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- events.parquet +3 -0
- markets.parquet +3 -0
- ohlcv_1d.parquet +3 -0
- pnl_change_monthly.parquet +3 -0
- pnl_daily_resolved_no_fee/year=2024/month=01/day=03/data.parquet +3 -0
- pnl_daily_resolved_no_fee/year=2024/month=01/day=06/data.parquet +3 -0
- pnl_daily_resolved_no_fee/year=2024/month=01/day=09/data.parquet +3 -0
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- pnl_daily_resolved_no_fee/year=2024/month=01/day=22/data.parquet +3 -0
- pnl_daily_resolved_no_fee/year=2024/month=01/day=26/data.parquet +3 -0
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- pnl_daily_resolved_no_fee/year=2024/month=11/day=06/data.parquet +3 -0
- pnl_daily_resolved_no_fee/year=2024/month=11/day=07/data.parquet +3 -0
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- pnl_daily_resolved_no_fee/year=2024/month=11/day=30/data.parquet +3 -0
- predictions.parquet +3 -0
- user_features.parquet +3 -0
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| 1 |
---
|
| 2 |
+
language:
|
| 3 |
+
- en
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| 4 |
+
license: cc-by-4.0
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| 5 |
+
pretty_name: Polymarket Users
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| 6 |
+
tags:
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| 7 |
+
- prediction-markets
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| 8 |
+
- polymarket
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| 9 |
+
- finance
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| 10 |
+
- cryptocurrency
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| 11 |
+
- user-behavior
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| 12 |
+
configs:
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| 13 |
+
- config_name: markets
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| 14 |
+
data_files:
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| 15 |
+
- split: train
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+
path: markets.parquet
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| 17 |
+
- config_name: events
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+
data_files:
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| 19 |
+
- split: train
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+
path: events.parquet
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+
- config_name: predictions
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| 22 |
+
data_files:
|
| 23 |
+
- split: train
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| 24 |
+
path: predictions.parquet
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| 25 |
+
- config_name: user_features
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| 26 |
+
data_files:
|
| 27 |
+
- split: train
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| 28 |
+
path: user_features.parquet
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| 29 |
+
- config_name: user_pnl_summary
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| 30 |
+
data_files:
|
| 31 |
+
- split: train
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| 32 |
+
path: user_pnl_summary.parquet
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| 33 |
+
- config_name: pnl_daily
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| 34 |
+
data_files:
|
| 35 |
+
- split: train
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| 36 |
+
path: pnl_daily/**/*.parquet
|
| 37 |
+
- config_name: pnl_daily_resolved
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| 38 |
+
data_files:
|
| 39 |
+
- split: train
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| 40 |
+
path: pnl_daily_resolved/**/*.parquet
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| 41 |
+
- config_name: pnl_daily_no_fee
|
| 42 |
+
data_files:
|
| 43 |
+
- split: train
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| 44 |
+
path: pnl_daily_no_fee/**/*.parquet
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| 45 |
+
- config_name: pnl_daily_resolved_no_fee
|
| 46 |
+
data_files:
|
| 47 |
+
- split: train
|
| 48 |
+
path: pnl_daily_resolved_no_fee/**/*.parquet
|
| 49 |
+
- config_name: pnl_change_daily
|
| 50 |
+
data_files:
|
| 51 |
+
- split: train
|
| 52 |
+
path: pnl_change_daily/**/*.parquet
|
| 53 |
+
- config_name: pnl_change_monthly
|
| 54 |
+
data_files:
|
| 55 |
+
- split: train
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| 56 |
+
path: pnl_change_monthly.parquet
|
| 57 |
+
- config_name: pnl_category_daily
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| 58 |
+
data_files:
|
| 59 |
+
- split: train
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| 60 |
+
path: pnl_category_daily/**/*.parquet
|
| 61 |
+
- config_name: pnl_category_daily_resolved
|
| 62 |
+
data_files:
|
| 63 |
+
- split: train
|
| 64 |
+
path: pnl_category_daily_resolved/**/*.parquet
|
| 65 |
+
- config_name: pnl_category_daily_no_fee
|
| 66 |
+
data_files:
|
| 67 |
+
- split: train
|
| 68 |
+
path: pnl_category_daily_no_fee/**/*.parquet
|
| 69 |
+
- config_name: pnl_category_daily_resolved_no_fee
|
| 70 |
+
data_files:
|
| 71 |
+
- split: train
|
| 72 |
+
path: pnl_category_daily_resolved_no_fee/**/*.parquet
|
| 73 |
+
- config_name: trades
|
| 74 |
+
data_files:
|
| 75 |
+
- split: train
|
| 76 |
+
path: trades/**/*.parquet
|
| 77 |
+
- config_name: ohlcv_1d
|
| 78 |
+
data_files:
|
| 79 |
+
- split: train
|
| 80 |
+
path: ohlcv_1d.parquet
|
| 81 |
+
- config_name: ohlcv_1h
|
| 82 |
+
data_files:
|
| 83 |
+
- split: train
|
| 84 |
+
path: ohlcv_1h/**/*.parquet
|
| 85 |
+
- config_name: ohlcv_5m
|
| 86 |
+
data_files:
|
| 87 |
+
- split: train
|
| 88 |
+
path: ohlcv_5m/**/*.parquet
|
| 89 |
---
|
| 90 |
+
|
| 91 |
+
# Polymarket Users
|
| 92 |
+
|
| 93 |
+
Trading activity, profits, and behavioral features for every user on
|
| 94 |
+
[Polymarket](https://polymarket.com), the largest on-chain prediction market.
|
| 95 |
+
The dataset spans from Polymarket's launch on **2022-11-11** to **2026-03-29**
|
| 96 |
+
and covers all reconciled end-user trades, daily mark-to-market PnL, and a
|
| 97 |
+
wide set of user-level behavioral features. Built from on-chain CTF Exchange
|
| 98 |
+
events on Polygon.
|
| 99 |
+
|
| 100 |
+
This is the research dataset behind:
|
| 101 |
+
|
| 102 |
+
> Akey, P., Grégoire, V., Harvie, N., & Martineau, C. (2026).
|
| 103 |
+
> *Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket.*
|
| 104 |
+
> Working Paper. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6443103>
|
| 105 |
+
|
| 106 |
+
**Documentation:** <https://www.vincentgregoire.com/polymarket-users-data>
|
| 107 |
+
|
| 108 |
+
> **Disclaimer.** This is an independent academic research dataset. The
|
| 109 |
+
> authors are not affiliated with, endorsed by, or sponsored by
|
| 110 |
+
> Polymarket. "Polymarket" is a trademark of its respective owner; it is
|
| 111 |
+
> referenced here only to identify the source platform of the underlying
|
| 112 |
+
> public on-chain data.
|
| 113 |
+
|
| 114 |
+
## Quick start
|
| 115 |
+
|
| 116 |
+
### With `polars` (recommended — fast, lazy, scans Hive partitions natively)
|
| 117 |
+
|
| 118 |
+
```python
|
| 119 |
+
import polars as pl
|
| 120 |
+
|
| 121 |
+
# Markets metadata (one row per market)
|
| 122 |
+
markets = pl.read_parquet("markets.parquet")
|
| 123 |
+
|
| 124 |
+
# Wide one-row-per-user terminal PnL across four variants
|
| 125 |
+
users = pl.read_parquet("user_pnl_summary.parquet")
|
| 126 |
+
print(users.sort("pnl_total", descending=True).head(10))
|
| 127 |
+
|
| 128 |
+
# Sparse daily PnL, Hive-partitioned by year/month/day
|
| 129 |
+
pnl = pl.scan_parquet("pnl_daily/**/*.parquet", hive_partitioning=False)
|
| 130 |
+
```
|
| 131 |
+
|
| 132 |
+
### With the `datasets` library
|
| 133 |
+
|
| 134 |
+
```python
|
| 135 |
+
from datasets import load_dataset
|
| 136 |
+
|
| 137 |
+
markets = load_dataset("vgregoire/polymarket-users", "markets")
|
| 138 |
+
events = load_dataset("vgregoire/polymarket-users", "events")
|
| 139 |
+
user_pnl = load_dataset("vgregoire/polymarket-users", "user_pnl_summary")
|
| 140 |
+
```
|
| 141 |
+
|
| 142 |
+
## Dataset structure
|
| 143 |
+
|
| 144 |
+
The dataset is a collection of related tables. Each row of the table below
|
| 145 |
+
maps to a `config_name` in the loader interface above.
|
| 146 |
+
|
| 147 |
+
### Metadata
|
| 148 |
+
|
| 149 |
+
| Subset | Layout | Rows per | Description |
|
| 150 |
+
|---|---|---|---|
|
| 151 |
+
| `markets` | `markets.parquet` | market | Per-market metadata including category (classifier-derived), parent event, resolution outcome, fee flags, and lifecycle timestamps. |
|
| 152 |
+
| `events` | `events.parquet` | event | Per-event metadata: tag-based category, number of constituent questions, total trading volume. |
|
| 153 |
+
| `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. |
|
| 154 |
+
|
| 155 |
+
### Per-user features and terminal PnL
|
| 156 |
+
|
| 157 |
+
| Subset | Layout | Rows per | Description |
|
| 158 |
+
|---|---|---|---|
|
| 159 |
+
| `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) |
|
| 160 |
+
| `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. |
|
| 161 |
+
|
| 162 |
+
### Daily PnL panels
|
| 163 |
+
|
| 164 |
+
| Subset | Layout | Notes |
|
| 165 |
+
|---|---|---|
|
| 166 |
+
| `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. |
|
| 167 |
+
| `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. |
|
| 168 |
+
| `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). |
|
| 169 |
+
| `pnl_daily_resolved_no_fee` | `pnl_daily_resolved_no_fee/year=YYYY/month=MM/day=DD/data.parquet` | Intersection of the two filters above. |
|
| 170 |
+
| `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. |
|
| 171 |
+
| `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. |
|
| 172 |
+
| `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. |
|
| 173 |
+
| `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. |
|
| 174 |
+
|
| 175 |
+
### PnL change panels
|
| 176 |
+
|
| 177 |
+
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.
|
| 178 |
+
|
| 179 |
+
| Subset | Layout | Rows per | Notes |
|
| 180 |
+
|---|---|---|---|
|
| 181 |
+
| `pnl_change_daily` | `pnl_change_daily/year=YYYY/month=MM/day=DD/data.parquet` | `(user, day)` | Per-user daily PnL change. |
|
| 182 |
+
| `pnl_change_monthly` | `pnl_change_monthly.parquet` | `(user, month)` | Per-user monthly aggregation of the same. |
|
| 183 |
+
|
| 184 |
+
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.
|
| 185 |
+
|
| 186 |
+
### Trade-level data
|
| 187 |
+
|
| 188 |
+
| Subset | Layout | Notes |
|
| 189 |
+
|---|---|---|
|
| 190 |
+
| `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`, `question_id`, `event_id`, `prediction_id`, `outcome`, `winner`, `category`, `category_original`, `price`, `quantity`, `maker_address`, `taker_address`, `taker_bought`. |
|
| 191 |
+
| `ohlcv_1d` | `ohlcv_1d.parquet` | Per-token daily OHLCV bars (open, high, low, close, volume, trade count) plus daily open interest. |
|
| 192 |
+
| `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). |
|
| 193 |
+
| `ohlcv_5m` | `ohlcv_5m/year=YYYY/month=MM/day=DD/data.parquet` | Per-token **5-minute** OHLCV bars, day-partitioned. No open interest column. |
|
| 194 |
+
|
| 195 |
+
### Forward-filling sparse PnL
|
| 196 |
+
|
| 197 |
+
The `pnl_daily` and `pnl_category_daily` tables are delta-encoded — they only
|
| 198 |
+
contain rows where PnL changed on that day. To reconstruct a dense daily
|
| 199 |
+
panel:
|
| 200 |
+
|
| 201 |
+
```python
|
| 202 |
+
import polars as pl
|
| 203 |
+
from datetime import date
|
| 204 |
+
|
| 205 |
+
sparse = pl.scan_parquet("pnl_daily/**/*.parquet", hive_partitioning=False)
|
| 206 |
+
|
| 207 |
+
# Date grid: every day of the sample period
|
| 208 |
+
grid = pl.LazyFrame({
|
| 209 |
+
"snapshot_time": pl.date_range(date(2022, 11, 11), date(2026, 3, 29), "1d", eager=True)
|
| 210 |
+
})
|
| 211 |
+
|
| 212 |
+
# Cross-join users × dates, then asof-join the sparse data
|
| 213 |
+
users = sparse.select("user_address").unique()
|
| 214 |
+
dense = (
|
| 215 |
+
users.join(grid, how="cross")
|
| 216 |
+
.sort("user_address", "snapshot_time")
|
| 217 |
+
.join_asof(
|
| 218 |
+
sparse.sort("user_address", "snapshot_time"),
|
| 219 |
+
on="snapshot_time",
|
| 220 |
+
by="user_address",
|
| 221 |
+
)
|
| 222 |
+
.collect()
|
| 223 |
+
)
|
| 224 |
+
```
|
| 225 |
+
|
| 226 |
+
## Sample, scope, and provenance
|
| 227 |
+
|
| 228 |
+
- **Source:** Public on-chain data from Polygon. Reconciled from
|
| 229 |
+
`OrderFilled` events emitted by the [CTF Exchange](https://github.com/Polymarket/ctf-exchange)
|
| 230 |
+
contract. End-user identification uses Polymarket's proxy/safe wallet pattern.
|
| 231 |
+
- **Sample period:** 2022-11-11 — 2026-03-29 (UTC).
|
| 232 |
+
- **Universe:** All Polymarket markets, including binary and multi-outcome.
|
| 233 |
+
Wash trading is detected (via counterparty HHI) but **not** filtered out of
|
| 234 |
+
the base PnL — see the paper for the methodology, and use the `resolved`
|
| 235 |
+
variant of `user_pnl_summary` if you want to restrict to fully-settled markets.
|
| 236 |
+
- **PnL methodology:** Mark-to-market `portfolio_value + usdc_balance` from
|
| 237 |
+
the reconstructed cash account. The `spread_adj` variants in
|
| 238 |
+
`user_pnl_summary` net out a fixed half-spread on each fill (paper
|
| 239 |
+
default 0.005, i.e., half the 1¢ tick).
|
| 240 |
+
|
| 241 |
+
## Citation
|
| 242 |
+
|
| 243 |
+
```bibtex
|
| 244 |
+
@unpublished{akey2026prediction,
|
| 245 |
+
title = {Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket},
|
| 246 |
+
author = {Akey, Pat and Gr{\'e}goire, Vincent and Harvie, Nicolas and Martineau, Charles},
|
| 247 |
+
note = {Working Paper},
|
| 248 |
+
year = {2026},
|
| 249 |
+
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6443103}
|
| 250 |
+
}
|
| 251 |
+
```
|
| 252 |
+
|
| 253 |
+
## License
|
| 254 |
+
|
| 255 |
+
The processed data in this release — reconciled end-user trades, computed
|
| 256 |
+
PnL panels and summaries, classifier-derived market categories, behavioral
|
| 257 |
+
user features, and OHLCV aggregates — is released under
|
| 258 |
+
[**CC-BY 4.0**](https://creativecommons.org/licenses/by/4.0/). Use, modify,
|
| 259 |
+
and redistribute freely, including commercially; please cite the paper.
|
| 260 |
+
|
| 261 |
+
**Scope.** CC-BY 4.0 covers the authors' contribution: cleaning,
|
| 262 |
+
reconciliation, classification, computation, and curation. It does **not**
|
| 263 |
+
cover fields that originate from the Polymarket API (e.g., market question
|
| 264 |
+
text, descriptions, slugs, raw platform tags, lifecycle timestamps). Those
|
| 265 |
+
fields are included for convenience and reproducibility but remain subject to
|
| 266 |
+
[Polymarket's terms of use](https://polymarket.com/tos). Users who plan to
|
| 267 |
+
redistribute those fields should consult Polymarket's terms directly.
|
| 268 |
+
|
| 269 |
+
**No warranty.** As CC-BY 4.0 §5 states, this dataset is provided "AS IS"
|
| 270 |
+
without warranty of any kind, express or implied.
|
| 271 |
+
|
| 272 |
+
## Changelog
|
| 273 |
+
|
| 274 |
+
- **v1.0** (2026-05-19) — Initial release. Sample 2022-11-11 → 2026-03-29.
|
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