Orderbook Snapshots

Historical orderbook data,
unmatched granularity

Replay any Polymarket orderbook at any point in time. 200-500M snapshots per day, down to the millisecond.

Get Started

200-500M

Updates per day

1ms

Resolution (LOCF)

Nov 2025

Data available from

Parquet

Export format

Granularity

100x more granular than the competition

Other providers capture millions of updates. We capture hundreds of millions — every day.

Orderbook Data Comparison

Provider	Updates / Day	Resolution	History	Format
ProbalyticsBest	200-500M	1ms (LOCF)	Nov 2025+	Parquet + SQL
Dome	2-3M	Lower	Limited	API only
DIY scraping	Varies	Seconds	Self-managed	Raw JSON

Data Schema

Full orderbook depth at every tick

Each snapshot captures the complete bid and ask sides of the orderbook for a given market outcome. Interpolated via LOCF at 1-millisecond resolution so there are never gaps in your data.

Download Parquet files via REST API for any market and time range
Query orderbook_snapshots table directly via ClickHouse SQL
LOCF interpolation at 1ms resolution — no gaps in your data

SQL Console

ClickHouse

SELECT
  market_id,
  outcome,
  bids,
  asks,
  timestamp
FROM orderbook_snapshots
WHERE market_id = '...'
  AND timestamp BETWEEN
    '2026-03-01' AND '2026-03-02'
ORDER BY timestamp
LIMIT 100

100 rows · 45ms Run Query

Parquet Downloads

Download snapshots as Parquet

Use the REST API to download orderbook snapshots for any market and time range. Returns a compressed Parquet file ready for analysis with Pandas, Polars, DuckDB, or Spark.

download.py

import requests
import pandas as pd

# Download orderbook snapshots as Parquet
res = requests.get(
  'https://api.probalytics.io/api/v1/orderbook-snapshots/download',
  headers={'Authorization': 'Bearer api_xxx:sk_xxx'},
  params={
    'market_id': '...',
    'start': '2026-03-01T00:00:00Z',
    'end': '2026-03-02T00:00:00Z',
  }
)

df = pd.read_parquet(io.BytesIO(res.content))
print(df.shape)  # (500000+, 5)

Use Cases

What you can do with granular orderbook data

Backtesting

Replay exact orderbook state at any millisecond to backtest strategies with realistic slippage and fill simulation.

Market Microstructure

Study bid-ask spread dynamics, liquidity depth, and order flow patterns across prediction markets.

Liquidity Analysis

Track how liquidity evolves around events, news, and market resolutions at the most granular level.

ML Feature Engineering

Build rich feature sets from orderbook depth, imbalance ratios, and spread dynamics for predictive models.

Coverage

Platform availability

Polymarket

Live

High quality from Feb 2026+

Nov 2025

Kalshi

Soon

Coming soon

—

Orderbook Data

Ready to explore orderbook data?

Access the most granular historical orderbook data for prediction markets.

Also explore: Market data·Trade data

Historical orderbook data,unmatched granularity