tabs/metrics.py

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

trade_metric_choices = [
    "mech calls",
    "collateral amount",
    "earnings",
    "net earnings",
    "ROI",
]

tool_metric_choices = ["losses", "wins", "total_request", "win_perc"]

default_trade_metric = "ROI"
default_tool_metric = "win_perc"

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 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(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[
        ["creation_timestamp", "month_year_week", "market_creator", column_name]
    ]

    # adding the total
    trades_filtered_all = trades_filtered.copy(deep=True)
    trades_filtered_all["market_creator"] = "all"

    # merging both dataframes
    all_filtered_trades = pd.concat(
        [trades_filtered, trades_filtered_all], ignore_index=True
    )
    all_filtered_trades = all_filtered_trades.sort_values(
        by="creation_timestamp", ascending=True
    )
    return all_filtered_trades


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":
        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 = get_metrics(metric_name, column_name, market_creator, trades_df)
    fig = px.line(
        trades_filtered, x="month_year_week", y=metric_name, color="percentile"
    )
    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_trade_metrics(
    metric_name: str, trades_df: pd.DataFrame, height: int = None, width: int = None
) -> 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 = get_boxplot_metrics(column_name, trades_df)
    fig = px.box(
        trades_filtered,
        x="month_year_week",
        y=column_name,
        color="market_creator",
        color_discrete_sequence=["goldenrod", "darkgreen", "purple"],
    )
    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")
    if height is not None:
        fig.update_layout(width=WIDTH, height=HEIGHT)
    return gr.Plot(
        value=fig,
    )


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_tool_metrics(wins_df: pd.DataFrame, winning_selector: str) -> gr.Plot:
    print("under construction")
    if winning_selector == "losses":
        yaxis_title = "Nr of mech calls per trade"
    elif winning_selector == "win_perc":
        column_name = "roi"
        yaxis_title = "ROI (net profit/cost)"
    elif winning_selector == "wins":
        yaxis_title = "Collateral amount per trade (xDAI)"
    else:  # "total_request"
        column_name = metric_name
        yaxis_title = "Gross profit per trade (xDAI)"