chore: remove filters

app.py

@@ -1776,13 +1776,26 @@ def create_combined_time_series_graph(df):
     logger.info(f"Unique agents after APR filter: {apr_data['agent_id'].nunique()}")
     logger.info(f"Agent IDs after APR filter: {sorted(apr_data['agent_id'].unique().tolist())}")
     
-    # Filter APR outliers (±500% range)
-    before_outlier_filter = len(apr_data)
-    apr_data = apr_data[(apr_data['apr'] <= 500) & (apr_data['apr'] >= -500)]
-    after_outlier_filter = len(apr_data)
+    # Date-based APR percentage filtering: ±500% filter until June 22, 2025, then no filter
+    cutoff_date = datetime(2025, 6, 22)
+    before_cutoff = apr_data[apr_data['timestamp'] < cutoff_date]
+    after_cutoff = apr_data[apr_data['timestamp'] >= cutoff_date]
+    
+    # Apply ±500% filter to data before June 22, 2025
+    before_outlier_filter = len(before_cutoff)
+    before_cutoff_filtered = before_cutoff[(before_cutoff['apr'] <= 500) & (before_cutoff['apr'] >= -500)]
+    after_outlier_filter = len(before_cutoff_filtered)
     excluded_by_outlier = before_outlier_filter - after_outlier_filter
     
-    logger.info(f"APR outlier filtering: {before_outlier_filter} -> {after_outlier_filter} data points ({excluded_by_outlier} excluded)")
+    logger.info(f"APR filtering before June 22, 2025: {before_outlier_filter} -> {after_outlier_filter} data points ({excluded_by_outlier} excluded by ±500% filter)")
+    
+    # No filtering for data after June 22, 2025
+    logger.info(f"APR filtering after June 22, 2025: {len(after_cutoff)} data points (no percentage filter applied)")
+    
+    # Combine filtered before data with unfiltered after data
+    apr_data = pd.concat([before_cutoff_filtered, after_cutoff], ignore_index=True)
+    
+    logger.info(f"Total APR data after date-based filtering: {len(apr_data)} data points")
     
     # IMPORTANT: Filter data by hardcoded date range (June 6 to July 8, 2025)
     min_date = datetime(2025, 6, 6)