diff --git a/__pycache__/config.cpython-314.pyc b/__pycache__/config.cpython-314.pyc
index 9d6bec9..886d755 100644
Binary files a/__pycache__/config.cpython-314.pyc and b/__pycache__/config.cpython-314.pyc differ
diff --git a/__pycache__/data_provider.cpython-314.pyc b/__pycache__/data_provider.cpython-314.pyc
index d62f771..61a6bc3 100644
Binary files a/__pycache__/data_provider.cpython-314.pyc and b/__pycache__/data_provider.cpython-314.pyc differ
diff --git a/__pycache__/signal_aggregator.cpython-314.pyc b/__pycache__/signal_aggregator.cpython-314.pyc
index 19acf2f..1770de2 100644
Binary files a/__pycache__/signal_aggregator.cpython-314.pyc and b/__pycache__/signal_aggregator.cpython-314.pyc differ
diff --git a/data_provider.py b/data_provider.py
index 3c787b9..c469eec 100644
--- a/data_provider.py
+++ b/data_provider.py
@@ -357,7 +357,7 @@ class ForexDataProvider:
                 cache_key = f"{symbol}_{timeframe.value}"
                 self._cache[cache_key] = df
                 self._cache_timestamps[cache_key] = now
-                logger.debug(f"Batch cached {len(df)} bars for {td_sym} @ {interval}")
+                logger.info(f"[live] {td_sym} {interval}: {len(df)} bars, last close={df['Close'].iloc[-1]:.5f}")
 
             except Exception as e:
                 logger.warning(f"Failed to parse batch data for {td_sym}: {e}")
@@ -426,7 +426,7 @@ class ForexDataProvider:
 
             df = pd.DataFrame(records).set_index("Datetime")
             df.index.name = "Datetime"
-            logger.debug(f"Fetched {len(df)} bars for {td_symbol} @ {interval}")
+            logger.info(f"[live] {td_symbol} {interval}: {len(df)} bars, last close={df['Close'].iloc[-1]:.5f}")
             return df
 
         except (DataUnavailableError, DataIntegrityError):
@@ -479,7 +479,7 @@ class ForexDataProvider:
         if std_ret > 0:
             outlier_mask = (returns - returns.mean()).abs() > 10 * std_ret
             if outlier_mask.any():
-                logger.warning(f"{symbol}: Removing {outlier_mask.sum()} extreme outlier bars")
+                logger.debug(f"{symbol}: Removing {outlier_mask.sum()} extreme outlier bars")
                 df = df[~outlier_mask]
 
         min_required = max(