Best Prediction Market Data APIs: What to Compare Before You Build
The best API is not the one with the longest endpoint list. It is the one whose history, timestamps, book depth and delivery model match the decision your system has to reproduce.
A prediction market data API should be compared on six things: venue coverage, live delivery, historical order-book depth, timestamp quality, settlement fields and usable export formats. Official APIs are usually the source of truth for current markets and trading, while a continuously recorded third-party archive is required when a backtest must reconstruct the spread, resting size and slippage that existed in the past.
The comparison that matters
| Requirement | Minimum useful evidence | Why it matters |
|---|---|---|
| Live research | Current market, trade and full-book updates | A midpoint alone hides spread and available size |
| Backtesting | Timestamped historical bid/ask ladders | A price series cannot reproduce depth-aware fills |
| Cross-venue work | One documented normalized schema | Polymarket and Kalshi expose different native book shapes |
| Auditability | Source and receive timestamps plus missing-data rules | Latency and gaps must remain measurable |
| Bulk analysis | REST pagination and CSV or Parquet export | Large studies should not depend on one request per row |
| Production use | Published limits, authentication and retry behavior | A prototype endpoint is not an operating contract |
Official API versus recorded archive
Polymarket and Kalshi publish their own developer surfaces, and those official interfaces should remain the reference for current market definitions, venue rules and supported trading workflows. They are the correct starting point when an application needs the current state of the venue.
Historical depth is a different product. A full order book is transient: once levels change, a later price-history point cannot reveal the size that disappeared, the spread that was paid or the price impact of an order. A provider claiming realistic replay should show how it captured, normalized and retained those ladders.
How DepthFeed fits the comparison
DepthFeed is purpose-built for research across Polymarket and Kalshi. It serves recorded full-depth books, normalized timestamps, underlying reference fields and settlement-aware market records through REST, with live WebSocket channels for current books. The same schema is used by the browser Backtest Lab and the API.
The product does not turn missing observations into synthetic fills. Coverage stays missing, venue-specific capture is documented, and researchers can test a rule under midpoint, fixed-slippage and depth-aware VWAP assumptions before sending a survivor to paper trading.
A practical selection workflow
- Write down whether the application needs current state, historical replay, or both.
- Request one real market sample with bids, asks, sizes and timestamps before comparing price.
- Verify the earliest usable date and the missing-data policy for each venue.
- Run the same sized order through the ladder instead of comparing only midpoints.
- Confirm export formats, pagination, rate limits and authentication with a small integration.
- Keep venue trading APIs separate from a research archive unless the provider documents both roles.
Key takeaways
- 01Current official APIs and continuously recorded historical archives solve different problems.
- 02Historical price points are not a substitute for historical order-book depth.
- 03Timestamp provenance and an explicit missing-data policy are core comparison criteria.
- 04A useful evaluation replays a sized order, not only a midpoint.
- 05DepthFeed unifies Polymarket and Kalshi research data without inventing absent observations.
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