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  1. README.md +272 -1
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README.md CHANGED
@@ -1,3 +1,274 @@
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  ---
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- license: cc-by-sa-4.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - en
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+ license: cc-by-4.0
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+ pretty_name: Polymarket Users
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+ tags:
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+ - prediction-markets
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+ - polymarket
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+ - finance
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+ - cryptocurrency
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+ - user-behavior
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+ configs:
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+ - config_name: markets
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+ data_files:
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+ - split: train
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+ path: markets.parquet
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+ - config_name: events
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+ data_files:
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+ - split: train
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+ path: events.parquet
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+ - config_name: predictions
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+ data_files:
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+ - split: train
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+ path: predictions.parquet
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+ - config_name: user_features
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+ data_files:
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+ - split: train
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+ path: user_features.parquet
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+ - config_name: user_pnl_summary
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+ data_files:
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+ - split: train
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+ path: user_pnl_summary.parquet
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+ - config_name: pnl_daily
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+ data_files:
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+ - split: train
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+ path: pnl_daily/**/*.parquet
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+ - config_name: pnl_daily_resolved
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+ data_files:
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+ - split: train
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+ path: pnl_daily_resolved/**/*.parquet
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+ - config_name: pnl_daily_no_fee
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+ data_files:
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+ - split: train
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+ path: pnl_daily_no_fee/**/*.parquet
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+ - config_name: pnl_daily_resolved_no_fee
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+ data_files:
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+ - split: train
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+ path: pnl_daily_resolved_no_fee/**/*.parquet
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+ - config_name: pnl_change_daily
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+ data_files:
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+ - split: train
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+ path: pnl_change_daily/**/*.parquet
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+ - config_name: pnl_change_monthly
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+ data_files:
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+ - split: train
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+ path: pnl_change_monthly.parquet
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+ - config_name: pnl_category_daily
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+ data_files:
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+ - split: train
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+ path: pnl_category_daily/**/*.parquet
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+ - config_name: pnl_category_daily_resolved
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+ data_files:
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+ - split: train
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+ path: pnl_category_daily_resolved/**/*.parquet
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+ - config_name: pnl_category_daily_no_fee
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+ data_files:
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+ - split: train
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+ path: pnl_category_daily_no_fee/**/*.parquet
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+ - config_name: pnl_category_daily_resolved_no_fee
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+ data_files:
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+ - split: train
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+ path: pnl_category_daily_resolved_no_fee/**/*.parquet
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+ - config_name: trades
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+ data_files:
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+ - split: train
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+ path: trades/**/*.parquet
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+ - config_name: ohlcv_1d
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+ data_files:
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+ - split: train
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+ path: ohlcv_1d.parquet
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+ - config_name: ohlcv_1h
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+ data_files:
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+ - split: train
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+ path: ohlcv_1h/**/*.parquet
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+ - config_name: ohlcv_5m
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+ data_files:
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+ - split: train
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+ path: ohlcv_5m/**/*.parquet
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  ---
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+
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+ # Polymarket Users
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+
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+ Trading activity, profits, and behavioral features for every user on
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+ [Polymarket](https://polymarket.com), the largest on-chain prediction market.
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+ The dataset spans from Polymarket's launch on **2022-11-11** to **2026-03-29**
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+ and covers all reconciled end-user trades, daily mark-to-market PnL, and a
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+ wide set of user-level behavioral features. Built from on-chain CTF Exchange
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+ events on Polygon.
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+
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+ This is the research dataset behind:
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+
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+ > Akey, P., Grégoire, V., Harvie, N., & Martineau, C. (2026).
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+ > *Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket.*
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+ > Working Paper. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6443103>
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+
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+ **Documentation:** <https://www.vincentgregoire.com/polymarket-users-data>
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+
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+ > **Disclaimer.** This is an independent academic research dataset. The
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+ > authors are not affiliated with, endorsed by, or sponsored by
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+ > Polymarket. "Polymarket" is a trademark of its respective owner; it is
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+ > referenced here only to identify the source platform of the underlying
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+ > public on-chain data.
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+
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+ ## Quick start
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+
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+ ### With `polars` (recommended — fast, lazy, scans Hive partitions natively)
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+
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+ ```python
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+ import polars as pl
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+
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+ # Markets metadata (one row per market)
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+ markets = pl.read_parquet("markets.parquet")
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+
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+ # Wide one-row-per-user terminal PnL across four variants
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+ users = pl.read_parquet("user_pnl_summary.parquet")
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+ print(users.sort("pnl_total", descending=True).head(10))
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+
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+ # Sparse daily PnL, Hive-partitioned by year/month/day
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+ pnl = pl.scan_parquet("pnl_daily/**/*.parquet", hive_partitioning=False)
130
+ ```
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+
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+ ### With the `datasets` library
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+
134
+ ```python
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+ 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
+ ```
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+
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
+ |---|---|---|---|
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+ | `markets` | `markets.parquet` | market | Per-market metadata including category (classifier-derived), parent event, resolution outcome, fee flags, and lifecycle timestamps. |
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+ | `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. |
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+ | `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.
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+
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+ | Subset | Layout | Rows per | Notes |
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+ |---|---|---|---|
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+ | `pnl_change_daily` | `pnl_change_daily/year=YYYY/month=MM/day=DD/data.parquet` | `(user, day)` | Per-user daily PnL change. |
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+ | `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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