tabs/trader_plots.py

import gradio as gr
import pandas as pd
import plotly.express as px

trader_metric_choices = [
    "mech calls",
    "bet amount",
    "earnings",
    "net earnings",
    "ROI",
]
default_trader_metric = "ROI"


def get_metrics_text() -> gr.Markdown:
    metric_text = """ 
        ## Metrics at the graph
        These metrics are computed weekly. The statistical measures are:
        * min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
        * the upper and lower fences to delimit possible outliers
        * the average values as the dotted lines
        """

    return gr.Markdown(metric_text)


def get_interpretation_text() -> gr.Markdown:
    interpretation_text = """
        ## Meaning of KL-divergence values
            * Y = 0.05129
                * Market accuracy off by 5%
            * Y = 0.1053
                * Market accuracy off by 10%
            * Y = 0.2876
                * Market accuracy off by 25%
            * Y = 0.5108
                * Market accuracy off by 40%
            * Y = 1.2040
                * Market accuracy off by 70%
            * Y = 2.3026
                * Market accuracy off by 90%
    """
    return gr.Markdown(interpretation_text)


def plot_trader_metrics_by_market_creator(
    metric_name: str, traders_df: pd.DataFrame
) -> gr.Plot:
    """Plots the weekly trader metrics."""

    if metric_name == "mech calls":
        metric_name = "mech_calls"
        column_name = "nr_mech_calls"
        yaxis_title = "Total nr of mech calls per trader"
    elif metric_name == "ROI":
        column_name = "roi"
        yaxis_title = "Total ROI (net profit/cost)"
    elif metric_name == "bet amount":
        metric_name = "bet_amount"
        column_name = metric_name
        yaxis_title = "Total bet amount per trader (xDAI)"
    elif metric_name == "net earnings":
        metric_name = "net_earnings"
        column_name = metric_name
        yaxis_title = "Total net profit per trader (xDAI)"
    else:  # earnings
        column_name = metric_name
        yaxis_title = "Total gross profit per trader (xDAI)"

    traders_filtered = traders_df[["month_year_week", "market_creator", column_name]]

    fig = px.box(
        traders_filtered,
        x="month_year_week",
        y=column_name,
        color="market_creator",
        color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
        category_orders={"market_creator": ["pearl", "quickstart", "all"]},
    )
    fig.update_traces(boxmean=True)
    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,
    )


def plot_median_roi_by_creation_date(traders_df: pd.DataFrame) -> gr.Plot:
    traders_df["creation_date"] = traders_df["creation_timestamp"].dt.date

    traders_all = traders_df.copy(deep=True)
    traders_all["market_creator"] = "all"

    # merging both dataframes
    final_traders = pd.concat([traders_all, traders_df], ignore_index=True)
    final_traders = final_traders.sort_values(by="creation_date", ascending=True)
    roi_daily_metrics = (
        final_traders.groupby(
            ["creation_date", "market_creator", "trader_address"], sort=False
        )
        .agg(
            median_roi=("roi", "median"),
            mean_roi=("roi", "mean"),
            total_trades=("roi", "count"),
        )
        .reset_index()
    )
    # Create the scatter plot with facets for each market_creator
    fig = px.scatter(
        roi_daily_metrics,
        x="creation_date",
        y="median_roi",
        facet_col="market_creator",
        color="market_creator",
        color_discrete_map={
            "pearl": "purple",
            "quickstart": "goldenrod",
            "all": "darkgreen",
        },
        title="Median ROI Over Time by Market Creator",
        labels={
            "creation_date": "Creation Date",
            "median_roi": "Median ROI (%)",
            "market_creator": "Market Creator",
        },
        hover_data={
            "creation_date": "|%B %d, %Y",  # Custom date format in hover
            "median_roi": True,
            "mean_roi": True,
            "total_trades": True,
        },
        category_orders={"market_creator": ["pearl", "quickstart", "all"]},
        # trendline=None,  # Ensure no trendlines are added
    )

    # Customize the layout for better aesthetics
    fig.update_layout(
        template="plotly_white",
        hovermode="closest",
        showlegend=False,  # Disable the legend as each facet has its own context
    )

    # Update each subplot's x-axis to share the same range
    fig.update_xaxes(matches="x")  # Link x-axes across facets
    fig.update_yaxes(matches="y")  # Link y-axes across facets

    # Add a vertical dashed line in dark red at the specified date
    vline_date = "2024-09-29"
    vline_datetime = pd.to_datetime(vline_date, format="%Y-%m-%d")
    fig.add_vline(
        x=vline_datetime,
        line_dash="dash",
        line_color="darkred",
    )
    return gr.Plot(
        value=fig,
    )


import plotly.express as px


def create_median_roi_plot(roi_daily_metrics):
    """
    Creates a Plotly scatter plot for median ROI over time, colored by market_creator.

    Parameters:
    - roi_daily_metrics (pd.DataFrame): Aggregated ROI metrics with columns:
        ['creation_date', 'market_creator', 'trader_address', 'median_roi', 'mean_roi', 'total_trades']

    Returns:
    - fig (plotly.graph_objs._figure.Figure): The Plotly figure object.
    """
    # Ensure 'creation_date' is in datetime format
    roi_daily_metrics["creation_date"] = pd.to_datetime(
        roi_daily_metrics["creation_date"]
    )

    # Create the line plot with scatter markers
    fig = px.line(
        roi_daily_metrics,
        x="creation_date",
        y="median_roi",
        color="market_creator",
        markers=True,  # Add markers to lines
        title="Median ROI Over Time by Market Creator",
        labels={
            "creation_date": "Creation Date",
            "median_roi": "Median ROI (%)",
            "market_creator": "Market Creator",
        },
        hover_data={
            "creation_date": "|%B %d, %Y",  # Custom date format in hover
            "median_roi": True,
            "mean_roi": True,
            "total_trades": True,
        },
    )

    # Customize the layout for better aesthetics
    fig.update_layout(
        xaxis_title="Creation Date",
        yaxis_title="Median ROI (%)",
        legend_title="Market Creator",
        template="plotly_white",
        hovermode="x unified",
    )

    # Optional: Add vertical lines for specific events (e.g., "multibet release")
    # Example:
    # fig.add_vline(
    #     x=pd.to_datetime("2023-01-02"),
    #     line_dash="dash",
    #     line_color="red",
    #     annotation_text="Multibet Release",
    #     annotation_position="top left",
    #     annotation=dict(
    #         bgcolor="white",
    #         font_size=12,
    #         font_color="red"
    #     )
    # )

    return fig


def plot_trader_metrics_by_trader_type(metric_name: str, traders_df: pd.DataFrame):
    """Plots the weekly trader metrics."""

    if metric_name == "mech calls":
        metric_name = "mech_calls"
        column_name = "nr_mech_calls"
        yaxis_title = "Total nr of mech calls per trader"
    elif metric_name == "ROI":
        column_name = "roi"
        yaxis_title = "Total ROI (net profit/cost)"
    elif metric_name == "bet amount":
        metric_name = "bet_amount"
        column_name = metric_name
        yaxis_title = "Total bet amount per trader (xDAI)"
    elif metric_name == "net earnings":
        metric_name = "net_earnings"
        column_name = metric_name
        yaxis_title = "Total net profit per trader (xDAI)"
    else:  # earnings
        column_name = metric_name
        yaxis_title = "Total gross profit per trader (xDAI)"

    traders_filtered = traders_df[["month_year_week", "trader_type", column_name]]

    fig = px.box(
        traders_filtered,
        x="month_year_week",
        y=column_name,
        color="trader_type",
        color_discrete_sequence=["gray", "orange", "darkblue"],
        category_orders={"trader_type": ["singlebet", "multibet", "all"]},
    )
    fig.update_traces(boxmean=True)
    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,
    )


def plot_winning_metric_per_trader(traders_winning_df: pd.DataFrame) -> gr.Plot:
    fig = px.box(
        traders_winning_df,
        x="month_year_week",
        y="winning_perc",
        color="market_creator",
        color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
        category_orders={"market_creator": ["pearl", "quickstart", "all"]},
    )
    fig.update_traces(boxmean=True)
    fig.update_layout(
        xaxis_title="Week",
        yaxis_title="Weekly winning percentage %",
        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
    )
    fig.update_xaxes(tickformat="%b %d\n%Y")

    return gr.Plot(
        value=fig,
    )