Lowe's Annual KPI historical data
Lowe's Annual KPI price history on Kalshi: 20 markets, every order book change and trade, as daily Parquet files.
Example market: Will Lowe's Companies Inc. report Above 760 million retail customer transactions in fiscal 2026?
72.8 thousand rows20 markets646 KB6 datasets, full order book includedUpdated daily
Datasets
One file per dataset per UTC day. Data dictionary · Hourly coverage
| Dataset | What it holds | Plan | Sample |
|---|---|---|---|
| Order book (L2, incremental) book_l2 | Every price level and every change to the YES and NO bid books. Snapshot rows set a level; delta rows add to it. 9 columns
| Full book | SampleParquet·CSV |
| Order book snapshots (top 10, 1 second) book_snap_1s | The top ten YES bid and ask levels at the end of every second in which the book changed. 43 columns
| Research | SampleParquet·CSV |
| Top of book (every change) bbo | Best YES bid and ask with sizes, one row each time any of the four values changes. 8 columns
| Research | SampleParquet·CSV |
| Top of book (1-minute bars) bbo_1m | Minute bars of the YES mid with closing bid and ask, plus trade count and volume. 12 columns
| Research | SampleParquet·CSV |
| Markets (point-in-time universe) markets | One row per market open at any time during the day: terms, strikes, times, tick size, rules, fees, and outcome. 22 columns
| Explorer | SampleParquet·CSV |
| Settlements settlements | Every market that settled during the day: result, settlement value, the underlying value it settled on, and when. 15 columns
| Explorer | SampleParquet·CSV |
Use it in code
API docs · MCP server · Backtest guide · Historical data guide
# pip install "kalshieye[pandas]" from kalshieye import Client kx = Client() # reads KALSHIEYE_API_KEY df = kx.load("bbo_1m", "2026-10-27", "2026-10-27", series="KXLOWA")