updating the metrics graph

app.py

@@ -22,6 +22,7 @@ from tabs.metrics import (
     default_metric,
     plot_trade_details,
     plot2_trade_details,
+    plot_trade_metrics,
 )
 
 from tabs.tool_win import (
@@ -192,15 +193,15 @@ with demo:
             with gr.Row():
                 gr.Markdown("# Trend of weekly trades")
             with gr.Row():
-                with gr.Column():
+                with gr.Column(min_width=400):
                     qs_trades_by_week = plot_trades_per_market_by_week(
                         trades_df=trades_by_market, market_type="quickstart"
                     )
-                with gr.Column():
+                with gr.Column(min_width=400):
                     pearl_trades_by_week = plot_trades_per_market_by_week(
                         trades_df=trades_by_market, market_type="pearl"
                     )
-                with gr.Column():
+                with gr.Column(min_width=400):
                     all_trades_by_week = plot_trades_per_market_by_week(
                         trades_df=trades_by_market, market_type="all"
                     )
@@ -208,15 +209,15 @@ with demo:
             with gr.Row():
                 gr.Markdown("# Percentage of winning trades per week")
             with gr.Row():
-                with gr.Column():
+                with gr.Column(min_width=400):
                     qs_wtrades_by_week = plot_winning_trades_per_market_by_week(
                         trades_df=winning_trades_by_market, market_type="quickstart"
                     )
-                with gr.Column():
+                with gr.Column(min_width=400):
                     pearl_wtrades_by_week = plot_winning_trades_per_market_by_week(
                         trades_df=winning_trades_by_market, market_type="pearl"
                     )
-                with gr.Column():
+                with gr.Column(min_width=400):
                     all_wtrades_by_week = plot_winning_trades_per_market_by_week(
                         trades_df=winning_trades_by_market, market_type="all"
                     )
@@ -230,14 +231,13 @@ with demo:
                     value=default_metric,
                 )
             with gr.Row():
-                trade_details_plot = plot2_trade_details(
-                    metric_name=default_metric, trades_df=trades_df
+                trade_details_plot = plot_trade_metrics(
+                    metric_name=default_metric,
+                    trades_df=trades_df,
                 )
 
             def update_trade_details(trade_detail):
-                return plot2_trade_details(
-                    metric_name=trade_detail, trades_df=trades_df
-                )
+                return plot_trade_metrics(metric_name=trade_detail, trades_df=trades_df)
 
             trade_details_selector.change(
                 update_trade_details,

tabs/metrics.py

@@ -59,7 +59,53 @@ def plot_trade_details(metric_name: str, trades_df: pd.DataFrame) -> gr.LinePlot
     )
 
 
-def plot2_trade_details(metric_name: str, trades_df: pd.DataFrame) -> gr.Plot:
+def get_metrics(
+    metric_name: str, column_name: str, market_creator: str, trades_df: pd.DataFrame
+) -> pd.DataFrame:
+    # this is to filter out the data before 2023-09-01
+    trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
+    if market_creator != "all":
+        trades_filtered = trades_filtered.loc[
+            trades_filtered["market_creator"] == market_creator
+        ]
+
+    trades_filtered = (
+        trades_filtered.groupby("month_year_week", sort=False)[column_name]
+        .quantile([0.25, 0.5, 0.75])
+        .unstack()
+    )
+    # reformat the data as percentile, date, value
+    trades_filtered = trades_filtered.melt(
+        id_vars=["month_year_week"], var_name="percentile", value_name=metric_name
+    )
+    trades_filtered.columns = trades_filtered.columns.astype(str)
+    trades_filtered.reset_index(inplace=True)
+    trades_filtered.columns = [
+        "month_year_week",
+        "25th_percentile",
+        "50th_percentile",
+        "75th_percentile",
+    ]
+    # reformat the data as percentile, date, value
+    trades_filtered = trades_filtered.melt(
+        id_vars=["month_year_week"], var_name="percentile", value_name=metric_name
+    )
+    return trades_filtered
+
+
+def get_boxplot_metrics(
+    metric_name: str, column_name: str, trades_df: pd.DataFrame
+) -> pd.DataFrame:
+    # this is to filter out the data before 2023-09-01
+    trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
+    trades_filtered = trades_filtered[
+        ["month_year_week", "market_creator", column_name]
+    ]
+
+
+def plot2_trade_details(
+    metric_name: str, market_creator: str, trades_df: pd.DataFrame
+) -> gr.Plot:
     """Plots the trade details for the given trade detail."""
 
     if metric_name == "mech calls":
@@ -81,29 +127,55 @@ def plot2_trade_details(metric_name: str, trades_df: pd.DataFrame) -> gr.Plot:
         column_name = metric_name
         yaxis_title = "Gross profit per trade (xDAI)"
 
-    # this is to filter out the data before 2023-09-01
-    trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
-    trades_filtered = (
-        trades_filtered.groupby("month_year_week", sort=False)[column_name]
-        .quantile([0.25, 0.5, 0.75])
-        .unstack()
+    trades_filtered = get_metrics(metric_name, column_name, market_creator, trades_df)
+    fig = px.line(
+        trades_filtered, x="month_year_week", y=metric_name, color="percentile"
     )
-    trades_filtered.columns = trades_filtered.columns.astype(str)
-    trades_filtered.reset_index(inplace=True)
-    trades_filtered.columns = [
-        "month_year_week",
-        "25th_percentile",
-        "50th_percentile",
-        "75th_percentile",
-    ]
-    # reformat the data as percentile, date, value
-    trades_filtered = trades_filtered.melt(
-        id_vars=["month_year_week"], var_name="percentile", value_name=metric_name
+    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,
     )
 
-    fig = px.line(
-        trades_filtered, x="month_year_week", y=metric_name, color="percentile"
+
+def plot_trade_metrics(metric_name: str, trades_df: pd.DataFrame) -> gr.Plot:
+    """Plots the trade metrics."""
+
+    if metric_name == "mech calls":
+        metric_name = "mech_calls"
+        column_name = "num_mech_calls"
+        yaxis_title = "Nr of mech calls per trade"
+    elif metric_name == "ROI":
+        column_name = "roi"
+        yaxis_title = "ROI (net profit/cost)"
+    elif metric_name == "collateral amount":
+        metric_name = "collateral_amount"
+        column_name = metric_name
+        yaxis_title = "Collateral amount per trade (xDAI)"
+    elif metric_name == "net earnings":
+        metric_name = "net_earnings"
+        column_name = metric_name
+        yaxis_title = "Net profit per trade (xDAI)"
+    else:  # earnings
+        column_name = metric_name
+        yaxis_title = "Gross profit per trade (xDAI)"
+
+    trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
+    trades_filtered = trades_filtered[
+        ["month_year_week", "market_creator", column_name]
+    ]
+    fig = px.box(
+        trades_filtered,
+        x="month_year_week",
+        y=column_name,
+        color="market_creator",
+        color_discrete_sequence=["goldenrod", "purple"],
     )
+    fig.update_traces(boxmean=True)
     fig.update_layout(
         xaxis_title="Week",
         yaxis_title=yaxis_title,