README.md

---
language:
  - en
license: cc-by-4.0
pretty_name: Polymarket Users
tags:
  - prediction-markets
  - polymarket
  - finance
  - cryptocurrency
  - user-behavior
configs:
  - config_name: markets
    data_files:
      - split: train
        path: markets.parquet
  - config_name: events
    data_files:
      - split: train
        path: events.parquet
  - config_name: predictions
    data_files:
      - split: train
        path: predictions.parquet
  - config_name: user_features
    data_files:
      - split: train
        path: user_features.parquet
  - config_name: user_pnl_summary
    data_files:
      - split: train
        path: user_pnl_summary.parquet
  - config_name: pnl_daily
    data_files:
      - split: train
        path: pnl_daily/**/*.parquet
  - config_name: pnl_daily_resolved
    data_files:
      - split: train
        path: pnl_daily_resolved/**/*.parquet
  - config_name: pnl_daily_no_fee
    data_files:
      - split: train
        path: pnl_daily_no_fee/**/*.parquet
  - config_name: pnl_daily_resolved_no_fee
    data_files:
      - split: train
        path: pnl_daily_resolved_no_fee/**/*.parquet
  - config_name: pnl_change_daily
    data_files:
      - split: train
        path: pnl_change_daily/**/*.parquet
  - config_name: pnl_change_monthly
    data_files:
      - split: train
        path: pnl_change_monthly.parquet
  - config_name: pnl_category_daily
    data_files:
      - split: train
        path: pnl_category_daily/**/*.parquet
  - config_name: pnl_category_daily_resolved
    data_files:
      - split: train
        path: pnl_category_daily_resolved/**/*.parquet
  - config_name: pnl_category_daily_no_fee
    data_files:
      - split: train
        path: pnl_category_daily_no_fee/**/*.parquet
  - config_name: pnl_category_daily_resolved_no_fee
    data_files:
      - split: train
        path: pnl_category_daily_resolved_no_fee/**/*.parquet
  - config_name: trades
    data_files:
      - split: train
        path: trades/**/*.parquet
  - config_name: ohlcv_1d
    data_files:
      - split: train
        path: ohlcv_1d.parquet
  - config_name: ohlcv_1h
    data_files:
      - split: train
        path: ohlcv_1h/**/*.parquet
  - config_name: ohlcv_5m
    data_files:
      - split: train
        path: ohlcv_5m/**/*.parquet
---

# Polymarket Users

Trading activity, profits, and behavioral features for every user on
[Polymarket](https://polymarket.com), 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)

```python
import polars as pl

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

# Wide one-row-per-user terminal PnL across five 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:

```bash
pip install datasets
# or with uv:
uv add datasets
```

```python
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. (87 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 five 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:

```python
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](https://github.com/Polymarket/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

```bibtex
@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**](https://creativecommons.org/licenses/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](https://polymarket.com/tos). 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

- **v1.3** (2026-07-06) — Corrected the two directional tail-price
  features `frac_longshot` and `frac_sureshot` in `user_features`. In
  v1.0–v1.2 both columns were shipped as byte-identical copies of
  `frac_extreme_price` — the two-tail share of a user's fills at an
  extreme price (`price < 0.10` **or** `price > 0.90`) — because a
  placeholder in the feature-engineering code aliased both to the
  two-tail measure instead of splitting the tails. They are now computed
  as documented: `frac_longshot` is the share of a user's fills at
  `price < 0.10` (the long-shot / low-priced side) and `frac_sureshot`
  the share at `price > 0.90` (the sure-shot / high-priced side), so that
  `frac_longshot + frac_sureshot = frac_extreme_price` for every user.
  `frac_extreme_price` itself was always correct and is unchanged, as is
  every other column in `user_features` and every other table. None of
  the accompanying paper's exhibits use the two corrected columns (they
  use only `frac_extreme_price`), so no published results change. With
  thanks to **Marcos Cardozo (Universidad Católica del Uruguay)** for
  identifying and carefully documenting the issue.

- **v1.2** (2026-06-14) — Removed 16 maker/taker terminal-PnL columns
  (`pnl_maker_total`, `pnl_taker_total`, and their per-category siblings)
  from `user_pnl_summary`. These were shipped undocumented in v1.1 and the
  per-category role columns did not recombine to the base per-category
  totals for users who acted as both maker and taker (≈17.5% of users), so
  they have been withdrawn. The five documented variants (base, resolved,
  no-fee, spread-adjusted, spread-adjusted resolved-only), their
  per-category breakdowns, and the `portfolio_value` / `usdc_balance`
  decomposition are unchanged. No other table is affected. A user's
  maker-vs-taker activity is still fully recoverable from the `trades`
  table (each row carries `maker_address` and `taker_address`); see the
  [documentation](https://www.vincentgregoire.com/polymarket-users-data)
  recipes.

- **v1.1** (2026-06-01) — Corrected PnL panels and per-category breakdowns.
  Every PnL-related table (`pnl_daily`, the three `pnl_daily_*` variants,
  `pnl_category_daily` and its three variants, `pnl_change_daily`,
  `pnl_change_monthly`, and `user_pnl_summary`) was rebuilt from corrected
  position snapshots. Sample period and the other panels (`markets`,
  `events`, `predictions`, `trades`, `user_features`, `ohlcv_*`) are
  unchanged from v1.0.

  **What changed and why:**

  1. **Phantom-settlement bug fixed.** A zero-position row was being
     dropped before settlement logic ran, which caused round-trip trades
     on already-resolved markets (buy + sell back within the same day) to
     produce a ghost USDC settlement for a position the user no longer
     held. The fix moves the zero-row filter to *after* settlement
     decisions. Effect on `pnl_daily` (base): aggregate user-population
     terminal PnL moved from \$114,798 to exactly \$0 (zero-sum
     restored); the median user's terminal PnL moved by \$0.0007; top-5
     winners' PnL is bit-identical to 6 decimals.

  2. **Analysis variants now filter at the market level.** The
     `pnl_daily_resolved`, `pnl_daily_no_fee`, `pnl_daily_resolved_no_fee`
     variants (and their `pnl_category_daily_*` counterparts) previously
     applied the variant predicate only at PnL aggregation time, which
     produced asymmetric USDC leakage of \$1–5 B per variant. The
     variants are now built from positions that exclude trades on markets
     outside the variant's subset, so each variant is structurally
     zero-sum within its subset (modulo platform-collected fees and
     non-user counterparties). Effect: each variant's aggregate terminal
     PnL moved by +\$1 B to +\$4.7 B, from large negative leakage totals
     down to ≈ \$0.

  3. **Sample composition.** A small number of users (≤ 0.05 % of the
     2.48 M user base) drop in or out of the panel depending on which
     variant subset they touch. For the variants, user counts drop by
     54 k–127 k as users who only traded outside the variant's market
     subset now correctly disappear from the panel.

  The headline statistics that appear in the accompanying paper round to
  the same values as in v1.0 — top 1 % capture 76.5 % of profits, top
  0.1 % capture 51.2 %, ~70 % of users lose money — but the underlying
  panels are now internally consistent and pass a whole-cache
  `maker_pnl + taker_pnl = base_pnl` invariant across all
  2.26 billion daily snapshots.

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