nr trades as live metric

app.py

@@ -14,18 +14,19 @@ from tabs.trader_plots import (
     plot_trader_daily_metrics_by_market_creator,
     default_trader_metric,
     trader_metric_choices,
-    trade_daily_metric_choices,
-    default_daily_metric,
     get_metrics_text,
     plot_winning_metric_per_trader,
     get_interpretation_text,
 )
-from tabs.daily_graphs import get_current_week_data, plot_daily_metrics
+from tabs.daily_graphs import (
+    get_current_week_data,
+    plot_daily_metrics,
+    trade_daily_metric_choices,
+    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,
-)
+from tabs.market_plots import plot_kl_div_per_market, plot_total_bet_amount
 
 
 def get_logger():
@@ -86,10 +87,9 @@ def prepare_data():
 
     # nr-trades variable
     volume_trades_per_trader_and_market = (
-        all_trades.groupby(["trader_address", "title"])["roi"].count().reset_index()
-    )
-    volume_trades_per_trader_and_market.rename(
-        columns={"roi": "nr_trades_per_market"}, inplace=True
+        all_trades.groupby(["trader_address", "title"])["roi"]
+        .count()
+        .reset_index(name="nr_trades_per_market")
     )
 
     trader_agents_data = pd.merge(
@@ -100,7 +100,7 @@ def prepare_data():
     trader_agents_data["trader_family"] = trader_agents_data.apply(
         lambda x: get_traders_family(x), axis=1
     )
-    print(trader_agents_data.trader_family.value_counts())
+    print(trader_agents_data.head())
 
     trader_agents_data = trader_agents_data.sort_values(
         by="creation_timestamp", ascending=True
@@ -336,6 +336,13 @@ 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.Row():
                 gr.Markdown(
                     "# Weekly volume of trades at each market per trader family"

scripts/metrics.py

@@ -19,12 +19,12 @@ def compute_metrics(
     agg_metrics["bet_amount"] = total_bet_amounts
     agg_metrics["nr_mech_calls"] = total_num_mech_calls
     agg_metrics["staking"] = trader_data.iloc[0].staking
+    agg_metrics["nr_trades"] = len(trader_data)
     if live_metrics:
         return agg_metrics
     total_net_earnings = trader_data.net_earnings.sum()
     agg_metrics["net_earnings"] = total_net_earnings
     agg_metrics["earnings"] = trader_data.earnings.sum()
-    agg_metrics["nr_trades"] = len(trader_data)
     total_fee_amounts = trader_data.mech_fee_amount.sum()
     total_costs = (
         total_bet_amounts

tabs/daily_graphs.py

@@ -7,6 +7,9 @@ import plotly.graph_objects as go
 from datetime import datetime, timedelta
 from tqdm import tqdm
 
+trade_daily_metric_choices = ["mech calls", "collateral amount", "nr_trades"]
+default_daily_metric = "collateral amount"
+
 
 def plot_daily_trades(trades_df: pd.DataFrame) -> gr.Plot:
     # get daily trades
@@ -75,21 +78,24 @@ def plot_daily_metrics(
     if metric_name == "mech calls":
         metric_name = "nr_mech_calls"
         column_name = "nr_mech_calls"
-        yaxis_title = "Nr of mech calls per trade"
+        yaxis_title = "Total nr of mech calls per trader"
+    elif metric_name == "nr_trades":
+        column_name = metric_name
+        yaxis_title = "Total nr of trades per trader"
     elif metric_name == "ROI":
         column_name = "roi"
-        yaxis_title = "ROI (net profit/cost)"
+        yaxis_title = "Total ROI (net profit/cost)"
     elif metric_name == "collateral amount":
         metric_name = "bet_amount"
         column_name = metric_name
-        yaxis_title = "Collateral amount per trade (xDAI)"
+        yaxis_title = "Total bet amount per trader (xDAI)"
     elif metric_name == "net earnings":
         metric_name = "net_earnings"
         column_name = metric_name
-        yaxis_title = "Net profit per trade (xDAI)"
+        yaxis_title = "Total net profit per trader (xDAI)"
     else:  # earnings
         column_name = metric_name
-        yaxis_title = "Gross profit per trade (xDAI)"
+        yaxis_title = "Total gross profit per trader (xDAI)"
 
     if trader_filter == "agent":
         trades_filtered = trades_df.loc[trades_df["staking"] != "non_agent"]

tabs/market_plots.py

@@ -85,3 +85,64 @@ def plot_kl_div_with_off_by(closed_markets: pd.DataFrame) -> gr.Plot:
     return gr.Plot(
         value=fig,
     )
+
+
+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,
+    )

tabs/trader_plots.py

@@ -12,12 +12,6 @@ trader_metric_choices = [
 ]
 default_trader_metric = "ROI"
 
-trade_daily_metric_choices = [
-    "mech calls",
-    "collateral amount",
-]
-default_daily_metric = "collateral amount"
-
 
 def get_metrics_text() -> gr.Markdown:
     metric_text = """