new aggregation level and fixed metrics text

app.py

@@ -25,8 +25,11 @@ from tabs.daily_graphs import (
     default_daily_metric,
 )
 from scripts.utils import get_traders_family
-from scripts.trades_volume_per_market import plot_weekly_trades_volume_by_trader_family
-from tabs.market_plots import plot_kl_div_per_market, plot_total_bet_amount
+from tabs.market_plots import (
+    plot_kl_div_per_market,
+    plot_total_bet_amount,
+    plot_nr_trades_per_trader_per_market,
+)
 
 
 def get_logger():
@@ -148,7 +151,7 @@ with demo:
     )
 
     with gr.Tabs():
-        with gr.TabItem("🔥 Weekly metrics"):
+        with gr.TabItem("🔥 Weekly profitability metrics"):
             with gr.Row():
                 gr.Markdown("# Weekly metrics of all traders")
             with gr.Row():
@@ -180,7 +183,7 @@ with demo:
             )
             # Agentic traders graph
             with gr.Row():
-                gr.Markdown("# Weekly metrics of trader Agents")
+                gr.Markdown("# Weekly metrics of trader Agents 🤖")
             with gr.Row():
                 trader_a_details_selector = gr.Dropdown(
                     label="Select a weekly trader metric",
@@ -239,7 +242,7 @@ with demo:
                 inputs=trader_na_details_selector,
                 outputs=na_trader_markets_plot,
             )
-        with gr.TabItem("📅 Daily trades dashboard (WIP)"):
+        with gr.TabItem("📅 Daily metrics (WIP)"):
             current_week_trades = get_current_week_data(trades_df=trader_agents_data)
             live_trades_current_week = get_current_week_data(trades_df=daily_info)
             if len(current_week_trades) > 0:
@@ -247,6 +250,7 @@ with demo:
                     compute_daily_metrics_by_market_creator(current_week_trades)
                 )
             else:
+                print("No profitability info about the current week")
                 daily_prof_metrics_by_market_creator = pd.DataFrame()
             live_metrics_by_market_creator = compute_daily_metrics_by_market_creator(
                 live_trades_current_week, trader_filter=None, live_metrics=True
@@ -269,7 +273,7 @@ with demo:
                         trades_df=live_metrics_by_market_creator,
                     )
                 with gr.Column(scale=1):
-                    trade_details_text = get_metrics_text()
+                    trade_details_text = get_metrics_text(daily=True)
 
             def update_trade_live_details(trade_detail, trade_live_details_plot):
                 new_a_plot = plot_daily_metrics(
@@ -300,7 +304,7 @@ with demo:
                             trades_df=daily_prof_metrics_by_market_creator,
                         )
                     with gr.Column(scale=1):
-                        trader_details_text = get_metrics_text()
+                        trader_details_text = get_metrics_text(daily=True)
 
                 def update_trader_daily_details(
                     trade_detail, trader_daily_details_plot
@@ -342,16 +346,34 @@ with demo:
                 )
             with gr.Row():
                 total_bet_amount = plot_total_bet_amount(trader_agents_data)
-
+        with gr.TabItem("💹 Metrics at the market level"):
             with gr.Row():
                 gr.Markdown(
-                    "# Weekly volume of trades at each market per trader family"
+                    "# Weekly nr of trades per trader per market for all traders"
                 )
 
             with gr.Row():
-                trades_volume_plot = plot_weekly_trades_volume_by_trader_family(
+                trades_volume_plot = plot_nr_trades_per_trader_per_market(
                     trader_agents_data
                 )
+            with gr.Row():
+                gr.Markdown(
+                    "# Weekly nr of trades per trader per market for trader Agents 🤖"
+                )
+
+            with gr.Row():
+                trades_volume_plot = plot_nr_trades_per_trader_per_market(
+                    trader_agents_data, trader_filter="agent"
+                )
+            with gr.Row():
+                gr.Markdown(
+                    "# Weekly nr of trades per trader per market for non-Agent traders"
+                )
+
+            with gr.Row():
+                trades_volume_plot = plot_nr_trades_per_trader_per_market(
+                    trader_agents_data, trader_filter="non_agent"
+                )
 
         with gr.TabItem("🎖️Weekly winning trades % per trader"):
             with gr.Row():

data/daily_info.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f41fdb6fc36cf8cb28980bca049de6b4fa986a9800176e269a5259a7e744c514
-size 251792
+oid sha256:3efc44de285c7f330661d31354843e5c95f89323a04d32774971576bdf049dba
+size 390502

scripts/trades_volume_per_market.py

@@ -1,38 +0,0 @@
-import pandas as pd
-import gradio as gr
-import plotly.express as px
-
-
-def plot_weekly_trades_volume_by_trader_family(
-    trader_agents_data: pd.DataFrame,
-) -> gr.Plot:
-    """Function to compute the metrics at the trader level per week
-    and with different categories by market creator"""
-
-    weekly_trades_volume = (
-        trader_agents_data.groupby(
-            ["month_year_week", "title", "trader_family"], sort=False
-        )["trader_address"]
-        .size()
-        .reset_index(name="trades")
-    )
-
-    fig = px.box(
-        weekly_trades_volume,
-        x="month_year_week",
-        y="trades",
-        color="trader_family",
-        color_discrete_sequence=["darkviolet", "goldenrod", "gray"],
-        category_orders={
-            "trader_family": ["pearl_agent", "quickstart_agent", "non_agent"]
-        },
-    )
-
-    fig.update_layout(
-        xaxis_title="Week",
-        yaxis_title="Weekly trades volume in each market per trader family type",
-        legend=dict(yanchor="top", y=0.5),
-    )
-    # fig.update_layout(width=WIDTH, height=HEIGHT)
-    fig.update_xaxes(tickformat="%b %d\n%Y")
-    return gr.Plot(value=fig)

tabs/daily_graphs.py

@@ -9,6 +9,14 @@ from tqdm import tqdm
 
 trade_daily_metric_choices = ["mech calls", "collateral amount", "nr_trades"]
 default_daily_metric = "collateral amount"
+color_mapping = [
+    "darkviolet",
+    "purple",
+    "goldenrod",
+    "darkgoldenrod",
+    "green",
+    "darkgreen",
+]
 
 
 def plot_daily_trades(trades_df: pd.DataFrame) -> gr.Plot:
@@ -103,14 +111,6 @@ def plot_daily_metrics(
         trades_filtered = trades_df.loc[trades_df["staking"] == "non_agent"]
     else:
         trades_filtered = trades_df
-    color_mapping = [
-        "darkviolet",
-        "purple",
-        "goldenrod",
-        "darkgoldenrod",
-        "green",
-        "darkgreen",
-    ]
 
     # Create binary staking category
     trades_filtered["trader_type"] = trades_filtered["staking"].apply(

tabs/market_plots.py

@@ -5,6 +5,7 @@ import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import matplotlib.pyplot as plt
 import seaborn as sns
+from tabs.daily_graphs import color_mapping
 
 
 def plot_kl_div_per_market(closed_markets: pd.DataFrame) -> gr.Plot:
@@ -146,3 +147,65 @@ def plot_total_bet_amount(trades_df: pd.DataFrame) -> gr.Plot:
     return gr.Plot(
         value=fig,
     )
+
+
+def plot_nr_trades_per_trader_per_market(
+    traders_data: pd.DataFrame, trader_filter: str = "all"
+) -> gr.Plot:
+    """Function to paint the plot with the metric nr_trades_per_market by trader type and market creator"""
+
+    traders_all = traders_data.copy(deep=True)
+    traders_all["market_creator"] = "all"
+
+    # merging both dataframes
+    final_traders = pd.concat([traders_all, traders_data], ignore_index=True)
+    final_traders = final_traders.sort_values(by="creation_date", ascending=True)
+
+    # Create binary staking category
+    final_traders["trader_type"] = final_traders["staking"].apply(
+        lambda x: "non_agent" if x == "non_agent" else "agent"
+    )
+    final_traders["trader_market"] = final_traders.apply(
+        lambda x: (x["trader_type"], x["market_creator"]), axis=1
+    )
+    color_discrete_sequence = ["purple", "goldenrod", "darkgreen"]
+    if trader_filter == "agent":
+        color_discrete_sequence = ["darkviolet", "goldenrod", "green"]
+        final_traders = final_traders.loc[final_traders["trader_type"] == "agent"]
+    elif trader_filter == "non_agent":
+        final_traders = final_traders.loc[final_traders["trader_type"] != "agent"]
+
+    fig = px.box(
+        final_traders,
+        x="month_year_week",
+        y="nr_trades_per_market",
+        color="market_creator",
+        color_discrete_sequence=color_discrete_sequence,
+        category_orders={
+            "market_creator": ["pearl", "quickstart", "all"],
+            "trader_market": [
+                ("agent", "pearl"),
+                ("non_agent", "pearl"),
+                ("agent", "quickstart"),
+                ("non_agent", "quickstart"),
+                ("agent", "all"),
+                ("non_agent", "all"),
+            ],
+        },
+        # facet_col="trader_type",
+    )
+    fig.update_traces(boxmean=True)
+    fig.update_layout(
+        xaxis_title="Week",
+        yaxis_title="Nr trades per trader per market",
+        legend=dict(yanchor="top", y=0.5),
+        width=1000,  # Adjusted for better fit on laptop screens
+        height=600,  # Adjusted for better fit on laptop screens
+    )
+    # for axis in fig.layout:
+    #     if axis.startswith("xaxis"):
+    #         fig.layout[axis].update(title="Week")
+    fig.update_xaxes(tickformat="%b %d\n%Y")
+    return gr.Plot(
+        value=fig,
+    )

tabs/trader_plots.py

@@ -13,7 +13,7 @@ trader_metric_choices = [
 default_trader_metric = "ROI"
 
 
-def get_metrics_text() -> gr.Markdown:
+def get_metrics_text(daily: bool = False) -> gr.Markdown:
     metric_text = """ 
         ## Metrics at the graph
         These metrics are computed weekly. The statistical measures are:
@@ -21,7 +21,14 @@ def get_metrics_text() -> gr.Markdown:
         * the upper and lower fences to delimit possible outliers
         * the average values as the dotted lines
         """
-
+    if daily:
+        metric_text = """ 
+            ## Metrics at the graph
+            These metrics are computed daily. The statistical measures are:
+            * min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
+            * the upper and lower fences to delimit possible outliers
+            * the average values as the dotted lines
+            """
     return gr.Markdown(metric_text)
 
 
@@ -144,190 +151,6 @@ def plot_trader_daily_metrics_by_market_creator(
     )
 
 
-def plot_median_roi_by_creation_date(traders_df: pd.DataFrame) -> gr.Plot:
-    traders_df["creation_date"] = traders_df["creation_timestamp"].dt.date
-
-    traders_all = traders_df.copy(deep=True)
-    traders_all["market_creator"] = "all"
-
-    # merging both dataframes
-    final_traders = pd.concat([traders_all, traders_df], ignore_index=True)
-    final_traders = final_traders.sort_values(by="creation_date", ascending=True)
-    roi_daily_metrics = (
-        final_traders.groupby(
-            ["creation_date", "market_creator", "trader_address"], sort=False
-        )
-        .agg(
-            median_roi=("roi", "median"),
-            mean_roi=("roi", "mean"),
-            total_trades=("roi", "count"),
-        )
-        .reset_index()
-    )
-    # Create the scatter plot with facets for each market_creator
-    fig = px.scatter(
-        roi_daily_metrics,
-        x="creation_date",
-        y="median_roi",
-        facet_col="market_creator",
-        color="market_creator",
-        color_discrete_map={
-            "pearl": "purple",
-            "quickstart": "goldenrod",
-            "all": "darkgreen",
-        },
-        title="Median ROI Over Time by Market Creator",
-        labels={
-            "creation_date": "Creation Date",
-            "median_roi": "Median ROI (%)",
-            "market_creator": "Market Creator",
-        },
-        hover_data={
-            "creation_date": "|%B %d, %Y",  # Custom date format in hover
-            "median_roi": True,
-            "mean_roi": True,
-            "total_trades": True,
-        },
-        category_orders={"market_creator": ["pearl", "quickstart", "all"]},
-        # trendline=None,  # Ensure no trendlines are added
-    )
-
-    # Customize the layout for better aesthetics
-    fig.update_layout(
-        template="plotly_white",
-        hovermode="closest",
-        showlegend=False,  # Disable the legend as each facet has its own context
-    )
-
-    # Update each subplot's x-axis to share the same range
-    fig.update_xaxes(matches="x")  # Link x-axes across facets
-    fig.update_yaxes(matches="y")  # Link y-axes across facets
-
-    # Add a vertical dashed line in dark red at the specified date
-    vline_date = "2024-09-29"
-    vline_datetime = pd.to_datetime(vline_date, format="%Y-%m-%d")
-    fig.add_vline(
-        x=vline_datetime,
-        line_dash="dash",
-        line_color="darkred",
-    )
-    return gr.Plot(
-        value=fig,
-    )
-
-
-import plotly.express as px
-
-
-def create_median_roi_plot(roi_daily_metrics):
-    """
-    Creates a Plotly scatter plot for median ROI over time, colored by market_creator.
-
-    Parameters:
-    - roi_daily_metrics (pd.DataFrame): Aggregated ROI metrics with columns:
-        ['creation_date', 'market_creator', 'trader_address', 'median_roi', 'mean_roi', 'total_trades']
-
-    Returns:
-    - fig (plotly.graph_objs._figure.Figure): The Plotly figure object.
-    """
-    # Ensure 'creation_date' is in datetime format
-    roi_daily_metrics["creation_date"] = pd.to_datetime(
-        roi_daily_metrics["creation_date"]
-    )
-
-    # Create the line plot with scatter markers
-    fig = px.line(
-        roi_daily_metrics,
-        x="creation_date",
-        y="median_roi",
-        color="market_creator",
-        markers=True,  # Add markers to lines
-        title="Median ROI Over Time by Market Creator",
-        labels={
-            "creation_date": "Creation Date",
-            "median_roi": "Median ROI (%)",
-            "market_creator": "Market Creator",
-        },
-        hover_data={
-            "creation_date": "|%B %d, %Y",  # Custom date format in hover
-            "median_roi": True,
-            "mean_roi": True,
-            "total_trades": True,
-        },
-    )
-
-    # Customize the layout for better aesthetics
-    fig.update_layout(
-        xaxis_title="Creation Date",
-        yaxis_title="Median ROI (%)",
-        legend_title="Market Creator",
-        template="plotly_white",
-        hovermode="x unified",
-    )
-
-    # Optional: Add vertical lines for specific events (e.g., "multibet release")
-    # Example:
-    # fig.add_vline(
-    #     x=pd.to_datetime("2023-01-02"),
-    #     line_dash="dash",
-    #     line_color="red",
-    #     annotation_text="Multibet Release",
-    #     annotation_position="top left",
-    #     annotation=dict(
-    #         bgcolor="white",
-    #         font_size=12,
-    #         font_color="red"
-    #     )
-    # )
-
-    return fig
-
-
-def plot_trader_metrics_by_trader_type(metric_name: str, traders_df: pd.DataFrame):
-    """Plots the weekly trader metrics."""
-
-    if metric_name == "mech calls":
-        metric_name = "mech_calls"
-        column_name = "nr_mech_calls"
-        yaxis_title = "Total nr of mech calls per trader"
-    elif metric_name == "ROI":
-        column_name = "roi"
-        yaxis_title = "Total ROI (net profit/cost)"
-    elif metric_name == "bet amount":
-        metric_name = "bet_amount"
-        column_name = metric_name
-        yaxis_title = "Total bet amount per trader (xDAI)"
-    elif metric_name == "net earnings":
-        metric_name = "net_earnings"
-        column_name = metric_name
-        yaxis_title = "Total net profit per trader (xDAI)"
-    else:  # earnings
-        column_name = metric_name
-        yaxis_title = "Total gross profit per trader (xDAI)"
-
-    traders_filtered = traders_df[["month_year_week", "trader_type", column_name]]
-
-    fig = px.box(
-        traders_filtered,
-        x="month_year_week",
-        y=column_name,
-        color="trader_type",
-        color_discrete_sequence=["gray", "orange", "darkblue"],
-        category_orders={"trader_type": ["singlebet", "multibet", "all"]},
-    )
-    fig.update_traces(boxmean=True)
-    fig.update_layout(
-        xaxis_title="Week",
-        yaxis_title=yaxis_title,
-        legend=dict(yanchor="top", y=0.5),
-    )
-    fig.update_xaxes(tickformat="%b %d\n%Y")
-
-    return gr.Plot(
-        value=fig,
-    )
-
-
 def plot_winning_metric_per_trader(traders_winning_df: pd.DataFrame) -> gr.Plot:
     fig = px.box(
         traders_winning_df,