some layout fixes

app.py

@@ -414,6 +414,15 @@ with demo:
         with gr.TabItem("🪝 Retention metrics (WIP)"):
             with gr.Row():
                 gr.Markdown("# Wow retention by trader type")
+            with gr.Row():
+                gr.Markdown(
+                    """
+                    - Olas trader: agent using Mech, with a service ID and the corresponding safe in the registry
+                    - Non-Olas trader: agent using Mech, with no service ID
+                    - Unclassified trader: agent (safe/EOAs) not using Mechs
+                    """
+                )
+
             with gr.Row():
                 with gr.Column(scale=1):
                     gr.Markdown("## Wow retention in Pearl markets")

scripts/retention_metrics.py

@@ -64,18 +64,19 @@ def calculate_wow_retention_by_type(
 
 # Cohort Retention
 def calculate_cohort_retention(
-    df: pd.DataFrame, market_creator: str, trader_type: str, max_weeks=12
+    df: pd.DataFrame, market_creator: str, trader_type: str
 ) -> pd.DataFrame:
     df_filtered = df.loc[
         (df["market_creator"] == market_creator) & (df["trader_type"] == trader_type)
     ]
+    df_filtered = df_filtered.sort_values(by="creation_timestamp", ascending=True)
     # Get first week for each trader
-    first_trades = (
+    first_activity = (
         df_filtered.groupby("trader_address")
         .agg({"creation_timestamp": "min", "month_year_week": "first"})
         .reset_index()
     )
-    first_trades.columns = ["trader_address", "first_trade", "cohort_week"]
+    first_activity.columns = ["trader_address", "first_activity", "cohort_week"]
 
     # Get ordered list of unique weeks - converting to datetime for proper sorting
     all_weeks = df_filtered["month_year_week"].unique()
@@ -89,7 +90,7 @@ def calculate_cohort_retention(
     # Merge back to get all activities
     cohort_data = pd.merge(
         df_filtered,
-        first_trades[["trader_address", "cohort_week"]],
+        first_activity[["trader_address", "cohort_week"]],
         on="trader_address",
     )
 
@@ -114,10 +115,6 @@ def calculate_cohort_retention(
     retention_matrix.index = pd.to_datetime(retention_matrix.index)
     retention_matrix = retention_matrix.sort_index()
 
-    # Limit to max_weeks if specified
-    if max_weeks is not None and max_weeks < retention_matrix.shape[1]:
-        retention_matrix = retention_matrix.iloc[:, :max_weeks]
-
     return retention_matrix.round(2)
 
 

tabs/retention_plots.py

@@ -84,8 +84,8 @@ def plot_cohort_retention_heatmap(retention_matrix: pd.DataFrame, cmap: str):
 
     # Customize the plot
     plt.title("Cohort Retention Analysis", pad=20, size=14)
-    plt.xlabel("Weeks Since First Trade", size=12)
-    plt.ylabel("Cohort Starting Week", size=12)
+    plt.xlabel("Weeks Since First Activiy", size=12)
+    plt.ylabel("Cohort First Day of the Week", size=12)
 
     # Format week numbers on x-axis
     x_labels = [f"Week {i}" for i in retention_matrix.columns]