups[symbol] = []
+                symbol_groups[symbol].append((timeframe, count, total))
+            
+            # Build compact log message
+            parts = []
+            for symbol in sorted(symbol_groups.keys()):
+                symbol_parts = []
+                for timeframe, count, total in sorted(symbol_groups[symbol]):
+                    symbol_parts.append(f"{timeframe}({count}, total: {total})")
+                parts.append(f"{symbol}: {', '.join(symbol_parts)}")
+            
+            log_msg = "Stored candles batch: " + " | ".join(parts)
+            logger.info(log_msg)
+            
+            # Clear buffer and reset flag
+            self._batch_buffer.clear()
+            self._flush_in_progress = False
+        
+        # Clear timer reference after flushing
+        with self._batch_timer_lock:
+            self._batch_flush_timer = None
+    
     def store_ohlcv_data(self, symbol: str, timeframe: str, df: pd.DataFrame) -> int:
         """
         Store OHLCV data directly in DuckDB table
@@ -177,7 +232,11 @@ class DuckDBStorage:
                 VALUES (?, ?, ?, ?, ?, ?, ?)
             """, (symbol, timeframe, '', first_ts, last_ts, count, now_ts))
             
-            logger.info(f"Stored {len(df)} candles for {symbol} {timeframe} in DuckDB (total: {count})")
+            # Add to batch buffer instead of logging immediately
+            with self._batch_lock:
+                self._batch_buffer.append((symbol, timeframe, len(df), count))
+                self._schedule_batch_flush()
+            
             return len(df)
             
         except Exception as e:
@@ -534,6 +593,14 @@ class DuckDBStorage:
     
     def close(self):
         """Close database connection"""
+        # Cancel any pending timer
+        with self._batch_timer_lock:
+            if self._batch_flush_timer:
+                self._batch_flush_timer.cancel()
+        
+        # Flush any pending batch logs
+        self._flush_batch_log()
+        
         if self.conn:
             self.conn.close()
             logger.info("DuckDB connection closed")