new nr trades and total bet amount per trader and market

app.py

@@ -27,8 +27,8 @@ from tabs.daily_graphs import (
 from scripts.utils import get_traders_family
 from tabs.market_plots import (
     plot_kl_div_per_market,
-    plot_total_bet_amount,
     plot_nr_trades_per_trader_per_market,
+    plot_total_bet_amount_per_trader_per_market,
 )
 
 
@@ -151,7 +151,7 @@ with demo:
     )
 
     with gr.Tabs():
-        with gr.TabItem("🔥 Weekly profitability metrics"):
+        with gr.TabItem("🔥 Weekly metrics"):
             with gr.Row():
                 gr.Markdown("# Weekly metrics of all traders")
             with gr.Row():
@@ -389,7 +389,7 @@ with demo:
             # else:
             #     gr.Markdown("Data not available yet")
 
-        with gr.TabItem("📉Closed Markets Kullback–Leibler divergence"):
+        with gr.TabItem("📉 Markets Kullback–Leibler divergence"):
             with gr.Row():
                 gr.Markdown(
                     "# Weekly Market Prediction Accuracy for Closed Markets (Kullback-Leibler Divergence)"
@@ -406,13 +406,7 @@ with demo:
                 with gr.Column(scale=1):
                     interpretation = get_interpretation_text()
 
-            with gr.Row():
-                gr.Markdown(
-                    "# Weekly total bet amount by market creator and trader type"
-                )
-            with gr.Row():
-                total_bet_amount = plot_total_bet_amount(trader_agents_data)
-        with gr.TabItem("💹 Metrics at the market level"):
+        with gr.TabItem("📮 Number of trades per market"):
             with gr.Row():
                 gr.Markdown(
                     "# Weekly nr of trades per trader per market for all traders"
@@ -440,6 +434,29 @@ with demo:
                 trades_volume_plot = plot_nr_trades_per_trader_per_market(
                     trader_agents_data, trader_filter="non_agent"
                 )
+        with gr.TabItem("💰 Money invested"):
+            with gr.Row():
+                gr.Markdown("# Weekly total bet amount by market for all traders")
+            with gr.Row():
+                total_bet_amount = plot_total_bet_amount_per_trader_per_market(
+                    trader_agents_data
+                )
+
+            with gr.Row():
+                gr.Markdown("# Weekly total bet amount by market for traders Agents 🤖")
+            with gr.Row():
+                a_trader_total_bet_amount = plot_total_bet_amount_per_trader_per_market(
+                    trader_agents_data, trader_filter="agent"
+                )
+
+            with gr.Row():
+                gr.Markdown("# Weekly total bet amount by market for Non-agent traders")
+            with gr.Row():
+                na_trader_total_bet_amount = (
+                    plot_total_bet_amount_per_trader_per_market(
+                        trader_agents_data, trader_filter="non_agent"
+                    )
+                )
 
         with gr.TabItem("🎖️Weekly winning trades % per trader"):
             with gr.Row():

tabs/market_plots.py

@@ -149,6 +149,76 @@ def plot_total_bet_amount(trades_df: pd.DataFrame) -> gr.Plot:
     )
 
 
+def plot_total_bet_amount_per_trader_per_market(
+    trades_df: pd.DataFrame, trader_filter: str = "all"
+) -> gr.Plot:
+    """Plots the total bet amount per trader per market."""
+    traders_all = trades_df.copy(deep=True)
+    traders_all["market_creator"] = "all"
+
+    # merging both dataframes
+    final_traders = pd.concat([traders_all, trades_df], 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"]
+
+    total_bet_amount = (
+        final_traders.groupby(
+            ["month_year_week", "market_creator", "trader_address", "title"],
+            sort=False,
+        )["collateral_amount"]
+        .sum()
+        .reset_index(name="total_bet_amount")
+    )
+
+    fig = px.box(
+        total_bet_amount,
+        x="month_year_week",
+        y="total_bet_amount",
+        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_layout(
+        xaxis_title="Week",
+        yaxis_title="Weekly total bet amount 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,
+    )
+
+
 def plot_nr_trades_per_trader_per_market(
     traders_data: pd.DataFrame, trader_filter: str = "all"
 ) -> gr.Plot: