new tab for agent metrics and update of roi functions for agents

app.py

@@ -26,14 +26,14 @@ from tabs.trader_plots import (
     get_interpretation_text,
     plot_total_bet_amount,
     plot_active_traders,
-    plot_rolling_average_roi,
 )
+from tabs.agent_graphs import plot_rolling_average_dune, plot_rolling_average_roi
+
 from tabs.daily_graphs import (
     get_current_week_data,
     plot_daily_metrics,
     trader_daily_metric_choices,
     default_daily_metric,
-    plot_rolling_average_dune,
 )
 from scripts.utils import get_traders_family
 from tabs.market_plots import (
@@ -434,13 +434,7 @@ with demo:
                     inputs=trader_u_details_selector,
                     outputs=trader_u_markets_plot,
                 )
-            with gr.Row():
-                gr.Markdown("# 2-weeks rolling average ROI for Pearl traders")
-            with gr.Row():
-                pearl_rolling_avg_plot = plot_rolling_average_roi(
-                    weekly_roi_df=weekly_metrics_by_market_creator,
-                    market_creator="pearl",
-                )
+
         with gr.TabItem("📅 Daily metrics"):
             live_trades_current_week = get_current_week_data(trades_df=daily_info)
             if len(live_trades_current_week) > 0:
@@ -547,6 +541,29 @@ with demo:
                 inputs=[no_trader_live_details_selector, no_trader_live_details_plot],
                 outputs=[no_trader_live_details_plot],
             )
+        with gr.TabItem(" Agent metrics"):
+            with gr.Row():
+                gr.Markdown(" # Daily active Pearl agents")
+            with gr.Row():
+                rolling_avg_plot = plot_rolling_average_dune(
+                    daa_pearl_df,
+                )
+            with gr.Row():
+                gr.Markdown(" # Daily active Quickstart agents")
+            with gr.Row():
+                rolling_avg_plot = plot_rolling_average_dune(
+                    daa_qs_df,
+                )
+            with gr.Row():
+                gr.Markdown("# 2-weeks rolling average ROI for Pearl agents")
+            with gr.Row():
+                pearl_rolling_avg_plot = plot_rolling_average_roi(
+                    weekly_roi_df=weekly_metrics_by_market_creator,
+                    market_creator="pearl",
+                )
+            with gr.Row():
+                gr.Markdown("# Average weekly ROI for Pearl agents (WIP)")
+
         with gr.TabItem("🪝 Retention metrics (WIP)"):
             with gr.Row():
                 gr.Markdown("# Wow retention by trader type")
@@ -684,18 +701,6 @@ with demo:
                 active_traders_plot_qs = plot_active_traders(
                     active_traders, market_creator="quickstart"
                 )
-            with gr.Row():
-                gr.Markdown(" # Daily active agents in Pearl markets")
-            with gr.Row():
-                rolling_avg_plot = plot_rolling_average_dune(
-                    daa_pearl_df,
-                )
-            with gr.Row():
-                gr.Markdown(" # Daily active agents in QS markets")
-            with gr.Row():
-                rolling_avg_plot = plot_rolling_average_dune(
-                    daa_qs_df,
-                )
 
         with gr.TabItem("📉 Markets Kullback–Leibler divergence"):
             with gr.Row():

tabs/agent_graphs.py

@@ -0,0 +1,127 @@
+import pandas as pd
+import gradio as gr
+import gc
+import plotly.express as px
+
+
+def plot_rolling_average_dune(
+    daa_df: pd.DataFrame,
+) -> gr.Plot:
+    """Function to plot the rolling average of daily active traders"""
+
+    fig = px.bar(
+        daa_df,
+        x="tx_date",
+        y="seven_day_trailing_avg",
+    )
+    fig.update_layout(
+        xaxis_title="Date",
+        yaxis_title="7-day rolling average of DAA",
+    )
+
+    return gr.Plot(
+        value=fig,
+    )
+
+
+def plot_rolling_average(
+    daa_df: pd.DataFrame,
+    market_creator: str = None,
+) -> gr.Plot:
+    """Function to plot the rolling average of daily active traders by markets"""
+    if market_creator is not None:
+        filtered_traders_df = daa_df.loc[daa_df["market_creator"] == market_creator]
+        rolling_avg_df = get_sevenday_rolling_average(filtered_traders_df)
+    else:
+        rolling_avg_df = get_sevenday_rolling_average(daa_df)
+    print(rolling_avg_df.head())
+
+    # Ensure 'creation_date' is a column, not an index
+    if "tx_date" not in rolling_avg_df.columns:
+        rolling_avg_df = rolling_avg_df.reset_index()
+
+    fig = px.bar(
+        rolling_avg_df,
+        x="tx_date",
+        y="rolling_avg_traders",
+    )
+    fig.update_layout(
+        xaxis_title="Date",
+        yaxis_title="7-day rolling average of DAA",
+    )
+
+    return gr.Plot(
+        value=fig,
+    )
+
+
+def get_sevenday_rolling_average(daa_df: pd.DataFrame) -> pd.DataFrame:
+    """Function to get the 7-day rolling average of the number of unique
+    trader_address"""
+    # Create a local copy of the dataframe
+    local_df = daa_df.copy()
+
+    # Sort the dataframe by date
+    local_df = local_df.sort_values(by="tx_date").set_index("tx_date")
+
+    # Group by market_creator and calculate rolling average of unique trader_address
+    rolling_avg = (
+        local_df.resample("D")["trader_address"]
+        .nunique()
+        .rolling(window=7)
+        .mean()
+        .reset_index()
+    )
+    rolling_avg.rename(columns={"trader_address": "rolling_avg_traders"}, inplace=True)
+    return rolling_avg
+
+
+def plot_rolling_average_roi(
+    weekly_roi_df: pd.DataFrame, daa_pearl_df: pd.DataFrame
+) -> gr.Plot:
+    """Function to plot the rolling average of ROI for pearl agents"""
+    # Get the list of unique addresses from the daa_pearl_df
+    unique_addresses = daa_pearl_df["trader_address"].unique()
+    # Filter the weekly_roi_df to include only those addresses
+    filtered_weekly_roi_df = weekly_roi_df[
+        weekly_roi_df["trader_address"].isin(unique_addresses)
+    ]
+    # Get the 2-week rolling average of ROI
+    rolling_avg_roi_df = get_twoweeks_rolling_average_roi(filtered_weekly_roi_df)
+    print(rolling_avg_roi_df.head())
+    # Ensure 'month_year_week' is a column, not an index
+    if "month_year_week" not in rolling_avg_roi_df.columns:
+        rolling_avg_roi_df = rolling_avg_roi_df.reset_index()
+    fig = px.bar(
+        rolling_avg_roi_df,
+        x="month_year_week",
+        y="rolling_avg_roi",
+    )
+    fig.update_layout(
+        xaxis_title="Week",
+        yaxis_title="2-week rolling average of ROI at the trader level",
+    )
+
+    return gr.Plot(
+        value=fig,
+    )
+
+
+def get_twoweeks_rolling_average_roi(weekly_roi_df: pd.DataFrame) -> pd.DataFrame:
+    """Function to get the 2-week rolling average of the ROI by market_creator and total"""
+    # Create a local copy of the dataframe
+    local_df = weekly_roi_df.copy()
+
+    # Convert string dates to datetime
+    local_df["month_year_week"] = pd.to_datetime(
+        local_df["month_year_week"], format="%b-%d-%Y"
+    )
+    # Sort the dataframe by date
+    local_df = local_df.sort_values(by="month_year_week").set_index("month_year_week")
+
+    # Group by market_creator and calculate rolling average of unique trader_address
+    trader_rolling_avg_roi = (
+        local_df.resample("W")["roi"].mean().rolling(window=2).mean().reset_index()
+    )
+    trader_rolling_avg_roi.rename(columns={"roi": "rolling_avg_roi"}, inplace=True)
+    return trader_rolling_avg_roi

tabs/daily_graphs.py

@@ -229,75 +229,3 @@ def plot_daily_metrics_v2(
 
     # Update y-axes to have the same range
     fig.update_yaxes(matches="y")
-
-
-def get_sevenday_rolling_average(daa_df: pd.DataFrame) -> pd.DataFrame:
-    """Function to get the 7-day rolling average of the number of unique
-    trader_address"""
-    # Create a local copy of the dataframe
-    local_df = daa_df.copy()
-
-    # Sort the dataframe by date
-    local_df = local_df.sort_values(by="tx_date").set_index("tx_date")
-
-    # Group by market_creator and calculate rolling average of unique trader_address
-    rolling_avg = (
-        local_df.resample("D")["trader_address"]
-        .nunique()
-        .rolling(window=7)
-        .mean()
-        .reset_index()
-    )
-    rolling_avg.rename(columns={"trader_address": "rolling_avg_traders"}, inplace=True)
-    return rolling_avg
-
-
-def plot_rolling_average(
-    daa_df: pd.DataFrame,
-    market_creator: str = None,
-) -> gr.Plot:
-    """Function to plot the rolling average of daily active traders by markets"""
-    if market_creator is not None:
-        filtered_traders_df = daa_df.loc[daa_df["market_creator"] == market_creator]
-        rolling_avg_df = get_sevenday_rolling_average(filtered_traders_df)
-    else:
-        rolling_avg_df = get_sevenday_rolling_average(daa_df)
-    print(rolling_avg_df.head())
-
-    # Ensure 'creation_date' is a column, not an index
-    if "tx_date" not in rolling_avg_df.columns:
-        rolling_avg_df = rolling_avg_df.reset_index()
-
-    fig = px.bar(
-        rolling_avg_df,
-        x="tx_date",
-        y="rolling_avg_traders",
-    )
-    fig.update_layout(
-        xaxis_title="Date",
-        yaxis_title="7-day rolling average of DAA",
-    )
-
-    return gr.Plot(
-        value=fig,
-    )
-
-
-def plot_rolling_average_dune(
-    daa_df: pd.DataFrame,
-) -> gr.Plot:
-    """Function to plot the rolling average of daily active traders"""
-
-    fig = px.bar(
-        daa_df,
-        x="tx_date",
-        y="seven_day_trailing_avg",
-    )
-    fig.update_layout(
-        xaxis_title="Date",
-        yaxis_title="7-day rolling average of DAA",
-    )
-
-    return gr.Plot(
-        value=fig,
-    )

tabs/trader_plots.py

@@ -348,104 +348,6 @@ def plot_total_bet_amount(
     )
 
 
-def get_sevenday_rolling_average_by_market_creator(
-    active_traders_df: pd.DataFrame,
-) -> pd.DataFrame:
-    """Function to get the 7-day rolling average of the number of unique trader_address by market_creator and total"""
-    # Create a local copy of the dataframe
-    local_df = active_traders_df.copy()
-
-    # Convert string dates to datetime
-    local_df["creation_date"] = pd.to_datetime(
-        local_df["creation_date"], format="%b-%d-%Y"
-    )
-    # Sort the dataframe by date
-    local_df = local_df.sort_values(by="creation_date")
-
-    # Group by market_creator and creation_date, count unique traders
-    daily_traders = (
-        local_df.groupby(["market_creator", "creation_date"])["trader_address"]
-        .nunique()
-        .reset_index()
-    )
-
-    # Calculate rolling average for each market_creator
-    rolling_avg_by_market = daily_traders.copy()
-    rolling_avg_by_market["rolling_avg_traders"] = rolling_avg_by_market.groupby(
-        "market_creator"
-    )["trader_address"].transform(lambda x: x.rolling(window=7).mean())
-
-    # Calculate the total rolling average across all market_creators
-    all_markets = daily_traders.copy()
-    all_markets["market_creator"] = "all"
-    all_markets = (
-        all_markets.groupby(["market_creator", "creation_date"])["trader_address"]
-        .sum()
-        .reset_index()
-    )
-
-    all_markets["rolling_avg_traders"] = (
-        all_markets["trader_address"].rolling(window=7).mean()
-    )
-
-    # Combine both results
-    combined_rolling_avg = pd.concat(
-        [rolling_avg_by_market, all_markets], ignore_index=True
-    )
-
-    return combined_rolling_avg
-
-
-def get_twoweeks_rolling_average_roi(weekly_roi_df: pd.DataFrame) -> pd.DataFrame:
-    """Function to get the 2-week rolling average of the ROI by market_creator and total"""
-    # Create a local copy of the dataframe
-    local_df = weekly_roi_df.copy()
-
-    # Convert string dates to datetime
-    local_df["month_year_week"] = pd.to_datetime(
-        local_df["month_year_week"], format="%b-%d-%Y"
-    )
-    # Sort the dataframe by date
-    local_df = local_df.sort_values(by="month_year_week").set_index("month_year_week")
-
-    # Group by market_creator and calculate rolling average of unique trader_address
-    trader_rolling_avg_roi = (
-        local_df.resample("W")["roi"].mean().rolling(window=2).mean().reset_index()
-    )
-    trader_rolling_avg_roi.rename(columns={"roi": "rolling_avg_roi"}, inplace=True)
-    return trader_rolling_avg_roi
-
-
-def plot_rolling_average_roi(
-    weekly_roi_df: pd.DataFrame, market_creator: str
-) -> gr.Plot:
-    """Function to plot the rolling average of ROI for traders in a given market"""
-    if market_creator != "all":
-        filtered_roi_df = weekly_roi_df.loc[
-            weekly_roi_df["market_creator"] == market_creator
-        ]
-        rolling_avg_roi_df = get_twoweeks_rolling_average_roi(filtered_roi_df)
-    else:
-        rolling_avg_roi_df = get_twoweeks_rolling_average_roi(weekly_roi_df)
-    print(rolling_avg_roi_df.head())
-    # Ensure 'month_year_week' is a column, not an index
-    if "month_year_week" not in rolling_avg_roi_df.columns:
-        rolling_avg_roi_df = rolling_avg_roi_df.reset_index()
-    fig = px.bar(
-        rolling_avg_roi_df,
-        x="month_year_week",
-        y="rolling_avg_roi",
-    )
-    fig.update_layout(
-        xaxis_title="Week",
-        yaxis_title="4-week rolling average of ROI at the trader level",
-    )
-
-    return gr.Plot(
-        value=fig,
-    )
-
-
 def plot_active_traders(
     active_traders_data: pd.DataFrame,
     market_creator: str = None,