vgregoire/polymarket-users

Polymarket Users

Trading activity, profits, and behavioral features for every user on Polymarket, the largest on-chain prediction market. The dataset spans from Polymarket's launch on 2022-11-11 to 2026-03-29 and covers all reconciled end-user trades, daily mark-to-market PnL, and a wide set of user-level behavioral features. Built from on-chain CTF Exchange events on Polygon.

This is the research dataset behind:

Akey, P., Grégoire, V., Harvie, N., & Martineau, C. (2026). Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket. Working Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6443103

Documentation: https://www.vincentgregoire.com/polymarket-users-data

Disclaimer. This is an independent academic research dataset. The authors are not affiliated with, endorsed by, or sponsored by Polymarket. "Polymarket" is a trademark of its respective owner; it is referenced here only to identify the source platform of the underlying public on-chain data.

Quick start

With polars (recommended — fast, lazy, scans Hive partitions natively)

import polars as pl

# Markets metadata (one row per market)
markets = pl.read_parquet("markets.parquet")

# Wide one-row-per-user terminal PnL across four variants
users = pl.read_parquet("user_pnl_summary.parquet")
print(users.sort("pnl_total", descending=True).head(10))

# Sparse daily PnL, Hive-partitioned by year/month/day
pnl = pl.scan_parquet("pnl_daily/**/*.parquet", hive_partitioning=False)

With the datasets library

Install it first:

pip install datasets
# or with uv:
uv add datasets

from datasets import load_dataset

markets = load_dataset("vgregoire/polymarket-users", "markets")
events = load_dataset("vgregoire/polymarket-users", "events")
user_pnl = load_dataset("vgregoire/polymarket-users", "user_pnl_summary")

Dataset structure

The dataset is a collection of related tables. Each row of the table below maps to a config_name in the loader interface above.

Metadata

Subset	Layout	Rows per	Description
markets	markets.parquet	market	Per-market metadata including category (classifier-derived), parent event, resolution outcome, fee flags, and lifecycle timestamps.
events	events.parquet	event	Per-event metadata: tag-based category, number of constituent questions, total trading volume.
predictions	predictions.parquet	conditional token	Per-token lookup mapping prediction_id → parent market_id, outcome label and index, number of outcomes, the complementary token id, and the resolution flag. Useful for joining trades back to market metadata at the token granularity.

Per-user features and terminal PnL

Subset	Layout	Rows per	Description
user_features	user_features.parquet	user	Full-sample behavioral feature vector: trade counts, volume, maker/taker share, holding durations, category concentration, distribution metrics, etc. (~83 columns)
user_pnl_summary	user_pnl_summary.parquet	user	Wide terminal PnL across five variants — base, resolved-only, no-fee, spread-adjusted, spread-adjusted resolved-only — each with a total and a per-category breakdown over seven categories (Sports, Crypto, Finance, Politics, Tech, Culture, Weather). The base variant also carries portfolio_value (mark-to-market value of open positions) and usdc_balance (cash account) so the realized/unrealized split is recoverable.

Daily PnL panels

Subset	Layout	Notes
pnl_daily	pnl_daily/year=YYYY/month=MM/day=DD/data.parquet	Sparse delta encoding: one row per (user, day) where PnL changed. To reconstruct a dense daily series, forward-fill from the last observation.
pnl_daily_resolved	pnl_daily_resolved/year=YYYY/month=MM/day=DD/data.parquet	Same as pnl_daily, restricted to markets that resolved on or before the sample end.
pnl_daily_no_fee	pnl_daily_no_fee/year=YYYY/month=MM/day=DD/data.parquet	Same restricted to markets with no taker fees (predates the Q4 2024 fee introduction).
pnl_daily_resolved_no_fee	pnl_daily_resolved_no_fee/year=YYYY/month=MM/day=DD/data.parquet	Intersection of the two filters above.
pnl_category_daily	pnl_category_daily/year=YYYY/month=MM/day=DD/data.parquet	As above but split by market category. Markets without a category label are excluded.
pnl_category_daily_resolved	pnl_category_daily_resolved/year=YYYY/month=MM/day=DD/data.parquet	Same as pnl_category_daily, restricted to markets that resolved on or before the sample end. Needed to reproduce the resolved-only variants of paper-profits exhibits (concentration, spread decomposition, probit) without inflating the per-(user, category) denominator with users who had no resolved positions in that category.
pnl_category_daily_no_fee	pnl_category_daily_no_fee/year=YYYY/month=MM/day=DD/data.parquet	Same restricted to markets with no taker fees (predates the Q4 2024 fee introduction). Companion of the no-fee variant of paper-profits exhibits.
pnl_category_daily_resolved_no_fee	pnl_category_daily_resolved_no_fee/year=YYYY/month=MM/day=DD/data.parquet	Intersection of the two filters above.

PnL change panels

These store the daily/monthly delta in user PnL (pnl_change), not the level. Useful for return-style analyses where you'd otherwise have to first-difference the level series yourself.

Subset	Layout	Rows per	Notes
pnl_change_daily	pnl_change_daily/year=YYYY/month=MM/day=DD/data.parquet	(user, day)	Per-user daily PnL change.
pnl_change_monthly	pnl_change_monthly.parquet	(user, month)	Per-user monthly aggregation of the same.

For a single point-in-time terminal PnL per user (no forward-filling needed), use user_pnl_summary above — it carries the same values plus all four variants and the portfolio_value / usdc_balance decomposition.

Trade-level data

Subset	Layout	Notes
trades	trades/year=YYYY/month=MM/day=DD/data.parquet	All reconciled end-user trades, one parquet per day. End-user maker/taker addresses are recovered from the CTF Exchange OrderFilled event stream. Schema: trade_id, timestamp, market_id, event_id, prediction_id, outcome, winner, category, category_original, price, quantity, maker_address, taker_address, taker_bought.
ohlcv_1d	ohlcv_1d.parquet	Per-token daily OHLCV bars (open, high, low, close, volume, trade count) plus daily open interest.
ohlcv_1h	ohlcv_1h/year=YYYY/month=MM/day=DD/data.parquet	Per-token hourly OHLCV bars, day-partitioned. No open interest column (no matching position cache).
ohlcv_5m	ohlcv_5m/year=YYYY/month=MM/day=DD/data.parquet	Per-token 5-minute OHLCV bars, day-partitioned. No open interest column.

Forward-filling sparse PnL

The pnl_daily and pnl_category_daily tables are delta-encoded — they only contain rows where PnL changed on that day. To reconstruct a dense daily panel:

import polars as pl
from datetime import date

sparse = pl.scan_parquet("pnl_daily/**/*.parquet", hive_partitioning=False)

# Date grid: every day of the sample period
grid = pl.LazyFrame({
    "snapshot_time": pl.date_range(date(2022, 11, 11), date(2026, 3, 29), "1d", eager=True)
})

# Cross-join users × dates, then asof-join the sparse data
users = sparse.select("user_address").unique()
dense = (
    users.join(grid, how="cross")
    .sort("user_address", "snapshot_time")
    .join_asof(
        sparse.sort("user_address", "snapshot_time"),
        on="snapshot_time",
        by="user_address",
    )
    .collect()
)

Sample, scope, and provenance

• Source: Public on-chain data from Polygon. Reconciled from OrderFilled events emitted by the CTF Exchange contract. End-user identification uses Polymarket's proxy/safe wallet pattern.
• Sample period: 2022-11-11 — 2026-03-29 (UTC).
• Universe: All Polymarket markets, including binary and multi-outcome. Wash trading is detected (via counterparty HHI) but not filtered out of the base PnL — see the paper for the methodology, and use the resolved variant of user_pnl_summary if you want to restrict to fully-settled markets.
• PnL methodology: Mark-to-market portfolio_value + usdc_balance from the reconstructed cash account. The spread_adj variants in user_pnl_summary net out a fixed half-spread on each fill (paper default 0.005, i.e., half the 1¢ tick).

Time convention

All timestamps are in UTC, and all daily / monthly bucketing is anchored at midnight UTC. Two labelling conventions are used across panels:

Event panels — natural timestamps. trades and ohlcv_* rows are timestamped at the moment the event occurred (block timestamp) or at the start of the bar (OHLCV). A row with timestamp = 2025-06-15 12:00 UTC is activity on the calendar day 2025-06-15, and the Hive partition path matches (day=15).

Snapshot panels — +1 day right-boundary. pnl_daily, the three pnl_daily_* filtered variants, pnl_category_daily and its three variants, pnl_change_daily, and pnl_change_monthly all label rows with the right boundary of the period they summarize. A label X 00:00 UTC means "state or change up to (but not including) that boundary" — i.e., the close of day X − 1.

Column	A label X 00:00 UTC means…
pnl_daily.snapshot_time (and category / variant siblings)	State at the close of day X − 1
pnl_change_daily.day	Change accumulated during day X − 1
pnl_change_monthly.month	Sum of pnl_change_daily rows whose day falls in calendar month X (those daily values are themselves +1-shifted)

The Hive partition path always matches the column value. The convention is chosen for compatibility with polars.join_asof against a daily price grid keyed at midnight UTC. Worked example: a user whose first trade is at 2025-06-15 18:18 UTC first appears in pnl_daily at snapshot_time = 2025-06-16 00:00 UTC (Hive partition day=16) and in pnl_change_daily at day = 2025-06-16 00:00 UTC.

Citation

@unpublished{akey2026prediction,
  title  = {Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket},
  author = {Akey, Pat and Gr{\'e}goire, Vincent and Harvie, Nicolas and Martineau, Charles},
  note   = {Working Paper},
  year   = {2026},
  url    = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6443103}
}

License

The processed data in this release — reconciled end-user trades, computed PnL panels and summaries, classifier-derived market categories, behavioral user features, and OHLCV aggregates — is released under CC-BY 4.0. Use, modify, and redistribute freely, including commercially; please cite the paper.

Scope. CC-BY 4.0 covers the authors' contribution: cleaning, reconciliation, classification, computation, and curation. It does not cover fields that originate from the Polymarket API (e.g., market question text, descriptions, slugs, raw platform tags, lifecycle timestamps). Those fields are included for convenience and reproducibility but remain subject to Polymarket's terms of use. Users who plan to redistribute those fields should consult Polymarket's terms directly.

No warranty. As CC-BY 4.0 §5 states, this dataset is provided "AS IS" without warranty of any kind, express or implied.

Changelog

• v1.0 (2026-05-19) — Initial release. Sample 2022-11-11 → 2026-03-29.