The most granular Kalshi Historical Data.

Download tick-by-tick order book snapshots & deltas, trades and lifecycle events. Colocated collection, ready for backtesting.

Python

import pandas as pd
market_ticker = "KXBTC15M-26OCT150930-30"
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

Reconstruct state of the limit order book at any given past moment in time across all markets.

API access and Parquet files

Get historical tick data with a single HTTP request as Parquet files.

Low latency

Collected in AWS us-east-2, colocated with Kalshi. Minimal timestamp error via AWS PHC. 6 ms median latency.

Focus on strategy research, leave data infrastructure to us.

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

665M+

available individual trade data points

5M+

stored markets

690GB+

of high-frequency historical market data

Since 2026-09

high-frequency data history

15K+

WebSocket messages collected per second

WebSocket connections per market

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	delta_price	delta_size	delta_side	yes_prices	yes_sizes	no_prices	no_sizes
1	KXBTC15M-26OCT150930-30	null	1792070101063667	orderbook_snapshot	null	null	null	[0.5300, 0.5200, ... 0.0010]	[0.0200, 7.1000, ... 4187501.0000]	[0.4600, 0.4500, ... 0.0010]	[2022.3400, 1079.9100, ... 4011468.0000]
2	KXBTC15M-26OCT150930-30	1792070101023000	1792070101063686	orderbook_delta	0.4500	-5.0000	no	null	null	null	null
3	KXBTC15M-26OCT150930-30	1792070101023000	1792070101063687	orderbook_delta	0.4200	5.0000	no	null	null	null	null
4	KXBTC15M-26OCT150930-30	1792070101023000	1792070101063689	orderbook_delta	0.4600	-30.4500	no	null	null	null	null
5	KXBTC15M-26OCT150930-30	1792070101029000	1792070101063735	orderbook_delta	0.4800	1.0000	yes	null	null	null	null
6	KXBTC15M-26OCT150930-30	1792070101029000	1792070101063736	orderbook_delta	0.4100	1.0000	no	null	null	null	null
7	KXBTC15M-26OCT150930-30	1792070101034000	1792070101063788	orderbook_delta	0.4300	-1.0000	no	null	null	null	null
8	KXBTC15M-26OCT150930-30	1792070101038000	1792070101063852	orderbook_delta	0.5100	-39.0000	yes	null	null	null	null

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

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 = "KXBTC15M-26OCT150930-30"
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

Colocated data collection — Low-latency collection with minimal timestamp error via AWS PHC, built for high-frequency trading research and backtesting.

Full limit order book reconstruction — Level 2 snapshots and incremental updates to reconstruct the complete order book.

Raw data at the finest granularity — Original market data with no aggregation, downsampling, or processing.

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