tabs/trades.py

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


HEIGHT = 600
WIDTH = 1000


def prepare_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
    """Prepares the trades data for analysis."""
    trades_df["creation_timestamp"] = pd.to_datetime(trades_df["creation_timestamp"])
    trades_df["creation_timestamp"] = trades_df["creation_timestamp"].dt.tz_convert(
        "UTC"
    )
    trades_df["month_year"] = (
        trades_df["creation_timestamp"].dt.to_period("M").astype(str)
    )
    trades_df["month_year_week"] = (
        trades_df["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d")
    )
    trades_df["winning_trade"] = trades_df["winning_trade"].astype(int)
    return trades_df


def get_overall_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
    """Gets the overall trades data"""
    trades_count = trades_df.groupby("month_year_week").size().reset_index()
    trades_count.columns = trades_count.columns.astype(str)
    trades_count.rename(columns={"0": "trades"}, inplace=True)
    return trades_count


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_count.columns = trades_count.columns.astype(str)
    trades_count.rename(columns={"0": "trades"}, inplace=True)
    return trades_count


def get_overall_winning_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(["month_year_week"])["winning_trade"].sum()
        / trades_df.groupby(["month_year_week"])["winning_trade"].count()
        * 100
    )
    # 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 = ["month_year_week", "winning_trade"]
    return winning_trades


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"])[
            "winning_trade"
        ].count()
        * 100
    )
    # 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"]
    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(
        value=trades_df,
        x="month_year_week",
        y="trades",
        show_label=True,
        interactive=True,
        show_actions_button=True,
        tooltip=["month_year_week", "trades"],
        height=HEIGHT,
        width=WIDTH,
    )


def plot_trades_per_market_by_week(
    trades_df: pd.DataFrame, market_type: str
) -> gr.Plot:
    """Plots the trades data for the given tools and calculates the winning percentage."""
    assert "market_creator" in trades_df.columns
    # if market_type is "all then no filter is applied"
    if market_type == "quickstart":
        trades = trades_df.loc[trades_df["market_creator"] == "quickstart"]
        color_sequence = ["goldenrod"]

    elif market_type == "pearl":
        trades = trades_df.loc[trades_df["market_creator"] == "pearl"]
        color_sequence = ["purple"]
    else:
        trades = trades_df
        color_sequence = ["darkgreen"]

    fig = px.bar(
        trades,
        x="month_year_week",
        y="trades",
        color_discrete_sequence=color_sequence,
        title=market_type + " trades",
    )
    fig.update_layout(
        xaxis_title="Week",
        yaxis_title="Weekly nr of trades",
    )
    fig.update_xaxes(tickformat="%b %d\n%Y")
    return gr.Plot(
        value=fig,
    )


def plot_winning_trades_by_week(trades_df: pd.DataFrame) -> gr.BarPlot:
    """Plots the winning trades data for the given tools and calculates the winning percentage."""
    return gr.BarPlot(
        value=trades_df,
        x="month_year_week",
        y="winning_trade",
        show_label=True,
        interactive=True,
        show_actions_button=True,
        tooltip=["month_year_week", "winning_trade"],
        height=HEIGHT,
        width=WIDTH,
    )


def plot_winning_trades_per_market_by_week(
    trades_df: pd.DataFrame, market_type: str
) -> gr.Plot:
    """Plots the winning trades data for the given tools and calculates the winning percentage."""
    # if market_type is "all then no filter is applied"
    if market_type == "quickstart":
        trades = trades_df.loc[trades_df["market_creator"] == "quickstart"]
        color_sequence = ["goldenrod"]

    elif market_type == "pearl":
        trades = trades_df.loc[trades_df["market_creator"] == "pearl"]
        color_sequence = ["purple"]
    else:
        trades = trades_df
        color_sequence = ["darkgreen"]

    fig = px.bar(
        trades,
        x="month_year_week",
        y="winning_trade",
        color_discrete_sequence=color_sequence,
        title=market_type + " winning trades",
    )
    fig.update_layout(
        xaxis_title="Week",
        yaxis_title="Weekly % of winning trades",
        # yaxis_tickformat="%",
    )
    fig.update_xaxes(tickformat="%b %d\n%Y")
    return gr.Plot(
        value=fig,
    )