feat: enhance hover data to include formatted timestamps and active agent counts for the last 24 hours

app.py

@@ -932,8 +932,16 @@ def create_combined_roi_time_series_graph(df):
         hover_data_roi = []
         for idx, row in avg_roi_data_with_ma.iterrows():
             timestamp = row['timestamp']
+            # Format timestamp to show only up to seconds (not milliseconds)
+            formatted_timestamp = timestamp.strftime('%Y-%m-%d %H:%M:%S')
+            
+            # Calculate number of active agents in the last 24 hours
+            time_24h_ago = timestamp - pd.Timedelta(hours=24)
+            active_agents = len(df[(df['timestamp'] >= time_24h_ago) & 
+                                  (df['timestamp'] <= timestamp)]['agent_id'].unique())
+            
             hover_data_roi.append(
-                f"Time: {timestamp}<br>Avg ROI (3d window): {row['moving_avg']:.2f}%"
+                f"Time: {formatted_timestamp}<br>Avg ROI (3d window): {row['moving_avg']:.2f}%<br>Active agents (24h): {active_agents}"
             )
         
         fig.add_trace(
@@ -1581,8 +1589,16 @@ def create_combined_time_series_graph(df):
         hover_data_apr = []
         for idx, row in avg_apr_data_with_ma.iterrows():
             timestamp = row['timestamp']
+            # Format timestamp to show only up to seconds (not milliseconds)
+            formatted_timestamp = timestamp.strftime('%Y-%m-%d %H:%M:%S')
+            
+            # Calculate number of active agents in the last 24 hours
+            time_24h_ago = timestamp - pd.Timedelta(hours=24)
+            active_agents = len(apr_data[(apr_data['timestamp'] >= time_24h_ago) & 
+                                        (apr_data['timestamp'] <= timestamp)]['agent_id'].unique())
+            
             hover_data_apr.append(
-                f"Time: {timestamp}<br>Avg APR (3d window): {row['moving_avg']:.2f}"
+                f"Time: {formatted_timestamp}<br>Avg APR (3d window): {row['moving_avg']:.2f}<br>Active agents (24h): {active_agents}"
             )
         
         fig.add_trace(
@@ -1614,13 +1630,21 @@ def create_combined_time_series_graph(df):
             hover_data_adj = []
             for idx, row in filled_avg_apr_data.iterrows():
                 timestamp = row['timestamp']
+                # Format timestamp to show only up to seconds (not milliseconds)
+                formatted_timestamp = timestamp.strftime('%Y-%m-%d %H:%M:%S')
+                
+                # Calculate number of active agents in the last 24 hours
+                time_24h_ago = timestamp - pd.Timedelta(hours=24)
+                active_agents = len(apr_data[(apr_data['timestamp'] >= time_24h_ago) & 
+                                           (apr_data['timestamp'] <= timestamp)]['agent_id'].unique())
+                
                 if pd.notna(row['adjusted_moving_avg']):
                     hover_data_adj.append(
-                        f"Time: {timestamp}<br>Avg ETH Adjusted APR (3d window): {row['adjusted_moving_avg']:.2f}"
+                        f"Time: {formatted_timestamp}<br>Avg ETH Adjusted APR (3d window): {row['adjusted_moving_avg']:.2f}<br>Active agents (24h): {active_agents}"
                     )
                 else:
                     hover_data_adj.append(
-                        f"Time: {timestamp}<br>Avg ETH Adjusted APR (3d window): N/A"
+                        f"Time: {formatted_timestamp}<br>Avg ETH Adjusted APR (3d window): N/A<br>Active agents (24h): {active_agents}"
                     )
             
             fig.add_trace(