tabs/daily_graphs.py

import pandas as pd
import gradio as gr
import gc
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from datetime import datetime, timedelta
from tqdm import tqdm

trader_daily_metric_choices = ["mech calls", "collateral amount", "nr_trades"]
default_daily_metric = "collateral amount"
color_mapping = [
    "darkviolet",
    "purple",
    "goldenrod",
    "darkgoldenrod",
    "green",
    "darkgreen",
]


def plot_daily_trades(trades_df: pd.DataFrame) -> gr.Plot:
    # get daily trades
    daily_trades_count = (
        trades_df.groupby("month_year_week").size().reset_index(name="trades")
    )
    daily_trades_count.columns = daily_trades_count.columns.astype(str)
    print("WIP")


def get_current_week_data(trades_df: pd.DataFrame) -> pd.DataFrame:
    # Get current date
    now = datetime.now()

    # Get start of the current week (Monday)
    start_of_week = now - timedelta(days=now.weekday())
    start_of_week = start_of_week.replace(hour=0, minute=0, second=0, microsecond=0)
    print(f"start of the week = {start_of_week}")

    # Get end of the current week (Sunday)
    end_of_week = start_of_week + timedelta(days=6)
    end_of_week = end_of_week.replace(hour=23, minute=59, second=59, microsecond=999999)
    print(f"end of the week = {end_of_week}")
    trades_df["creation_date"] = pd.to_datetime(trades_df["creation_date"])
    # Filter the dataframe
    return trades_df[
        (trades_df["creation_date"] >= start_of_week)
        & (trades_df["creation_date"] <= end_of_week)
    ]


def get_boxplot_daily_metrics(
    column_name: str, trades_df: pd.DataFrame
) -> pd.DataFrame:
    trades_filtered = trades_df[
        [
            "creation_timestamp",
            "creation_date",
            "market_creator",
            "trader_address",
            "staking",
            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
    )
    gc.collect()
    return all_filtered_trades


def plot_daily_metrics(
    metric_name: str, trades_df: pd.DataFrame, trader_filter: str = None
) -> gr.Plot:
    """Plots the trade metrics."""

    if metric_name == "mech calls":
        metric_name = "nr_mech_calls"
        column_name = "nr_mech_calls"
        yaxis_title = "Total nr of mech calls per trader"
    elif metric_name == "nr_trades":
        column_name = metric_name
        yaxis_title = "Total nr of trades per trader"
    elif metric_name == "ROI":
        column_name = "roi"
        yaxis_title = "Total ROI (net profit/cost)"
    elif metric_name == "collateral 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)"

    color_discrete_sequence = ["purple", "goldenrod", "darkgreen"]
    if trader_filter == "agent":
        color_discrete_sequence = ["darkviolet", "goldenrod", "green"]
        trades_filtered = trades_df.loc[trades_df["staking"] != "non_agent"]
    elif trader_filter == "non_agent":
        trades_filtered = trades_df.loc[trades_df["staking"] == "non_agent"]
    else:
        trades_filtered = trades_df

    # Create binary staking category
    trades_filtered["trader_type"] = trades_filtered["staking"].apply(
        lambda x: "non_agent" if x == "non_agent" else "agent"
    )
    trades_filtered["trader_market"] = trades_filtered.apply(
        lambda x: (x["trader_type"], x["market_creator"]), axis=1
    )

    all_dates = sorted(trades_filtered["creation_date"].unique())
    fig = px.box(
        trades_filtered,
        x="creation_date",
        y=column_name,
        color="market_creator",
        color_discrete_sequence=color_discrete_sequence,
        category_orders={
            "market_creator": ["pearl", "quickstart", "all"],
            "trader_market": [
                ("agent", "pearl"),
                ("non_agent", "pearl"),
                ("agent", "quickstart"),
                ("non_agent", "quickstart"),
                ("agent", "all"),
                ("non_agent", "all"),
            ],
        },
        # facet_col="market_creator",
    )
    fig.update_traces(boxmean=True)
    fig.update_layout(
        xaxis_title="Day",
        yaxis_title=yaxis_title,
        legend=dict(yanchor="top", y=0.5),
    )
    # for axis in fig.layout:
    #     if axis.startswith("xaxis"):
    #         fig.layout[axis].update(title="Day")
    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_daily_metrics_v2(
    metric_name: str, trades_df: pd.DataFrame, trader_filter: str = 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)"

    color_discrete = ["purple", "darkgoldenrod", "darkgreen"]
    trades_filtered = get_boxplot_daily_metrics(column_name, trades_df)
    fig = make_subplots(rows=1, cols=2, subplot_titles=("Agent", "Non-Agents"))

    # Create first boxplot for staking=True
    fig.add_trace(
        go.Box(
            x=trades_filtered[trades_filtered["staking"] != "non_agent"][
                "creation_date"
            ],
            y=trades_filtered[trades_filtered["staking"] != "non_agent"][column_name],
            name="Trades from agents",
            marker_color=color_discrete[0],
            legendgroup="staking_true",
            showlegend=True,
        ),
        row=1,
        col=1,
    )

    # Create second boxplot for staking=False
    fig.add_trace(
        go.Box(
            x=trades_filtered[trades_filtered["staking"] == False]["creation_date"],
            y=trades_filtered[trades_filtered["staking"] == False][column_name],
            name="Staking False",
            marker_color=color_discrete[1],
            legendgroup="staking_false",
            showlegend=True,
        ),
        row=1,
        col=2,
    )

    # Update layout
    fig.update_layout(
        height=600,
        width=1200,
        title_text=f"Box Plot of {column_name} by Staking Status",
        showlegend=True,
    )

    # Update y-axes to have the same range
    fig.update_yaxes(matches="y")