FinFeedAPI Blog - Easy Access to Prediction Market Data

Prediction market data can power everything from a simple probability dashboard to large-scale research, event monitoring, and AI agents.

But easy access to prediction market data does not mean using the same access method for every application.

A small app may only need REST. A research team may need historical datasets for larger analyses. An application monitoring active markets can periodically retrieve the latest available data, while an AI application may benefit from MCP tools.

The right architecture depends on what you are building.

What Does Easy Access to Prediction Market Data Mean?

A prediction market price is only one part of the dataset.

Developer-grade access can include:

active markets and market metadata
exchange and market identifiers
outcomes
trades and quotes
OHLCV history
order books
historical market activity

This distinction matters because a current probability tells you where a market is now. Historical trades, quotes, candles, and activity tell you how it got there.

FinFeedAPI provides standardized prediction market data across multiple venues, including Polymarket, Kalshi, Myriad, Manifold, Hyperliquid Outcome Markets, Gemini Prediction Markets, Pascal, Crypto.com Prediction Markets, and ForecastEx.

Instead of integrating different venue APIs and normalizing their schemas independently, developers can work with a common data layer.

Which Prediction Market Data Access Method Should You Use?

There is no single interface for every workflow.

Access method	Best for	Main advantage
REST	Apps, dashboards, targeted queries	Simple programmatic access
JSON-RPC	Internal services	Method-oriented API calls
MCP	AI agents	Structured tool access
Historical/bulk data	Research and backtesting	Larger historical datasets
Scheduled REST polling	Monitoring	Periodically refreshed market state

A useful way to think about the difference is:

REST: What does this market look like now?

Historical data: What did this market look like over time?

Scheduled polling: What changed since my last request?

MCP: How can an AI agent find and inspect relevant prediction market data?

REST: The Simplest Starting Point for Developers

For most applications, REST is the easiest place to start.

It works well when an application needs specific markets, selected historical periods, or the latest available market information.

Typical use cases include:

prediction market dashboards
event-tracking applications
backend services
forecast applications
scheduled data jobs
market discovery
targeted historical queries

A typical workflow can be straightforward:

FinFeedAPI REST API → application backend → database → dashboard or application

The application first discovers available or active markets. It then selects the relevant market and outcome identifiers and retrieves the required trades, quotes, OHLCV, activity, or order book information.

The results can be stored in a database and refreshed according to the needs of the application.

REST Is More Than a Probability Endpoint

A common mistake is to retrieve the latest market price and treat it as the complete forecast signal.

A more useful application can combine:

market metadata + latest trades + quotes + spread + OHLCV + order book

For example, two markets may both imply a probability around 60%.

But one may have a narrow bid/ask spread and consistent trading activity, while the other has little activity and a thin order book.

The number may look similar. The underlying market state is not.

Historical and Bulk Data: Better for Research and Backtesting

REST works well for targeted requests.

It becomes less convenient when a research workflow needs large amounts of historical data.

Imagine analyzing thousands of prediction markets to determine how probabilities moved around major events. Repeatedly requesting large numbers of individual historical records adds unnecessary complexity.

Where historical datasets are available, bulk-style access is better suited to workflows such as:

quantitative research
historical backtesting
AI model training
event studies
data warehousing
offline feature engineering
reproducible analysis

A typical architecture looks different:

Historical datasets → data lake or warehouse → feature engineering → models or backtests

Instead of repeatedly requesting individual observations, the team can ingest historical datasets and perform analysis locally.

REST or Historical Data?

Look at the question your application is asking.

If the question is:

“Give me the latest data for these 20 markets.”

REST makes sense.

If the question becomes:

“Give me a large historical dataset so I can analyze market behavior over time.”

Historical or bulk access is more practical where the required dataset is available.

The two approaches can also work together. Historical data can provide a backfill, while REST can retrieve subsequent observations.

Monitoring Prediction Markets Without Real-Time Streaming

This distinction is important.

FinFeedAPI Prediction Markets API does not currently provide WebSocket streaming.

Applications that need to monitor markets can instead use scheduled requests to retrieve the latest available data and compare it with previously stored observations.

This approach can support:

market dashboards
probability alerts
event monitoring
AI watchlists
research tools
news-reaction analysis

A monitoring workflow might look like this:

Discover active markets.
Select markets by topic, venue, category, or keyword.
Periodically request the latest available market data.
Compare it with the previous stored observation.
Apply liquidity and recency filters.
Trigger an alert when defined conditions are met.
Store observations for later analysis.

This is polling-based monitoring, not real-time streaming.

The distinction matters when designing systems that depend on update frequency or latency.

Do Not Alert on Price Alone

Suppose a market moves from 42% to 49% between two observations.

That seven-percentage-point move may look significant.

Before triggering an alert, an application can also check:

How recent is the observation?

How wide is the spread?

Did trading activity increase?

What does the order book look like?

A stronger monitoring system evaluates the market state around the probability, not only the probability itself.

JSON-RPC: An Alternative for Internal Infrastructure

FinFeedAPI also provides JSON-RPC access to prediction market data.

For many developers, the difference between REST and JSON-RPC is primarily about integration style.

REST is resource-oriented.

JSON-RPC is method-oriented.

That can make JSON-RPC useful for internal services, centralized data gateways, or infrastructure where applications already communicate through RPC-style calls.

For teams building conventional applications, REST will often remain the more familiar starting point.

MCP: Prediction Market Data for AI Agents

AI applications introduce a different access problem.

An agent may not know the exact market identifier it needs before beginning a task. It may first need to discover markets, inspect their metadata, retrieve observations, and decide which information is relevant.

FinFeedAPI's MCP interface gives AI applications structured access to prediction market data.

This can support workflows such as:

finding active markets related to a topic
retrieving OHLCV for a selected market
checking market activity
inspecting order books
building market watchlists
generating research summaries

The architecture becomes:

AI application → MCP tools → FinFeedAPI prediction market data → structured output

MCP should not replace deterministic application logic.

An agent can decide which markets to investigate and which data it needs. Calculations such as probability changes, liquidity filters, spread thresholds, and alert conditions are better handled by deterministic code.

Small App, Research Workflow, or Production Pipeline?

The easiest prediction market data architecture changes as the application grows.

Small App

Start with REST.

Retrieve active markets, selected market metadata, activity, and the latest available observations you need.

REST → backend → database → UI

This is enough for many dashboards, event trackers, and early-stage products.

Research Workflow

Historical analysis has different requirements.

Researchers may need larger datasets, consistent historical observations, and the ability to rerun analysis without repeatedly retrieving the same information.

Historical data → warehouse/notebook → analysis

This approach works well for backtests, forecast evaluation, probability studies, and model training.

Production Data Pipeline

More advanced systems can combine both approaches.

REST + historical data → ingestion → raw storage → normalized warehouse → signals, dashboards, APIs, or AI tools

Historical data provides context and backfill.

REST provides the latest available observations on request.

The warehouse creates a consistent analytical layer between the source data and downstream applications.

From Raw Market Price to Usable Probability

Prediction market prices are often interpreted as implied probabilities.

If a binary contract is represented between 0 and 1, a price of:

0.67

can be interpreted as approximately:

67%

But developers do not have to rely exclusively on the last traded price.

If bid and ask data are available, a simple midpoint can be calculated:

mid_probability = (best_bid + best_ask) / 2

The spread provides additional context:

spread = best_ask - best_bid

Historical trades can also be aggregated into volume-weighted observations, while OHLCV data can show how implied probabilities changed through time.

This turns raw prediction market data into something much more useful for charts, monitoring, research, and forecasting systems.

Build the Data Pipeline Before Building the Signal

Regardless of the access method, production applications should account for several basic engineering requirements.

Historical ingestion should support pagination, retries, deduplication, and incremental synchronization.

Timestamps should be normalized consistently, preferably to UTC, and applications should retain enough information to identify stale observations.

For more advanced systems, keeping both raw and normalized data can also be useful.

Raw observations support debugging and auditing.

Normalized tables make analytics easier.

Basic Data Quality Checks Matter

Easy access does not remove the need to validate incoming data.

A prediction market pipeline can check that:

market and outcome identifiers are present
probabilities remain within expected bounds
timestamps are valid
bid prices do not exceed ask prices
spreads are not negative
volumes are non-negative
OHLCV values are internally consistent
duplicate observations are handled
stale observations are detected

These checks become increasingly important when prediction market data feeds automated analytics, alerts, or AI systems.

One Data Layer, Different Ways to Access It

Prediction market applications do not all need the same infrastructure.

A developer building a dashboard can start with REST.

A research team working with larger historical datasets can use historical or bulk-oriented access where available.

An application that needs to monitor active markets can periodically request the latest available data and compare it with stored observations.

An AI application can use MCP to discover and inspect structured market data.

FinFeedAPI Prediction Markets API currently uses request-based access rather than WebSocket or real-time streaming. This gives developers a clear choice between REST, JSON-RPC, MCP, historical data workflows, and scheduled polling depending on what they are building.

Access Prediction Market Data with FinFeedAPI

Build applications, research workflows, monitoring tools, and AI systems using standardized prediction market data across multiple venues.

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Read the Prediction Markets API Documentation

Easy Access to Prediction Market Data