scripts/market_metrics.py

import numpy as np
import pandas as pd
import time
from utils import convert_hex_to_int, ROOT_DIR, TMP_DIR


def determine_market_status(row):
    current_answer = row["currentAnswer"]
    """Determine the market status of a trade."""
    if (current_answer is np.nan or current_answer is None) and time.time() >= int(
        row["openingTimestamp"]
    ):
        return "pending"
    if current_answer is np.nan or current_answer is None:
        return "open"
    if row["fpmm.isPendingArbitration"]:
        return "arbitrating"
    if row["fpmm.answerFinalizedTimestamp"] and time.time() < int(
        row["fpmm.answerFinalizedTimestamp"]
    ):
        return "finalizing"
    return "closed"


def compute_market_metrics(all_trades: pd.DataFrame):
    print("Preparing dataset")
    all_trades.rename(
        columns={
            "fpmm.currentAnswer": "currentAnswer",
            "fpmm.openingTimestamp": "openingTimestamp",
            "fpmm.id": "market_id",
        },
        inplace=True,
    )
    all_trades["currentAnswer"] = all_trades["currentAnswer"].apply(
        lambda x: convert_hex_to_int(x)
    )
    all_trades["market_status"] = all_trades.apply(
        lambda x: determine_market_status(x), axis=1
    )
    closed_trades = all_trades.loc[all_trades["market_status"] == "closed"]
    print("Computing metrics")
    nr_trades = (
        closed_trades.groupby("market_id")["id"].count().reset_index(name="nr_trades")
    )
    total_traders = (
        closed_trades.groupby("market_id")["trader_address"]
        .nunique()
        .reset_index(name="total_traders")
    )
    final_dataset = nr_trades.merge(total_traders, on="market_id")
    markets = closed_trades[
        ["market_id", "title", "market_creator", "openingTimestamp"]
    ]
    markets.drop_duplicates("market_id", inplace=True)
    market_metrics = markets.merge(final_dataset, on="market_id")
    print("Saving dataset")
    market_metrics.to_parquet(ROOT_DIR / "closed_market_metrics.parquet", index=False)
    print(market_metrics.head())


if __name__ == "__main__":
    all_trades = pd.read_parquet(TMP_DIR / "fpmmTrades.parquet")
    compute_market_metrics(all_trades)