prediction markets api (2026)

A prediction markets API gives developers programmatic access to event-contract data and, in many cases, order execution on platforms like Polymarket and Kalshi. Some APIs handle trading directly; others aggregate odds, prices, and volume across multiple venues for dashboards, bots, and research tools. Picking the right one depends on whether you need to place trades, pull historical data, or track live probabilities across platforms at once.

What a prediction markets API actually does

Prediction markets let people trade on the outcome of real-world events: elections, Fed rate decisions, sports results, economic data releases. Each contract trades between $0 and $1 (or $0 and 100), and the price reflects the market’s implied probability of the event happening. An API exposes that data, and sometimes trading functions, so developers don’t have to scrape a website or use a manual trading interface.

Broadly, these APIs fall into three categories:

• Trading APIs from the exchanges themselves (Polymarket, Kalshi), which let you place orders, manage positions, and redeem winnings.
• Analytics or data APIs that expose market metadata, order book depth, and historical price series without execution.
• Aggregation APIs (like Dome) that unify data and, in some cases, trading across multiple platforms through a single integration.

Polymarket API

Polymarket runs on Polygon and offers the largest crypto-native prediction market by volume. Its documentation covers a developer quickstart, core concepts (markets, events, order books, positions, resolution), and SDKs in multiple languages. A basic flow looks like this: create a client, list open markets, fetch the first page of results, then place or manage orders against a specific market’s order book.

Polymarket’s API is well suited for teams building directly on its liquidity, especially anything involving on-chain settlement, wallet-based auth, or CLOB (central limit order book) mechanics. It’s less useful if your product needs data from Kalshi or other venues too, since you’d need a separate integration for each.

Kalshi API

Kalshi is a CFTC-regulated exchange, which makes it the main option for US-based, dollar-settled event contracts without crypto rails. Its API supports market data retrieval and order placement, with authentication tied to a registered Kalshi account. Because Kalshi operates under direct US regulatory oversight, it’s the more common choice for products that need to stay clearly within US commodities law rather than operating through offshore or crypto-based structures.

The tradeoff is coverage: Kalshi’s contract catalog and Polymarket’s don’t fully overlap, so an application that wants both regulated US contracts and Polymarket’s broader event coverage needs to either integrate both APIs separately or use an aggregator.

Aggregator APIs: one integration, multiple venues

Aggregators sit on top of exchange APIs and normalize the data. Dome, for example, matches identical markets across Polymarket and Kalshi, then exposes them through one interface with consistent formatting, real-time streaming, and historical data going back further than some exchange-native endpoints allow.

This matters for two practical reasons:

• Format differences. Each exchange returns odds, volume, and resolution data differently. Writing separate parsing logic for each platform adds maintenance overhead every time an exchange changes its schema.
• Cross-platform arbitrage or comparison. If the same event (say, a Fed rate decision) trades on both Kalshi and Polymarket, an aggregator lets you compare implied probabilities side by side without juggling two SDKs and two auth systems.

The cost is a dependency layer: you’re relying on the aggregator to stay in sync with upstream changes, and any downtime or lag on their end affects your app even if the underlying exchange is fine.

How to evaluate a prediction markets API

Before committing to one, check these areas directly against the documentation rather than marketing copy.

Data depth and history

Confirm how far back historical price and volume data goes, and whether it’s available via REST, bulk export, or only through a paid tier. Some APIs only expose live order book snapshots with no historical archive.

WebSocket and streaming support

For anything involving live odds (a betting dashboard, an alert bot, a trading strategy), polling a REST endpoint every few seconds is inefficient and can hit rate limits fast. Look for a WebSocket feed with documented reconnect behavior.

Authentication and sandbox access

Trading APIs typically require account verification, API keys tied to a funded account, and scoped permissions. Check whether a sandbox or paper-trading environment exists so you can test order logic without risking real funds.

Rate limits and cost

Free tiers usually cap requests per minute and may throttle or block WebSocket connections. If your product needs to poll many markets simultaneously, model your expected call volume against the published limits before building.

Compliance and geography

Kalshi operates as a regulated US exchange, so its terms are built around US users. Polymarket has, at various points, restricted US retail access to its trading functions depending on jurisdiction and product changes, so US-based builders should read the current terms of service rather than assume account creation is open. This is not legal advice; confirm current restrictions directly with each platform before building a product around them.

Comparing the main options

Common use cases

• Trading bots that place or adjust orders based on price movement or news triggers.
• Odds dashboards for elections, economic indicators, or sports that pull live probability data.
• Research tools that analyze historical accuracy of prediction markets against actual outcomes.
• News and media integrations that embed live market odds alongside coverage of an event.
• Arbitrage detection across platforms when the same event trades at different implied probabilities.

Getting started

Start with a narrow test: pick one market type (a single Fed meeting, a single election race), pull it from the API you’re evaluating, and check the response against the platform’s own website in real time. Confirm timestamps, price precision, and how resolution/settlement data gets reported once an event closes. That single-market test will surface formatting quirks, rate limit behavior, and documentation gaps faster than reading the docs alone.