app.py

from datetime import datetime, timedelta
import gradio as gr
import pandas as pd
import duckdb
import logging


from scripts.metrics import (
    compute_weekly_metrics_by_market_creator,
    compute_weekly_metrics_by_trader_type,
    compute_winning_metrics_by_trader,
)
from tabs.trader_plots import (
    plot_trader_metrics_by_market_creator,
    plot_trader_metrics_by_trader_type,
    default_trader_metric,
    trader_metric_choices,
    get_metrics_text,
    plot_winning_metric_per_trader,
    get_interpretation_text,
)

from tabs.market_plots import (
    plot_kl_div_per_market,
)


def get_logger():
    logger = logging.getLogger(__name__)
    logger.setLevel(logging.DEBUG)
    # stream handler and formatter
    stream_handler = logging.StreamHandler()
    stream_handler.setLevel(logging.DEBUG)
    formatter = logging.Formatter(
        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
    )
    stream_handler.setFormatter(formatter)
    logger.addHandler(stream_handler)
    return logger


logger = get_logger()


def get_all_data():
    """
    Get parquet files from weekly stats and new generated
    """
    logger.info("Getting traders data")
    con = duckdb.connect(":memory:")
    # Query to fetch data from all_trades_profitability.parquet
    query1 = f"""
    SELECT *
    FROM read_parquet('./data/all_trades_profitability.parquet')
    """
    df1 = con.execute(query1).fetchdf()
    logger.info("Got all data from all_trades_profitability.parquet")

    # Query to fetch data from closed_markets_div.parquet
    query2 = f"""
    SELECT *
    FROM read_parquet('./data/closed_markets_div.parquet')
    """
    df2 = con.execute(query2).fetchdf()
    logger.info("Got all data from closed_markets_div.parquet")

    con.close()

    return df1, df2


def prepare_data():

    all_trades, closed_markets = get_all_data()

    all_trades["creation_date"] = all_trades["creation_timestamp"].dt.date

    # adding multi-bet variables
    volume_trades_per_trader_and_market = (
        all_trades.groupby(["trader_address", "title"])["roi"].count().reset_index()
    )
    volume_trades_per_trader_and_market.rename(
        columns={"roi": "nr_trades_per_market"}, inplace=True
    )

    trader_agents_data = pd.merge(
        all_trades, volume_trades_per_trader_and_market, on=["trader_address", "title"]
    )
    # right now all traders are of the same type: singlebet
    trader_agents_data["trader_type"] = "singlebet"

    trader_agents_data = trader_agents_data.sort_values(
        by="creation_timestamp", ascending=True
    )

    trader_agents_data["month_year_week"] = (
        trader_agents_data["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d")
    )

    closed_markets["month_year_week"] = (
        closed_markets["opening_datetime"].dt.to_period("W").dt.strftime("%b-%d")
    )
    return trader_agents_data, closed_markets


trader_agents_data, closed_markets = prepare_data()
print("trader agents data before computing metrics")
print(trader_agents_data.head())
demo = gr.Blocks()
# get weekly metrics by market creator: qs, pearl or all.
weekly_metrics_by_market_creator = compute_weekly_metrics_by_market_creator(
    trader_agents_data
)
print("weekly metrics by market creator")
print(weekly_metrics_by_market_creator.head())
# get weekly metrics by trader type: multibet, singlebet or all.
weekly_metrics_by_trader_type = compute_weekly_metrics_by_trader_type(
    trader_agents_data
)
weekly_winning_metrics = compute_winning_metrics_by_trader(
    trader_agents_data=trader_agents_data
)
with demo:
    gr.HTML("<h1>Trader agents monitoring dashboard </h1>")
    gr.Markdown(
        "This app shows the weekly performance of the trader agents in Olas Predict."
    )

    with gr.Tabs():
        with gr.TabItem("🔥Trader Agents Dashboard"):
            with gr.Row():
                gr.Markdown("# Weekly metrics of trader agents by market creator")
            with gr.Row():
                trader_details_selector = gr.Dropdown(
                    label="Select a trader metric",
                    choices=trader_metric_choices,
                    value=default_trader_metric,
                )

            with gr.Row():
                with gr.Column(scale=3):
                    trader_markets_plot = plot_trader_metrics_by_market_creator(
                        metric_name=default_trader_metric,
                        traders_df=weekly_metrics_by_market_creator,
                    )
                with gr.Column(scale=1):
                    trade_details_text = get_metrics_text()

            def update_trader_details(trader_detail):
                return plot_trader_metrics_by_market_creator(
                    metric_name=trader_detail,
                    traders_df=weekly_metrics_by_market_creator,
                )

            trader_details_selector.change(
                update_trader_details,
                inputs=trader_details_selector,
                outputs=trader_markets_plot,
            )

        with gr.TabItem("📉Closed Markets Kullback–Leibler divergence"):
            with gr.Row():
                gr.Markdown(
                    "# Weekly Market Prediction Accuracy for Closed Markets (Kullback-Leibler Divergence)"
                )
            with gr.Row():
                gr.Markdown(
                    "Aka, how much off is the market prediction’s accuracy from the real outcome of the event. Values capped at 20 for market outcomes completely opposite to the real outcome."
                )
            with gr.Row():
                trade_details_text = get_metrics_text()
            with gr.Row():
                with gr.Column(scale=3):
                    kl_div_plot = plot_kl_div_per_market(closed_markets=closed_markets)
                with gr.Column(scale=1):
                    interpretation = get_interpretation_text()

        with gr.TabItem("🎖️Weekly winning trades % per trader"):
            with gr.Row():
                gr.Markdown("# Winning trades percentage from weekly trades by trader")
            with gr.Row():
                metrics_text = get_metrics_text()
            with gr.Row():

                winning_metric = plot_winning_metric_per_trader(weekly_winning_metrics)

demo.queue(default_concurrency_limit=40).launch()