| # Schema — `crashes_v1.csv` |
| |
| 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 | `(pre_crash_high − entry_price) / pre_crash_high × 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](https://github.com/LuciferForge/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 | `exit_price / pre_crash_high × 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](https://github.com/LuciferForge/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: |
|
|
| ```python |
| import requests |
| mkt = requests.get( |
| "https://gamma-api.polymarket.com/markets", |
| params={"id": "544093"}, |
| ).json() |
| print(mkt[0]["category"], mkt[0]["endDate"]) |
| ``` |
|
|
