tabs/trader_plots.py

import gradio as gr
import pandas as pd
import plotly.express as px
from tabs.market_plots import color_mapping
from datetime import datetime

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


def get_metrics_text(trader_type: str = None, daily: bool = False) -> gr.Markdown:
    if daily:
        metric_text = """ 
            ## Metrics at the graph
            These metrics are computed daily. 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
            """
    elif trader_type is None:
        metric_text = """ 
            ## Description of 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
            """
    elif trader_type == "Olas":
        metric_text = """ 
            ## Definition of Olas trader
            Agents using Mech, with a service ID and the corresponding safe in the registry
            ## Description of 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
            """
    elif trader_type == "non_Olas":
        metric_text = """ 
            ## Definition of non-Olas trader
            Agents using Mech, with no service ID
            ## Description of 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
            """
    else:  # Unclassified
        metric_text = """ 
            ## Definition of unclassified trader
            Agents (safe/EOAs) not using Mechs
            ## Description of 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_agent_categories(
    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 = "total_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 = "total_bet_amount"
        yaxis_title = "Total bet amount per trader (xDAI)"
    elif metric_name == "net earnings":
        metric_name = "net_earnings"
        column_name = "net_earnings"
        yaxis_title = "Total net profit per trader (xDAI)"
    elif metric_name == "nr_trades":
        column_name = metric_name
        yaxis_title = "Total nr of trades per trader"
    else:  # earnings
        column_name = metric_name
        yaxis_title = "Total gross profit per trader (xDAI)"

    fig = px.box(
        traders_df,
        x="week_start",
        y=column_name,
    )
    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_trader_daily_metrics_by_market_creator(
    metric_name: str, traders_df: pd.DataFrame
) -> gr.Plot:
    """Plots the daily 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)"
    elif metric_name == "nr_trades":
        column_name = metric_name
        yaxis_title = "Total nr of trades per trader"
    else:  # earnings
        column_name = metric_name
        yaxis_title = "Total gross profit per trader (xDAI)"

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

    fig = px.box(
        traders_filtered,
        x="creation_date",
        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="Day",
        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,
    )


def plot_total_bet_amount(
    trades_df: pd.DataFrame, market_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_Olas" if x == "non_Olas" else "Olas"
    )

    total_bet_amount = (
        final_traders.groupby(
            ["month_year_week", "market_creator", "trader_type"], sort=False
        )["collateral_amount"]
        .sum()
        .reset_index(name="total_bet_amount")
    )
    # Convert string dates to datetime and sort them
    all_dates_dt = sorted(
        [
            datetime.strptime(date, "%b-%d-%Y")
            for date in total_bet_amount["month_year_week"].unique()
        ]
    )
    # Convert back to string format
    all_dates = [date.strftime("%b-%d-%Y") for date in all_dates_dt]
    total_bet_amount["trader_market"] = total_bet_amount.apply(
        lambda x: (x["trader_type"], x["market_creator"]), axis=1
    )
    color_discrete_sequence = ["purple", "goldenrod", "darkgreen"]
    if market_filter == "pearl":
        color_discrete_sequence = ["darkviolet", "goldenrod", "green"]
        total_bet_amount = total_bet_amount.loc[
            total_bet_amount["market_creator"] == "pearl"
        ]
    elif market_filter == "quickstart":
        total_bet_amount = total_bet_amount.loc[
            total_bet_amount["market_creator"] == "quickstart"
        ]
    else:
        total_bet_amount = total_bet_amount.loc[
            total_bet_amount["market_creator"] == "all"
        ]

    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": [
                ("Olas", "pearl"),
                ("non_Olas", "pearl"),
                ("Olas", "quickstart"),
                ("non_Olas", "quickstart"),
                ("Olas", "all"),
                ("non_Olas", "all"),
            ],
        },
        barmode="group",
    )

    fig.update_layout(
        xaxis_title="Week",
        yaxis_title="Weekly total bet amount per trader type",
        legend=dict(yanchor="top", y=0.5),
    )

    fig.update_xaxes(tickformat="%b %d")
    # Update layout to force x-axis category order (hotfix for a sorting issue)
    fig.update_layout(xaxis={"categoryorder": "array", "categoryarray": all_dates})
    return gr.Plot(
        value=fig,
    )


def plot_active_traders(
    active_traders_data: pd.DataFrame,
    market_creator: str = None,
):
    """Function to plot the volume of active traders for the different categories and markets"""

    filtered_traders_data = active_traders_data.copy()
    if market_creator is not None:
        filtered_traders_data = filtered_traders_data.loc[
            filtered_traders_data["market_creator"] == market_creator
        ]
    active_traders = (
        filtered_traders_data.groupby(by=["month_year_week", "trader_type"])[
            "trader_address"
        ]
        .nunique()
        .reset_index(name="nr_traders")
    )
    # Convert string dates to datetime and sort them
    all_dates_dt = sorted(
        [
            datetime.strptime(date, "%b-%d-%Y")
            for date in active_traders["month_year_week"].unique()
        ]
    )
    # Convert back to string format
    all_dates = [date.strftime("%b-%d-%Y") for date in all_dates_dt]
    color_mapping = [
        "royalblue",
        "goldenrod",
        "gray",
    ]
    fig = px.bar(
        active_traders,
        x="month_year_week",
        y="nr_traders",
        color="trader_type",
        color_discrete_sequence=color_mapping,
        category_orders={
            "trader_type": ["Olas", "non_Olas", "unknown"],
        },
        barmode="group",
    )
    fig.update_layout(
        xaxis_title="Week",
        yaxis_title="Weekly active traders per trader type",
        legend=dict(yanchor="top", y=0.5),
    )

    fig.update_xaxes(tickformat="%b %d")
    # Update layout to force x-axis category order (hotfix for a sorting issue)
    fig.update_layout(xaxis={"categoryorder": "array", "categoryarray": all_dates})
    return gr.Plot(
        value=fig,
    )