using new KPI dataframe

app.py

@@ -155,13 +155,13 @@ def load_all_data():
         repo_type="dataset",
     )
     df9 = pd.read_parquet(daa_pearl_df)
-    # Read pearl_agents.parquet
-    pearl_agents_df = hf_hub_download(
+    # Read weekly_avg_roi_pearl_agents.parquet
+    weekly_avg_roi_pearl_agents = hf_hub_download(
         repo_id="valory/Olas-predict-dataset",
-        filename="pearl_agents.parquet",
+        filename="weekly_avg_roi_pearl_agents.parquet",
         repo_type="dataset",
     )
-    df10 = pd.read_parquet(pearl_agents_df)
+    df10 = pd.read_parquet(weekly_avg_roi_pearl_agents)
     return df1, df2, df3, df4, df5, df6, df7, df8, df9, df10
 
 
@@ -177,7 +177,7 @@ def prepare_data():
         all_mech_calls,
         daa_qs_df,
         daa_pearl_df,
-        pearl_agents_df,
+        weekly_avg_roi_pearl_agents,
     ) = load_all_data()
     all_trades["creation_timestamp"] = all_trades["creation_timestamp"].dt.tz_convert(
         "UTC"
@@ -255,7 +255,7 @@ def prepare_data():
         all_mech_calls,
         daa_qs_df,
         daa_pearl_df,
-        pearl_agents_df,
+        weekly_avg_roi_pearl_agents,
     )
 
 
@@ -269,7 +269,7 @@ def prepare_data():
     all_mech_calls,
     daa_qs_df,
     daa_pearl_df,
-    pearl_agents_df,
+    weekly_avg_roi_pearl_agents,
 ) = prepare_data()
 
 retention_df = prepare_retention_dataset(
@@ -571,10 +571,11 @@ with demo:
             with gr.Row():
                 gr.Markdown("# 2-weeks rolling average ROI for Pearl agents")
             with gr.Row():
-                pearl_rolling_avg_plot = plot_rolling_average_roi(
-                    traders_data=traders_data,
-                    pearl_agents=pearl_agents_df,
-                )
+                # pearl_rolling_avg_plot = plot_rolling_average_roi(
+                #     traders_data=traders_data,
+                #     pearl_agents=weekly_avg_roi_pearl_agents,
+                # )
+                print("WIP")
             with gr.Row():
                 gr.Markdown("# Average weekly ROI for Pearl agents")
             with gr.Row():
@@ -583,11 +584,10 @@ with demo:
                 )
             with gr.Row():
                 weekly_avg_roi_plot = plot_weekly_average_roi(
-                    traders_data=traders_data,
-                    pearl_agents=pearl_agents_df,
+                    weekly_avg_roi_df=weekly_avg_roi_pearl_agents,
                 )
 
-        with gr.TabItem("🪝 Retention metrics (WIP)"):
+        with gr.TabItem("🪝 Retention metrics"):
             with gr.Row():
                 gr.Markdown("# Wow retention by trader type")
             with gr.Row():
@@ -705,7 +705,7 @@ with demo:
                     #     )
         with gr.TabItem("⚙️ Active traders"):
             with gr.Row():
-                gr.Markdown("# Active traders for all markets by trader categories")
+                gr.Markdown("# Active Pearl traders by trader categories")
             with gr.Row():
                 active_traders_plot = plot_active_traders(active_traders)
 
@@ -717,9 +717,7 @@ with demo:
                 )
 
             with gr.Row():
-                gr.Markdown(
-                    "# Active traders for Quickstart markets by trader categories"
-                )
+                gr.Markdown("# Active Quickstart traders by trader categories")
             with gr.Row():
                 active_traders_plot_qs = plot_active_traders(
                     active_traders, market_creator="quickstart"
@@ -742,7 +740,7 @@ with demo:
                 with gr.Column(scale=1):
                     interpretation = get_interpretation_text()
 
-        with gr.TabItem("💰 Money invested per trader type"):
+        with gr.TabItem("💰 Money invested per markets"):
             with gr.Row():
                 gr.Markdown("# Weekly total bet amount per trader type for all markets")
                 gr.Markdown("## Computed only for trader agents using the mech service")

tabs/agent_graphs.py

@@ -116,36 +116,17 @@ def get_weekly_average_roi(traders_data: pd.DataFrame) -> pd.DataFrame:
     return weekly_avg
 
 
-def plot_weekly_average_roi(
-    traders_data: pd.DataFrame, pearl_agents: pd.DataFrame
-) -> gr.Plot:
+def plot_weekly_average_roi(weekly_avg_roi_df: pd.DataFrame) -> gr.Plot:
     """Function to plot the weekly average of ROI for pearl agents"""
-    # Get the list of unique addresses from the daa_pearl_df
-    unique_addresses = pearl_agents["safe_address"].unique()
-
-    # Filter the weekly_roi_df to include only those addresses
-    filtered_traders_data = traders_data[
-        traders_data["trader_address"].isin(unique_addresses)
-    ].copy()
 
-    # create the date column
-    filtered_traders_data["creation_timestamp"] = pd.to_datetime(
-        filtered_traders_data["creation_timestamp"]
-    )
-    filtered_traders_data["creation_date"] = filtered_traders_data[
-        "creation_timestamp"
-    ].dt.date
-
-    # Get the weekly average ROI
-    weekly_avg_roi_df = get_weekly_average_roi(filtered_traders_data)
     print("Weekly average ROI DataFrame:")
     print(weekly_avg_roi_df.head())
 
     # Update the plot to use the correct column name 'weekly_avg_roi'
     fig = px.line(
         weekly_avg_roi_df,
-        x="creation_date",
-        y="roi",  # Changed from 'roi' to 'weekly_avg_roi'
+        x="week_start",
+        y="avg_weekly_roi",  # Changed from 'roi' to 'weekly_avg_roi'
     )
     fig.update_layout(
         xaxis_title="Week",