Best Prediction Market APIs for Data: Coverage, History, and Resolution Data Compared

Getting prediction market prices is easy.

Getting reliable data across multiple platforms, tracking how probabilities change, and understanding what happens when markets resolve?

That's a different challenge.

Polymarket, Kalshi, Manifold, and other prediction market platforms all expose data through APIs. But their coverage, historical depth, market identifiers, and resolution information can look very different.

For developers, quant researchers, and data teams, choosing the right prediction market API means looking beyond the latest probability.

Let's compare five options and see where each one fits.

Best Prediction Market APIs: Comparison at a Glance

Capabilities reflect published documentation as of October 2026. Coverage and historical availability can differ by venue, market, and dataset.

The biggest distinction is between native exchange APIs and multi-venue prediction market data providers.

Native APIs provide direct access to one platform. Aggregated APIs reduce the work required to collect and standardize data from several platforms.

Neither approach is automatically better. It depends on what you're building.

1. FinFeedAPI: Best for Multi-Venue Data and Historical Research

FinFeedAPI Prediction Markets API is designed for teams that need more than one exchange's prices.

Instead of maintaining separate integrations with Polymarket, Kalshi, and other platforms, developers can access normalized prediction market data through one interface.

Its documented coverage spans nine venues:

• Polymarket
• Kalshi
• Myriad
• Manifold
• Hyperliquid Outcome Markets
• Gemini Prediction Markets
• Pascal
• Crypto.com Prediction Markets
• ForecastEx

This brings together regulated exchanges, decentralized markets, social prediction platforms, and on-chain outcome markets.

What data is available?

FinFeedAPI covers several parts of the prediction market lifecycle.

Market discovery and metadata: Find active markets, retrieve market descriptions, identify outcomes, and inspect market status.

Trades and quotes: Access the latest trade and quote snapshots, recent activity, and historical records.

Order books: Retrieve current bid and ask depth. Historical order book events are available where supported.

OHLCV data: Analyze historical price movement, trading volume, and activity through standardized time-series data.

Historical markets: Retrieve older market records, including closed and resolved markets where supported.

The distinction matters because a probability alone tells you very little about market quality.

A market might show a 70% implied probability, but that doesn't reveal how much liquidity supports the price or how frequently the market trades.

Trades, quotes, and order books add that missing context.

History and resolution tracking

FinFeedAPI separates active market discovery from broader historical queries.

Developers can use /v1/markets/{exchange_id}/active to identify active market IDs, then retrieve metadata for individual markets.

The /v1/markets/{exchange_id}/history endpoint provides access to a broader market universe, including markets with Open, Closed, and Resolved statuses.

For research, that makes it possible to study markets beyond the moment they stop trading.

However, resolution fields and historical availability depend on the underlying exchange. Not every market provides identical settlement information or historical depth.

Interfaces and integration

FinFeedAPI supports three interfaces:

• REST: For applications, dashboards, and data pipelines.
• JSON-RPC: For applications using RPC-style requests.
• MCP: For AI assistants and tool-calling research workflows.

The hosted MCP server is particularly useful for teams building AI applications that need structured access to exchanges, markets, historical prices, and liquidity data.

FinFeedAPI provides current and latest data through request-response endpoints. It should not be confused with a native WebSocket streaming feed.

Best for: Multi-exchange analytics, historical research, backtesting, data engineering, and AI-agent applications that need standardized prediction market data.

2. Polymarket API: Best for Direct Polymarket Integration

Polymarket provides its own APIs for accessing events, markets, prices, order books, and trading activity.

For developers building specifically around Polymarket, going directly to the source can be a practical choice.

Polymarket separates several functions across its API infrastructure.

The Gamma API handles event discovery and market metadata. The CLOB API provides prices, order books, and trading functionality. The Data API exposes account activity and market participation information.

WebSocket channels provide continuous updates for applications that require streaming market data.

Where Polymarket works well

Its native APIs are a strong fit for live Polymarket dashboards, trading tools, wallet analytics, and applications that need direct access to individual outcome tokens.

Developers can also access historical prices and market information.

However, Polymarket's event, market, and outcome identifiers must be handled correctly. A single event may contain multiple markets, and each tradable outcome has its own identifier.

The limitation is coverage.

If an application later needs Kalshi, ForecastEx, or other venues, another integration becomes necessary.

Best for: Applications focused entirely on Polymarket, especially those needing native streaming data or trading functionality.

3. Kalshi API: Best for Direct Kalshi Market Data

Kalshi operates a regulated event-contract exchange and provides developer APIs for accessing its markets.

Its API supports market discovery, prices, order books, trades, and historical market information.

For applications focused specifically on Kalshi, the native integration offers direct access to the exchange's own market and settlement data.

Historical data and settlement

Kalshi supports historical trades, market candlesticks, and archived markets.

One important detail is how it separates historical data from current records.

Older markets and trading records can move into dedicated historical endpoints. Developers retrieving complete histories need to account for the transition between live and archived datasets.

Kalshi also exposes market settlement information.

For research teams studying forecasting accuracy, the final outcome is just as important as the probability before settlement.

A market that traded at 80% before resolving NO tells a very different story from one that ultimately resolved YES.

Where Kalshi works well

Kalshi offers REST and WebSocket access, along with FIX connectivity for supported trading workflows.

That makes it useful for both market-data applications and teams building directly against Kalshi's exchange infrastructure.

Like Polymarket, its native API does not solve the problem of gathering comparable data across unrelated venues.

Best for: Kalshi-specific applications, regulated event-market research, settlement analysis, and direct exchange integrations.

4. Prediction.com API: Best for Cross-Platform Market Matching

Prediction.com API, developed by Prediction Hunt, takes a different approach.

It focuses on accessing market data across venues and identifying equivalent markets trading on different platforms.

This is useful when developers want to compare prices for the same underlying event.

For example, a particular election outcome might trade on both Polymarket and Kalshi, but under different names and identifiers.

Prediction.com's matching tools help connect those markets.

Data coverage

The API provides market discovery, current prices, order books, historical price data, and cross-platform matching.

It also supports WebSocket streams and an MCP server.

Historical OHLC candles are available, although their construction differs by venue. Kalshi candles use native trading history, while some other venue candles are calculated from captured order book prices and may not include trading volume.

That's an important distinction for quantitative users.

A historical midpoint series is not necessarily equivalent to a trade-derived OHLCV dataset.

Its unified trade feed also has narrower venue coverage than its broader market catalog.

Best for: Cross-exchange price comparison, market matching, arbitrage research, and applications that need multi-venue streaming information.

5. Manifold API: Best for Social Prediction Market Research

Manifold is different from traditional event-contract exchanges.

Its platform focuses on community forecasting, including social and play-money prediction markets.

The Manifold API provides access to markets, probabilities, bets, user activity, and resolution information.

This makes it useful for applications studying how groups of people update their beliefs over time.

What makes Manifold different?

Markets can cover questions that might never appear on regulated financial exchanges.

Some are objective, such as whether a specific event will occur by a deadline.

Others involve subjective judgments, personal predictions, or community-created outcomes.

Resolution is also different. Market creators generally determine the result, subject to platform moderation and rules.

For research, that creates an interesting dataset, but it also means outcomes require different interpretation from exchange-settled financial contracts.

Best for: Social forecasting research, community prediction tools, and applications built specifically around Manifold.

What Actually Matters When Choosing a Prediction Market Data API?

Provider coverage is the obvious starting point.

But several less visible differences can have a much bigger impact once an application reaches production.

1. Coverage is more than the number of exchanges

An API might advertise broad market coverage but offer different datasets for each venue.

One exchange may provide complete trade history. Another may expose only current prices or limited historical records.

Before choosing a provider, check whether your required venues support the exact data types you need.

For cross-market research, consistency matters more than the number of logos on a provider's website.

2. Historical data needs to go beyond price charts

Consider two datasets.

The first contains hourly probability snapshots.

The second contains historical trades, bid/ask quotes, volume, market status, and settlement information.

Both count as prediction market data, but they support very different research.

For backtesting, model development, and liquidity analysis, historical trades and quotes may reveal information that aggregated price history misses.

Researchers should also check when the history begins, whether inactive markets remain accessible, and how missing records are handled.

3. Closed is not the same as resolved

This distinction is especially important for prediction markets.

An open market is still trading. A closed market has stopped accepting trades. A resolved market has had its outcome determined.

Closing does not automatically mean that the outcome is known.

Imagine a market asking whether Bitcoin will exceed a particular price by December 31.

You need to know what the market expected during trading, when it closed, and how the event ultimately resolved.

Without that final information, a dataset is incomplete for accuracy analysis.

A good API should allow researchers to recover market lifecycle information and, where available, the final outcome or settlement record.

4. Liquidity matters as much as probability

A prediction market probability is a market price, not a guarantee that the event will occur.

And prices can become misleading when trading activity is low.

Imagine two platforms pricing the same outcome at 65%.

One market has meaningful trading volume and a narrow bid/ask spread.

The other has barely traded for hours.

Those two observations shouldn't carry equal weight in a research model.

Access to trades, quotes, order books, and volume helps teams evaluate how much confidence to place in a market's price.

5. Market identifiers and timestamps can make or break a pipeline

Prediction markets are built around events and outcomes.

A question such as "Will Bitcoin reach $150,000 this year?" may have separate YES and NO instruments.

Those instruments need distinct identifiers.

When you add more venues, you also need to manage exchange-specific market IDs, lifecycle changes, and potentially different contract structures.

Timestamps deserve similar attention.

The time an exchange created a quote may differ from the time a data provider received or processed it.

For serious historical analysis, those differences matter. They affect data freshness, event sequencing, and the ability to reconstruct what information was available at a particular moment.

Which Prediction Market API Should You Choose?

The right answer depends on how the data will be used.

For a single-exchange application, Polymarket's or Kalshi's native APIs make sense. They provide direct access to their respective platforms and include streaming functionality for applications that need it.

For cross-platform price comparison, Prediction.com offers market matching and streaming tools designed around equivalent events on different venues.

For social forecasting research, Manifold provides access to a different category of prediction markets.

For multi-exchange historical analysis, FinFeedAPI is worth considering when your requirements extend beyond probability snapshots into trades, quotes, OHLCV, order books, and market lifecycle data.

And for AI-agent builders, the choice often comes down to whether the application needs raw exchange-specific interfaces or standardized tools that can query multiple data sources.

Frequently Asked Questions

Can prediction market APIs provide historical resolution data?

Yes, some APIs expose closed or resolved markets, final outcomes, and settlement information.

However, support varies by provider and venue. Market status alone should not be treated as confirmation that a final outcome or payout value is available.

Is a Polymarket API enough for prediction market research?

For Polymarket-only research, it may be.

But researchers comparing Kalshi, Polymarket, and other venues generally need additional integrations or a multi-venue data provider.

The same applies to projects requiring standardized historical datasets across different market structures.

Build With FinFeedAPI Prediction Markets API

Prediction market research needs more than a list of probabilities.

FinFeedAPI brings market discovery, trades, quotes, OHLCV, order books, and historical market data together through one standardized API.

Explore the FinFeedAPI Prediction Markets API, review the documentation and start building with the data your application needs.

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