Fixing x axis order

app.py

@@ -15,8 +15,14 @@ from tabs.trades import (
     plot_trades_per_market_by_week,
     plot_winning_trades_by_week,
     plot_winning_trades_per_market_by_week,
+)
+
+from tabs.metrics import (
+    metric_choices,
+    default_metric,
     plot_trade_details,
 )
+
 from tabs.tool_win import (
     get_tool_winning_rate,
     get_overall_winning_rate,
@@ -212,7 +218,7 @@ with demo:
                     )
                 with gr.Column(min_width=350):
                     all_wtrades_by_week = plot_winning_trades_per_market_by_week(
-                        trades_df=trades_winning_rate_df, market_type="all"
+                        trades_df=winning_trades_by_market, market_type="all"
                     )
 
             with gr.Row():
@@ -220,24 +226,16 @@ with demo:
             with gr.Row():
                 trade_details_selector = gr.Dropdown(
                     label="Select a trade metric",
-                    choices=[
-                        "mech calls",
-                        "collateral amount",
-                        "earnings",
-                        "net earnings",
-                        "ROI",
-                    ],
-                    value="mech calls",
+                    choices=metric_choices,
+                    value=default_metric,
                 )
             with gr.Row():
                 trade_details_plot = plot_trade_details(
-                    trade_detail="mech calls", trades_df=trades_df
+                    metric_name=default_metric, trades_df=trades_df
                 )
 
             def update_trade_details(trade_detail):
-                return plot_trade_details(
-                    trade_detail=trade_detail, trades_df=trades_df
-                )
+                return plot_trade_details(metric_name=trade_detail, trades_df=trades_df)
 
             trade_details_selector.change(
                 update_trade_details,

tabs/metrics.py

@@ -0,0 +1,80 @@
+import pandas as pd
+import gradio as gr
+
+metric_choices = [
+    "mech calls",
+    "collateral amount",
+    "earnings",
+    "net earnings",
+    "ROI",
+]
+
+default_metric = "ROI"
+
+HEIGHT = 600
+WIDTH = 1000
+
+
+def plot_trade_details(metric_name: str, trades_df: pd.DataFrame) -> gr.LinePlot:
+    """Plots the trade details for the given trade detail."""
+    column_name = metric_name
+    if metric_name == "mech calls":
+        metric_name = "mech_calls"
+        column_name = "num_mech_calls"
+    elif metric_name == "ROI":
+        column_name = "roi"
+
+    # 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")[column_name]
+        .quantile([0.25, 0.5, 0.75])
+        .unstack()
+    )
+    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 gr.LinePlot(
+        value=trades_filtered,
+        x="month_year_week",
+        y=metric_name,
+        color="percentile",
+        show_label=True,
+        interactive=True,
+        show_actions_button=True,
+        tooltip=["month_year_week", "percentile", metric_name],
+        height=HEIGHT,
+        width=WIDTH,
+    )
+
+
+def plot_average_roi_per_market_by_week(trades_df: pd.DataFrame) -> gr.LinePlot:
+
+    mean_roi_per_market_by_week = (
+        trades_df.groupby(["market_creator", "month_year_week"])["roi"]
+        .mean()
+        .reset_index()
+    )
+    mean_roi_per_market_by_week.rename(columns={"roi": "mean_roi"}, inplace=True)
+    return gr.LinePlot(
+        value=mean_roi_per_market_by_week,
+        x="month_year_week",
+        y="ROI",
+        color="market_creator",
+        show_label=True,
+        interactive=True,
+        show_actions_button=True,
+        tooltip=["month_year_week", "market_creator", "mean_roi"],
+        height=HEIGHT,
+        width=WIDTH,
+    )

tabs/trades.py

@@ -13,6 +13,7 @@ def prepare_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
     trades_df["creation_timestamp"] = trades_df["creation_timestamp"].dt.tz_convert(
         "UTC"
     )
+    trades_df = trades_df.sort_values(by="creation_timestamp", ascending=True)
     trades_df["month_year"] = (
         trades_df["creation_timestamp"].dt.to_period("M").astype(str)
     )
@@ -34,7 +35,9 @@ def get_overall_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
 def get_overall_by_market_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
     """Gets the overall trades data"""
     trades_count = (
-        trades_df.groupby(["market_creator", "month_year_week"]).size().reset_index()
+        trades_df.groupby(["month_year_week", "market_creator"], sort=False)
+        .size()
+        .reset_index()
     )
     trades_count.columns = trades_count.columns.astype(str)
     trades_count.rename(columns={"0": "trades"}, inplace=True)
@@ -58,8 +61,10 @@ def get_overall_winning_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
 def get_overall_winning_by_market_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
     """Gets the overall winning trades data for the given tools and calculates the winning percentage."""
     winning_trades = (
-        trades_df.groupby(["market_creator", "month_year_week"])["winning_trade"].sum()
-        / trades_df.groupby(["market_creator", "month_year_week"])[
+        trades_df.groupby(["month_year_week", "market_creator"], sort=False)[
+            "winning_trade"
+        ].sum()
+        / trades_df.groupby(["month_year_week", "market_creator"], sort=False)[
             "winning_trade"
         ].count()
         * 100
@@ -67,206 +72,11 @@ def get_overall_winning_by_market_trades(trades_df: pd.DataFrame) -> pd.DataFram
     # winning_trades is a series, give it a dataframe
     winning_trades = winning_trades.reset_index()
     winning_trades.columns = winning_trades.columns.astype(str)
-    winning_trades.columns = ["market_creator", "month_year_week", "winning_trade"]
+    winning_trades.columns = ["month_year_week", "market_creator", "winning_trade"]
     print(winning_trades.head())
     return winning_trades
 
 
-def plot_trade_details(trade_detail: str, trades_df: pd.DataFrame) -> gr.LinePlot:
-    """Plots the trade details for the given trade detail."""
-    if trade_detail == "mech calls":
-        # 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")["num_mech_calls"]
-            .quantile([0.25, 0.5, 0.75])
-            .unstack()
-        )
-        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="mech_calls"
-        )
-
-        return gr.LinePlot(
-            value=trades_filtered,
-            x="month_year_week",
-            y="mech_calls",
-            color="percentile",
-            show_label=True,
-            interactive=True,
-            show_actions_button=True,
-            tooltip=["month_year_week", "percentile", "mech_calls"],
-            height=HEIGHT,
-            width=WIDTH,
-        )
-
-    if trade_detail == "collateral amount":
-        trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
-        trades_filtered = (
-            trades_filtered.groupby("month_year_week")["collateral_amount"]
-            .quantile([0.25, 0.5, 0.75])
-            .unstack()
-        )
-        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="collateral_amount",
-        )
-
-        return gr.LinePlot(
-            value=trades_filtered,
-            x="month_year_week",
-            y="collateral_amount",
-            color="percentile",
-            show_label=True,
-            interactive=True,
-            show_actions_button=True,
-            tooltip=["month_year_week", "percentile", "collateral_amount"],
-            height=HEIGHT,
-            width=WIDTH,
-        )
-
-    if trade_detail == "earnings":
-        trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
-        trades_filtered = (
-            trades_filtered.groupby("month_year_week")["earnings"]
-            .quantile([0.25, 0.5, 0.75])
-            .unstack()
-        )
-        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="earnings"
-        )
-
-        return gr.LinePlot(
-            value=trades_filtered,
-            x="month_year_week",
-            y="earnings",
-            color="percentile",
-            show_label=True,
-            interactive=True,
-            show_actions_button=True,
-            tooltip=["month_year_week", "percentile", "earnings"],
-            height=HEIGHT,
-            width=WIDTH,
-        )
-
-    if trade_detail == "net earnings":
-        trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
-        trades_filtered = (
-            trades_filtered.groupby("month_year_week")["net_earnings"]
-            .quantile([0.25, 0.5, 0.75])
-            .unstack()
-        )
-        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="net_earnings",
-        )
-
-        return gr.LinePlot(
-            value=trades_filtered,
-            x="month_year_week",
-            y="net_earnings",
-            color="percentile",
-            show_label=True,
-            interactive=True,
-            show_actions_button=True,
-            tooltip=["month_year_week", "percentile", "net_earnings"],
-            height=HEIGHT,
-            width=WIDTH,
-        )
-
-    if trade_detail == "ROI":
-        trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
-        trades_filtered = (
-            trades_filtered.groupby("month_year_week")["roi"]
-            .quantile([0.25, 0.5, 0.75])
-            .unstack()
-        )
-        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="ROI"
-        )
-
-        return gr.LinePlot(
-            value=trades_filtered,
-            x="month_year_week",
-            y="ROI",
-            color="percentile",
-            show_label=True,
-            interactive=True,
-            show_actions_button=True,
-            tooltip=["month_year_week", "percentile", "ROI"],
-            height=HEIGHT,
-            width=WIDTH,
-        )
-
-
-def plot_average_roi_per_market_by_week(trades_df: pd.DataFrame) -> gr.LinePlot:
-
-    mean_roi_per_market_by_week = (
-        trades_df.groupby(["market_creator", "month_year_week"])["roi"]
-        .mean()
-        .reset_index()
-    )
-    mean_roi_per_market_by_week.rename(columns={"roi": "mean_roi"}, inplace=True)
-    return gr.LinePlot(
-        value=mean_roi_per_market_by_week,
-        x="month_year_week",
-        y="ROI",
-        color="market_creator",
-        show_label=True,
-        interactive=True,
-        show_actions_button=True,
-        tooltip=["month_year_week", "market_creator", "mean_roi"],
-        height=HEIGHT,
-        width=WIDTH,
-    )
-
-
 def plot_trades_by_week(trades_df: pd.DataFrame) -> gr.BarPlot:
     """Plots the trades data for the given tools and calculates the winning percentage."""
     return gr.BarPlot(
@@ -309,6 +119,7 @@ def plot_trades_per_market_by_week(
     fig.update_layout(
         xaxis_title="Week",
         yaxis_title="Weekly nr of trades",
+        # xaxis_type="category",
     )
     fig.update_xaxes(tickformat="%b %d\n%Y")
     return gr.Plot(
@@ -357,7 +168,7 @@ def plot_winning_trades_per_market_by_week(
     fig.update_layout(
         xaxis_title="Week",
         yaxis_title="Weekly % of winning trades",
-        # yaxis_tickformat="%",
+        # xaxis_type="category",
     )
     fig.update_xaxes(tickformat="%b %d\n%Y")
     return gr.Plot(