Built for
performance

Multiple ways to access your data. Choose what fits your workflow — from real-time streams to direct SQL queries.

Real-time Streaming

Server-Sent Events

Subscribe to real-time updates via SSE. Prices, trades, orderbook changes, and market resolutions — streamed the instant they happen. No polling, no WebSocket complexity.

Sub-10ms latency from source to your application
Filter by platform, market, or event type
Automatic reconnection with event replay

GET /v1/stream?platforms=polymarket,kalshi

Event Stream Connected

priceBTC $100k0.720ms ago

tradeElection 2024$1,24012ms ago

priceETH $5k0.4528ms ago

REST API

Simple, powerful API

RESTful endpoints for markets, trades, orderbooks, and historical data. Consistent response format across all platforms. Paginated, filterable, and fast.

Unified schema — same format across all platforms
Filter by platform, category, date range, volume
OpenAPI spec with generated client libraries

GET/v1/markets

200 OK

{
  "data": [
    {
      "id": "poly_0x1234...",
      "platform": "polymarket",
      "question": "BTC above $100k?",
      "yes_price": 0.72,
      "volume_24h": 1240000,
      "end_date": "2024-12-31"
    }
  ],
  "meta": { "total": 12847, "page": 1 }
}

Bulk Data Access

Direct S3 access

For large-scale analysis and ML pipelines, access complete historical datasets directly from S3. Parquet files partitioned by date and platform for efficient queries.

Parquet format — works with Spark, DuckDB, Pandas
Daily snapshots with hourly incremental updates
Pre-signed URLs or bring your own AWS credentials

s3://probalytics-data/

markets/

trades/

trades/polymarket/2024/

2024-12-01.parquet

847 MB

2024-12-02.parquet

912 MB

2024-12-03.parquet

891 MB

SQL Access

Query with SQL

Connect directly to our ClickHouse cluster from your browser or any SQL client. Run complex analytical queries on billions of rows in milliseconds.

In-browser SQL console — no setup required
Connect via HTTPS or native ClickHouse protocol
Sub-second queries across all historical data

DBeaverDataGripMetabaseGrafana

SQL Console

ClickHouse

SELECT
  market_id,
  platform,
  AVG(price) as avg_price,
  COUNT(*) as trade_count
FROM trades
WHERE timestamp > now() - INTERVAL 1 HOUR
GROUP BY market_id, platform
ORDER BY trade_count DESC
LIMIT 10

10 rows · 23ms

Ready to get started?

Start building with unified prediction market data today.