> ## 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.

# Introduction

> Unified prediction market data platform

# 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, the Python client, or bulk S3 exports.

<CardGroup cols={2}>
  <Card title="REST API" icon="code" href="/docs/quickstart#rest-api">
    Query via HTTP with auth
  </Card>

  <Card title="SQL (ClickHouse)" icon="database" href="/docs/quickstart#sql-clickhouse">
    Direct database connection
  </Card>

  <Card title="Python Client" icon="python" href="/docs/python-client">
    Typed models and dataframes
  </Card>

  <Card title="S3 Data Files" icon="arrow-down" href="/docs/file-downloads/overview">
    Bulk Parquet exports over S3
  </Card>

  <Card title="Status API" icon="code" href="/docs/api-reference/status">
    Query uptime and incidents via HTTP (no auth required)
  </Card>
</CardGroup>

## The Problem

<Warning>
  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.
</Warning>

What we solved:

* ✓ Unified API across exchanges
* ✓ Normalized data schema
* ✓ Dataset-specific historical coverage with source timestamps
* ✓ Continuous updates
* ✓ Multiple access methods for your workflow

## Data Available

Three core datasets, continuously updated:

<CardGroup cols={2}>
  <Card title="Markets" icon="chart-line" href="/docs/datasets/markets">
    Market metadata: title, outcomes, category, status, lifecycle timestamps, and resolution data
  </Card>

  <Card title="Fills" icon="handshake" href="/docs/datasets/fills">
    Recorded executions: price, size, taker side, timestamp, and available participant IDs
  </Card>

  <Card title="Orderbook snapshots" icon="layer-group" href="/docs/datasets/orderbook">
    Full bid/ask depth per outcome for Orderbook and Custom ClickHouse tiers
  </Card>
</CardGroup>

Prediction markets and perpetual instruments share these datasets. Join fills to markets and inspect `market_type` before interpreting units: perpetual prices are instrument prices, not probabilities.

<Tip>
  Coverage depends on dataset, source, product, and access tier. "All retained history" means all retained rows available to the account, not guaranteed coverage back to venue launch. See the [Datasets overview](/docs/datasets/overview) for coverage, normalization rules, and known exclusions.
</Tip>

## Supported Exchanges

| Exchange | Type | Coverage | Status |
| - | - | - | - |
| **Polymarket** | Blockchain (Polygon) | Markets, fills, orderbook snapshots | Live ✓ |
| **Kalshi** | US-regulated | Markets, fills, orderbook snapshots | Live ✓ |

## Access Methods

Choose what fits your workflow:

### REST API

Query via HTTP with simple authentication. Best for: production applications, quick integrations.

* Authentication: API key in header
* Resources: markets and fills
* Operational traffic protections may apply; no fixed REST request quota is currently published
* Response format: JSON

### 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

### S3 Data Files

Bulk Parquet exports over S3. Best for: backfills, local analysis, research, data science pipelines.

* Format: Parquet, downloaded with any S3 client using your API key
* Frequency: daily and weekly (fills), daily (orderbook snapshots), monthly (markets)
* See [S3 Data Files](/docs/file-downloads/overview)

## Use Cases

<CardGroup cols={2}>
  <Card title="Trading" icon="rocket">
    Build bots, alerts, dashboards. Track prices across platforms. Detect opportunities.
  </Card>

  <Card title="Research" icon="microscope">
    Market efficiency analysis. Forecast accuracy studies. Information aggregation patterns.
  </Card>

  <Card title="Arbitrage" icon="arrows-left-right">
    Find price spreads. Match markets across exchanges. Identify inefficiencies.
  </Card>

  <Card title="Backtesting" icon="chart-area">
    Test strategies against historical data. Validate models. Performance analysis.
  </Card>
</CardGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="Quickstart" icon="play" href="/docs/quickstart">
    Get working code in 2 minutes
  </Card>

  <Card title="Datasets" icon="table" href="/docs/datasets/overview">
    Coverage, provenance, normalization, and known limitations
  </Card>

  <Card title="SQL Guide" icon="database" href="/docs/sql-guide/overview">
    Tables, schemas, queries
  </Card>

  <Card title="S3 Data Files" icon="arrow-down" href="/docs/file-downloads/overview">
    Bulk Parquet exports
  </Card>

  <Card title="Common Queries" icon="code" href="/docs/sql-guide/common-queries">
    Query patterns for markets, fills, and orderbooks
  </Card>
</CardGroup>

## Need Help?

* **Quick question?** Check [Troubleshooting](/docs/troubleshooting)
* **Still stuck?** Email [support@probalytics.io](mailto:support@probalytics.io) or join our [Discord](https://discord.gg/probalytics)
* **Found a bug?** Contact [support](/docs/support) with the request, timestamp, and full error


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