new money invested tabs and graphs

app.py

@@ -17,6 +17,7 @@ from tabs.trader_plots import (
     get_metrics_text,
     plot_winning_metric_per_trader,
     get_interpretation_text,
+    plot_total_bet_amount,
 )
 from tabs.daily_graphs import (
     get_current_week_data,
@@ -27,7 +28,6 @@ from tabs.daily_graphs import (
 from scripts.utils import get_traders_family
 from tabs.market_plots import (
     plot_kl_div_per_market,
-    plot_nr_trades_per_trader_per_market,
     plot_total_bet_amount_per_trader_per_market,
 )
 
@@ -406,56 +406,49 @@ with demo:
                 with gr.Column(scale=1):
                     interpretation = get_interpretation_text()
 
-        with gr.TabItem("📮 Number of trades per market"):
+        with gr.TabItem("💰 Money invested per trader"):
             with gr.Row():
-                gr.Markdown(
-                    "# Weekly nr of trades per trader per market for all traders"
-                )
-
+                gr.Markdown("# Weekly total bet amount per trader for all traders")
             with gr.Row():
-                trades_volume_plot = plot_nr_trades_per_trader_per_market(
-                    trader_agents_data
-                )
+                total_bet_amount = plot_total_bet_amount(trader_agents_data)
+
             with gr.Row():
                 gr.Markdown(
-                    "# Weekly nr of trades per trader per market for trader Agents 🤖"
+                    "# Weekly total bet amount per trader for traders Agents 🤖"
                 )
-
             with gr.Row():
-                trades_volume_plot = plot_nr_trades_per_trader_per_market(
+                a_trader_total_bet_amount = plot_total_bet_amount(
                     trader_agents_data, trader_filter="agent"
                 )
+
             with gr.Row():
                 gr.Markdown(
-                    "# Weekly nr of trades per trader per market for non-Agent traders"
+                    "# Weekly total bet amount per trader for Non-agent traders"
                 )
-
             with gr.Row():
-                trades_volume_plot = plot_nr_trades_per_trader_per_market(
+                na_trader_total_bet_amount = plot_total_bet_amount(
                     trader_agents_data, trader_filter="non_agent"
                 )
-        with gr.TabItem("💰 Money invested"):
+        with gr.TabItem("💰 Money invested per market"):
             with gr.Row():
-                gr.Markdown("# Weekly total bet amount by market for all traders")
+                gr.Markdown("# Weekly bet amounts per market for all traders")
             with gr.Row():
-                total_bet_amount = plot_total_bet_amount_per_trader_per_market(
+                bet_amounts = 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 🤖")
+                gr.Markdown("# Weekly bet amounts per market for traders Agents 🤖")
             with gr.Row():
-                a_trader_total_bet_amount = plot_total_bet_amount_per_trader_per_market(
+                a_trader_bet_amounts = 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")
+                gr.Markdown("# Weekly bet amounts per 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"
-                    )
+                na_trader_bet_amounts = plot_total_bet_amount_per_trader_per_market(
+                    trader_agents_data, trader_filter="non_agent"
                 )
 
         with gr.TabItem("🎖️Weekly winning trades % per trader"):

tabs/market_plots.py

@@ -7,6 +7,15 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 from tabs.daily_graphs import color_mapping
 
+color_mapping = [
+    "darkviolet",
+    "purple",
+    "goldenrod",
+    "darkgoldenrod",
+    "green",
+    "darkgreen",
+]
+
 
 def plot_kl_div_per_market(closed_markets: pd.DataFrame) -> gr.Plot:
 
@@ -88,67 +97,6 @@ def plot_kl_div_with_off_by(closed_markets: pd.DataFrame) -> gr.Plot:
     )
 
 
-def plot_total_bet_amount(trades_df: pd.DataFrame) -> gr.Plot:
-    """Plots the trade metrics."""
-    # Create binary staking category
-    trades_df["trader_type"] = trades_df["staking"].apply(
-        lambda x: "non_agent" if x == "non_agent" else "agent"
-    )
-
-    total_bet_amount = (
-        trades_df.groupby(
-            ["month_year_week", "market_creator", "trader_type"], sort=False
-        )["collateral_amount"]
-        .sum()
-        .reset_index(name="total_bet_amount")
-    )
-    color_mapping = [
-        "darkviolet",
-        "purple",
-        "goldenrod",
-        "darkgoldenrod",
-        "green",
-        "darkgreen",
-    ]
-    total_bet_amount["trader_market"] = total_bet_amount.apply(
-        lambda x: (x["trader_type"], x["market_creator"]), axis=1
-    )
-
-    fig = px.bar(
-        total_bet_amount,
-        x="month_year_week",
-        y="total_bet_amount",
-        color="trader_market",
-        color_discrete_sequence=color_mapping,
-        category_orders={
-            "market_creator": ["pearl", "quickstart", "all"],
-            "trader_market": [
-                ("agent", "pearl"),
-                ("non_agent", "pearl"),
-                ("agent", "quickstart"),
-                ("non_agent", "quickstart"),
-                ("agent", "all"),
-                ("non_agent", "all"),
-            ],
-        },
-        barmode="group",
-        facet_col="market_creator",
-    )
-
-    fig.update_layout(
-        xaxis_title="Week",
-        yaxis_title="Weekly total bet amount",
-        legend=dict(yanchor="top", y=0.5),
-    )
-    for axis in fig.layout:
-        if axis.startswith("xaxis"):
-            fig.layout[axis].update(title="Week")
-    fig.update_xaxes(tickformat="%b %d")
-    return gr.Plot(
-        value=fig,
-    )
-
-
 def plot_total_bet_amount_per_trader_per_market(
     trades_df: pd.DataFrame, trader_filter: str = "all"
 ) -> gr.Plot:
@@ -202,10 +150,10 @@ def plot_total_bet_amount_per_trader_per_market(
         },
         # facet_col="trader_type",
     )
-
+    fig.update_traces(boxmean=True)
     fig.update_layout(
         xaxis_title="Week",
-        yaxis_title="Weekly total bet amount per trader per market",
+        yaxis_title="Weekly bet amounts 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

tabs/trader_plots.py

@@ -173,3 +173,60 @@ def plot_winning_metric_per_trader(traders_winning_df: pd.DataFrame) -> gr.Plot:
     return gr.Plot(
         value=fig,
     )
+
+
+def plot_total_bet_amount(
+    trades_df: pd.DataFrame, trader_filter: str = "all"
+) -> gr.Plot:
+    """Plots the trade metrics."""
+    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"
+    )
+
+    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"], 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"],
+        },
+        # barmode="group",
+        # facet_col="market_creator",
+    )
+    fig.update_traces(boxmean=True)
+    fig.update_layout(
+        xaxis_title="Week",
+        yaxis_title="Weekly total bet amount",
+        legend=dict(yanchor="top", y=0.5),
+    )
+    # for axis in fig.layout:
+    #     if axis.startswith("xaxis"):
+    #         fig.layout[axis].update(title="Week")
+    fig.update_xaxes(tickformat="%b %d")
+    return gr.Plot(
+        value=fig,
+    )