KXTRUMPDIVIDENDKalshiPoliticsOne-time2 open on Nov 3

Will Trump issue dividend payments in 2026? odds & historical data

Will Trump issue dividend payments in 2026? price history on Kalshi: 2 markets, every order book change and trade, as daily Parquet files.

Example market: Will it be reported by any of the Source Agencies that at least 1,000,000 people have each been issued at least $100 in payments under the same new federal program for the general public presented by the Trump administration as a dividend, rebate, or equivalent return of government revenue or savings before Apr 1, 2027?

Sample: Nov 1 Get this data

330 thousand rows2 markets4.03 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", "2026-11-01", "2026-11-01", series="KXTRUMPDIVIDEND")

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