Prediction Market Data API Handbook: Tracking Polymarket, Kalshi, and Outcome Feeds

Prediction markets are becoming a useful alternative data source for traders, analysts, AI applications, and event-driven research.

Instead of showing only what has already happened, these markets show how participants are pricing possible future outcomes.

But the data is fragmented.

Polymarket, Kalshi, Myriad, Manifold, Hyperliquid Outcome Markets, Gemini, Pascal, and Crypto.com can have different market structures, identifiers, trading mechanics, and data formats.

A modern prediction market API helps solve this problem by turning separate outcome feeds into structured data that developers can use for research, monitoring, and analytics.

FinFeedAPI’s Prediction Markets API provides latest and historical prediction market data across these venues through REST, JSON-RPC, and MCP.

How Prediction Markets Work

Prediction markets trade contracts tied to real-world outcomes.

A market might ask:

• Will a candidate win an election?
• Will an economic indicator reach a certain level?
• Will a policy decision happen before a specific date?
• Will a company complete a particular event?

Many markets use binary YES/NO outcomes.

If a YES contract trades around 0.62, that price can often be interpreted as roughly a 62% market-implied probability, depending on the contract structure, liquidity, fees, and other market conditions.

That does not mean the event has a guaranteed 62% probability of happening.

It means that is where the market is currently pricing the outcome.

For systematic research, the change in that price can be even more interesting than the latest value.

Prices Are Only One Part of the Data

A serious prediction market pipeline should not collect only the latest probability.

A price move becomes more useful when you can examine:

• trades
• quotes
• volume
• bid-ask spreads
• OHLCV
• order book depth
• historical activity
• activity across other venues

Imagine a market moving from 45% to 60%.

Was that caused by one small trade?

Did volume increase sharply?

Did the spread tighten?

Did similar markets on other exchanges move too?

Those questions require deeper outcome markets data.

Why Prediction Market Data Is Difficult to Integrate

There is no universal prediction market data model.

A direct Polymarket API integration is different from a Kalshi API integration. Other venues introduce their own identifiers, market structures, and data formats.

That creates several engineering problems:

• different exchange identifiers
• different market IDs
• different endpoint structures
• different market metadata
• different quote and order book representations
• different historical formats
• different contract and resolution rules

For one dashboard, maintaining custom integrations may be manageable.

For a trading system, research platform, AI application, or alternative data product, maintaining many connectors becomes much harder.

This is where normalization matters.

Normalizing Polymarket, Kalshi, and Other Outcome Markets

FinFeedAPI provides a normalized data layer across supported prediction market venues.

Current coverage includes:

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

Instead of building separate data infrastructure around every venue, applications can use consistent interfaces for discovering exchanges and markets and retrieving market activity, OHLCV, and order book data.

FinFeedAPI supports:

AI agents and MCP-compatible applications

This gives teams flexibility without forcing the analytics layer to understand every venue individually.

Core Prediction Market Data

A useful prediction market API should provide several layers of information.

Market Metadata

Before comparing prices, you need to understand what is actually being traded.

Market data can include exchange information, market identifiers, active markets, and other contract metadata.

This is especially important when comparing venues.

Two markets may appear to cover the same event while using different wording, deadlines, or resolution conditions.

Always compare the contract definitions before comparing the prices.

Trades and Quotes

Trades show transactions that actually occurred.

Quotes show prices currently available in the market.

Suppose the last trade occurred at 0.58, but the current market is:

1Bid: 0.62
2Ask: 0.64

Using only the last trade could give you a stale picture.

For research and monitoring, trades and quotes are often more useful together.

OHLCV

OHLCV gives developers standardized time-series data:

• Open
• High
• Low
• Close
• Volume

This makes prediction market data easier to chart, analyze, and backtest using tools already common in quantitative finance.

A researcher can measure how an event probability changed over one minute, one hour, one day, or another supported period without reconstructing every move manually.

Order Books

Order books show liquidity around an outcome.

They can help answer questions such as:

• How much size supports the current price?
• Is the spread widening?
• Is liquidity disappearing?
• Did the probability move on meaningful volume?
• Are traders repricing the event across several price levels?

FinFeedAPI provides current bids and asks for supported markets as well as historical order book updates.

One technical detail matters here: historical order book data represents raw order updates, not reconstructed snapshots.

Systems performing historical order book analysis should account for that difference.

Tracking Changes in Event Probability

One of the simplest research features is probability change.

If a market moves from:

10.42 → 0.57

the implied probability has increased by roughly 15 percentage points.

A system could measure:

1probability_change = probability_now - probability_previous

Then calculate the same feature across several windows:

15 minutes
21 hour
36 hours
424 hours
57 days

But probability change becomes more useful when combined with market activity.

For example:

1signal = {
2    "probability_change_1h": 0.12,
3    "volume_change": 1.8,
4    "spread": 0.02,
5    "order_book_depth": 15400
6}

Now the model has more context than a single probability value.

Cross-Venue Monitoring

Normalization also makes it easier to compare similar markets across exchanges.

Imagine related contracts trading at:

1Polymarket: 0.62
2Kalshi:     0.67

The five-point difference may be worth investigating.

But it is not automatically an arbitrage opportunity.

Before comparing markets, check:

• contract wording
• outcome definitions
• resolution rules
• expiration times
• fees
• liquidity
• available size

Two contracts can sound similar while settling under different conditions.

A better workflow is:

1Find related markets
2        ↓
3Compare contract definitions
4        ↓
5Compare prices
6        ↓
7Check liquidity and spreads
8        ↓
9Account for fees
10        ↓
11Evaluate the difference

This is why metadata is just as important as price data.

Prediction Markets for News Analytics

Prediction markets can also add another layer to news analytics.

Suppose an application detects that an event contract moved from 38% to 55% within 20 minutes.

Instead of immediately declaring that new information has appeared, the system can investigate:

• trade activity
• volume
• spread changes
• order book depth
• related markets
• external news sources

It can then compare when the market moved with when relevant information became available.

This makes prediction market data useful for studying how markets react around news, without assuming that every probability movement predicts a future headline.

Prediction markets do not replace news.

They provide a market-based view of how participants are pricing an event.

A Simple Prediction Market Data Pipeline

A practical ingestion workflow can look like this:

1. Discover exchanges

Identify which prediction market venues are available.

2. Retrieve active markets

Find the markets currently available on each exchange.

3. Inspect market metadata

Understand the event and outcome before comparing it with other contracts.

4. Retrieve market activity

Collect latest and historical trades and quotes.

5. Pull OHLCV

Build historical probability series for charts and quantitative research.

6. Retrieve the current order book

Measure liquidity, spread, and available depth.

7. Store historical data

Use past trades, quotes, OHLCV, and order updates for research and backtesting.

The result is a cleaner pipeline for dashboards, systematic research, monitoring systems, and AI applications.

REST, JSON-RPC, and MCP

FinFeedAPI provides several ways to access prediction market data.

REST is useful for standard request-response applications and data pipelines.

JSON-RPC provides RPC-style access to the same general data resources.

MCP exposes prediction market data through tools designed for compatible AI agents and applications.

The hosted MCP interface includes tools for exchange discovery, markets, activity, OHLCV, and order books.

This can be useful when building AI research workflows that need structured prediction market data instead of relying only on web search or unstructured pages.

Why Use FinFeedAPI for Prediction Market Data?

Building directly against every prediction venue means maintaining separate schemas, identifiers, endpoints, and historical data pipelines.

FinFeedAPI provides a normalized alternative.

The FinFeedAPI Prediction Markets API gives developers access to latest and historical data across Polymarket, Kalshi, Myriad, Manifold, Hyperliquid Outcome Markets, Gemini, Pascal, and Crypto.com.

Available data includes:

• exchange metadata
• market listings
• active markets
• latest activity
• trades
• quotes
• OHLCV
• current order books
• historical order book updates

Developers can use that data to build systematic research tools, probability dashboards, AI agents, news analytics applications, monitoring systems, and alternative data workflows.

Instead of maintaining a separate integration for every venue, teams can build their applications on a normalized prediction market data layer.

Build With the FinFeedAPI Prediction Markets API

Prediction markets turn uncertainty around real-world events into structured, tradable market data.

But as more venues appear, integrating that data becomes harder.

The FinFeedAPI Prediction Markets API provides one normalized infrastructure layer across leading prediction market platforms, with REST, JSON-RPC, and MCP access for latest and historical data.

That gives developers more time to focus on what they build with the data instead of maintaining another exchange connector.

Explore the Prediction Markets API, create a free API key, and start building with complete, normalized prediction market data today.

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