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Documentation Index

Fetch the complete documentation index at: https://docs.probalytics.io/llms.txt

Use this file to discover all available pages before exploring further.

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Probalytics

Clean, unified prediction market data. One API. Multiple access methods. Aggregate data from Polymarket, Kalshi, and more into a single normalized dataset. Choose how you access it: REST API, raw SQL, or bulk exports.

REST API

Query via HTTP with auth

SQL (ClickHouse)

Direct database connection

File Downloads

Parquet exports

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The Problem

Prediction market data is fragmented across exchanges with different APIs, formats, and schemas. Polymarket needs blockchain indexing. Kalshi has its own REST API. Building cross-platform analysis means maintaining multiple integrations and handling data inconsistencies.
What we solved:
  • ✓ Unified API across exchanges
  • ✓ Normalized data schema
  • ✓ Historical data back to platform launch
  • ✓ Continuous updates
  • ✓ Multiple access methods for your workflow

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Data Available

Two core datasets, continuously updated:

Markets

Every prediction market question: title, outcomes, category, status, open/close dates, resolution data, current prices

Fills

Every executed trade: price, size, taker side, timestamp, taker/maker IDs
Historical records go back to each platform’s launch. Data refreshes every 5 minutes.

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Supported Exchanges

ExchangeTypeCoverageStatus
PolymarketBlockchain (Polygon)Markets, fills, orderbook snapshotsLive ✓
KalshiUS-regulatedMarkets, fillsLive ✓
PredictItUS-basedMarkets, fillsComing soon
MetaculusForecasting platformQuestions, predictionsComing soon

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Access Methods

Choose what fits your workflow:

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REST API

Query via HTTP with simple authentication. Best for: production applications, quick integrations.
  • Authentication: API key in header
  • Rate limited: 3,000 requests per 10 seconds
  • Response format: JSON

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SQL (ClickHouse)

Direct database connection. Best for: data analysis, batch operations, complex queries, dashboards.
  • Connect from: Python, Node.js, Go, DBeaver, etc.
  • Full SQL support: aggregations, joins, window functions
  • Performance: optimized for analytics

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File Downloads

Bulk Parquet exports. Best for: local analysis, research, backups, data science pipelines.
  • Format: Parquet
  • Frequency: weekly (fills), monthly (markets)
  • See File Downloads

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Use Cases

Trading

Build bots, alerts, dashboards. Track prices across platforms. Detect opportunities.

Research

Market efficiency analysis. Forecast accuracy studies. Information aggregation patterns.

Arbitrage

Find price spreads. Match markets across exchanges. Identify inefficiencies.

Backtesting

Test strategies against historical data. Validate models. Performance analysis.

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Next Steps

Quickstart

Get working code in 2 minutes

SQL Guide

Tables, schemas, queries

File Downloads

Bulk Parquet exports

Tutorials

Real examples: arbitrage, analysis, pipelines

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Need Help?