Schema

19 columns, one row per closed Polymarket trade.

Column	Type	Description
trade_id	int	Sequential 0-indexed trade ID for cross-referencing with the bot's own logs.
market_id	str	Polymarket market ID. Public — queryable via gamma-api.polymarket.com/markets?id=<market_id>.
question	str	The market question text at the time of the trade.
outcome_label	str	The YES/NO outcome the bot bet on. Most rows are Yes (the bot bets on the high-probability side).
entry_time	str (ISO-8601 UTC)	When the crash trigger fired and the bot opened the position.
exit_time	str (ISO-8601 UTC)	When the position closed (sell completed).
entry_price	float (0–1)	Per-share price at entry. Polymarket prices are probabilities.
exit_price	float (0–1)	Per-share price at exit.
pre_crash_high	float (0–1)	The recent local-window high used as the crash reference. The signal fires when current price drops > X% from this high.
drop_pct	float	$(\text{pre\_crash\_high} − \text{entry\_price}) / \text{pre\_crash\_high} \times 100$. Magnitude of the crash.
size_usd	float	USD allocated to the trade (typically $5 in this dataset).
shares	float	Share count purchased = size_usd / entry_price.
hold_hours	float	Wall-clock hours from entry_time to exit_time.
pnl_usd	float	Realized P&L in USD. Theoretical, not slippage-adjusted. Use pnl-truthteller for slippage-adjusted PnL.
is_profitable	int (0/1)	1 if pnl_usd > 0, 0 otherwise. The default classification target.
exit_reason	str	RECOVERY (price came back), TIMEOUT_48H (held 48h, exited at whatever price), TIMEOUT (older shorter-timeout variant), or STOP (hit stop-loss — rare).
entry_hour_utc	int (0–23)	Hour-of-day at entry, UTC.
entry_dow	int (0–6)	Day-of-week at entry. 0 = Monday, 6 = Sunday.
recovered_to_pct_of_high	float	$\text{exit\_price} / \text{pre\_crash\_high} \times 100$. How close to the pre-crash high did the price come back.

Notes on usage

pnl_usd is theoretical, not slippage-adjusted

The bot's internal records compute pnl = (exit_price - entry_price) × shares. This assumes you got every share filled at the listed entry/exit prices. In practice on thin Polymarket books, fills are noisier — the actual on-chain proceeds are typically lower than theoretical. See the methodology doc for context.

If you need slippage-adjusted P&L, the pnl-truthteller tool reconciles bot records against on-chain fills. The aggregate slippage on this dataset is roughly -$120 across 300+ trades, so the bot's lifetime claim of "+$33 theoretical" becomes "-$90 actual" once slippage is included.

RECOVERY vs TIMEOUT_48H

If you're modeling for a binary classifier:

• Use is_profitable (clean 0/1) — most uses.
• If you want a 4-class outcome label, use exit_reason directly.

entry_dow and entry_hour_utc

Trade timing has measurable signal. Markets are thinner overnight UTC (NA/Europe asleep) — slippage is worse, but counter-trend signals also stronger. Try grouping is_profitable by entry_hour_utc to see the U-shape.

market_id

The market ID lets you cross-reference with Polymarket's gamma-api for richer metadata: category, end_date, current odds, etc. Example:

import requests
mkt = requests.get(
    "https://gamma-api.polymarket.com/markets",
    params={"id": "544093"},
).json()
print(mkt[0]["category"], mkt[0]["endDate"])