Tick-level Kalshi Historical Data.

Download tick-by-tick order book snapshots & deltas, trades, ticker updates and lifecycle events for every Kalshi market. Parquet files, ready for backtesting.

Python

import pandas as pd
market_ticker = "KXHIGHNY-26OCT15-B68.5"
data_type = "l2"
date = "2026/10/15"

url = f"https://api.kacdata.com/v1/kalshi/{data_type}/{date}/{market_ticker}.parquet"
df = pd.read_parquet(url, storage_options={"api_key": "<YOUR_API_KEY>"})
print(df.head())

Tick-by-tick replay

Every L2 file starts with the start-of-day order book, so you can rebuild the book at any moment of a day from one file.

Redundant collection

Two WebSocket connections record the same feed, reducing the risk of missed messages during network disruptions.

All Kalshi markets

Order books, trades, ticker updates and lifecycle events for every Kalshi market, one Parquet file per market per UTC day.

Data sources

Kalshi

Every Kalshi market, from the Kalshi Trade API WebSocket.

Order book L2 · Trades · Ticker · Lifecycle

All markets since 2026-09-08Docs

Focus on strategy research, leave data infrastructure to us.

Save on server hosting, data storage, and maintenance for an in-house Kalshi data pipeline.

2026-09-08

all Kalshi markets since

datasets: L2, trades, ticker, lifecycle

1 file

per market per UTC day

600

free downloads at sign-up

$49

per month for Plus

Parquet

ready for pandas, Polars and DuckDB

Tick-level Kalshi datasets

Order book snapshots and deltas, trades, ticker updates and market lifecycle events, one Parquet file per market per UTC day.

#	market_ticker	timestamp	local_timestamp	event_type	yes_prices	yes_sizes	no_prices	no_sizes
1	KXHIGHNY-26OCT15-B68.5	1792022400000000	1792022400000000	orderbook_snapshot	[0.4100, 0.4000, ... 0.0100]	[250.00, 1200.00, ... 5000.00]	[0.5700, 0.5600, ... 0.0100]	[310.00, 800.00, ... 4200.00]
2	KXHIGHNY-26OCT15-B68.5	null	1792031553118204	orderbook_snapshot	[0.4200, 0.4100, ... 0.0100]	[90.00, 640.00, ... 5000.00]	[0.5700, 0.5600, ... 0.0100]	[310.00, 820.00, ... 4200.00]

Order book snapshots from the Kalshi WebSocket. Since 2026-09-08

Illustrative rows（占位，待改）

Download Sample See Schema

Full documentation

Developer-centric API

Download all data for a market with a single HTTP request.

Python

import pandas as pd
market_ticker = "KXHIGHNY-26OCT15-B68.5"
data_type = "l2"
date = "2026/10/15"

url = f"https://api.kacdata.com/v1/kalshi/{data_type}/{date}/{market_ticker}.parquet"
df = pd.read_parquet(url, storage_options={"api_key": "<YOUR_API_KEY>"})
print(df.head())

Why KacData?

comprehensive - fair - transparent

Full limit order book reconstruction — Snapshots and signed deltas, with a synthesized start-of-day and end-of-day book in every L2 file.

Raw data at the finest granularity — Every message of the Kalshi WebSocket, with no aggregation or downsampling.

Exact decimals — Prices and sizes are stored as decimal(18,4), so replaying deltas never drifts.

Redundant collection — Dual WebSocket connections reduce the risk of data gaps during network disruptions.

Source timestamps and receive timestamps— Kalshi's own timestamp next to the time KacData received each message.

Simple pricing — Start free with 600 downloads; Plus is $49 per month for all data, including the last 3 weeks.