KXNDAAKalshiPoliticsOne-time5 open on Nov 15

Will NDAA become law? odds & historical data

Will NDAA become law? price history on Kalshi: 7 markets, every order book change and trade, as daily Parquet files.

Example market: Will legislation designated as, or substantively serving as, the National Defense Authorization Act for Fiscal Year 2027 by authorizing national defense programs or defense policy for that fiscal year become law before Dec 25, 2026?

Full order book on Full book, $74.50/mo. Each dataset below has a sample file, no account needed.

Get this data

260.9 thousand rows7 markets2.43 MB7 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 ts_recvtimestamp[ns, UTC] ts_venuetimestamp[ns, UTC] tickerstring seriesstring seqint64 is_snapshotbool sidestring pricefloat64 sizefloat64	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 tstimestamp[ns, UTC] tickerstring seriesstring bid_px_0float64 bid_px_1float64 bid_px_2float64 bid_px_3float64 bid_px_4float64 bid_px_5float64 bid_px_6float64 bid_px_7float64 bid_px_8float64 bid_px_9float64 bid_sz_0float64 bid_sz_1float64 bid_sz_2float64 bid_sz_3float64 bid_sz_4float64 bid_sz_5float64 bid_sz_6float64 bid_sz_7float64 bid_sz_8float64 bid_sz_9float64 ask_px_0float64 ask_px_1float64 ask_px_2float64 ask_px_3float64 ask_px_4float64 ask_px_5float64 ask_px_6float64 ask_px_7float64 ask_px_8float64 ask_px_9float64 ask_sz_0float64 ask_sz_1float64 ask_sz_2float64 ask_sz_3float64 ask_sz_4float64 ask_sz_5float64 ask_sz_6float64 ask_sz_7float64 ask_sz_8float64 ask_sz_9float64	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 ts_recvtimestamp[ns, UTC] ts_venuetimestamp[ns, UTC] tickerstring seriesstring yes_bidfloat64 yes_bid_sizefloat64 yes_askfloat64 yes_ask_sizefloat64	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 minutetimestamp[ns, UTC] tickerstring seriesstring mid_openfloat64 mid_highfloat64 mid_lowfloat64 mid_closefloat64 yes_bid_closefloat64 yes_ask_closefloat64 book_updatesint64 trade_countint64 trade_volumefloat64	Research	SampleParquet·CSV
Trades trades	Every recorded fill with YES price, size, and taker side. 8 columns ts_recvtimestamp[ns, UTC] ts_venuetimestamp[ns, UTC] tickerstring seriesstring trade_idstring yes_pricefloat64 sizefloat64 taker_sidestring	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 tickerstring event_tickerstring seriesstring titlestring yes_sub_titlestring categorystring market_typestring statusstring resultstring open_tstimestamp[ns, UTC] close_tstimestamp[ns, UTC] expected_expiration_tstimestamp[ns, UTC] floor_strikefloat64 cap_strikefloat64 strike_typestring tick_structurestring price_rangesstring can_close_earlybool fee_typestring fee_multiplierfloat64 rules_primarystring rules_secondarystring	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 tickerstring event_tickerstring seriesstring categorystring resultstring settlement_valuefloat64 expiration_valuestring close_tstimestamp[ns, UTC] settlement_tstimestamp[ns, UTC] ts_recvtimestamp[ns, UTC] floor_strikefloat64 cap_strikefloat64 strike_typestring coin_price_startfloat64 coin_price_endfloat64	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", "2026-11-13", "2026-11-13", series="KXNDAA")

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