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FinFeedAPI Prediction Markets

Access normalized prediction-market listings, trades, quotes, order books and historical candles through REST, JSON-RPC or MCP.

Visit official sitewww.finfeedapi.com
Available on
REST API, JSON-RPC, MCP

Screenshots and interface

What is FinFeedAPI Prediction Markets?

FinFeedAPI gives market-data applications a consistent model across prediction venues. Start with exchange and active-market discovery, then request metadata, recent activity or historical data for the chosen identifiers. The service includes both ready-made candles and raw historical order-book events, allowing a dashboard and a replay pipeline to use different levels of detail from the same integration.

Who it is for

  • Developers consolidating several prediction-market data sources
  • Researchers building historical replay and backtests
  • AI applications that need discoverable market-data tools

Features and coverage

Available on
REST API, JSON-RPC, MCP
Markets & venues
Polymarket, Kalshi, Manifold, Myriad
Research topics
Market data, Historical replay
  • Exchange and market discovery

    List supported venues, active IDs and full market records including outcomes, status and prices.

  • Trades and quotes

    Retrieve recent or historical activity through a consistent schema that retains timing information.

  • OHLCV candles

    Request market or exchange histories with available period choices and ascending time order.

  • Current order books

    Inspect bids, asks, depth and spread for an individual outcome.

  • Historical book events

    Retrieve adds, updates and deletions from flat files for your own book reconstruction.

  • Hosted MCP

    Expose exchange discovery, market lookup, activity, candles and book queries to a compatible AI client.

  • Multiple protocols

    Choose REST, JSON-RPC or MCP around the same normalized datasets.

Workflows with FinFeedAPI Prediction Markets

Build a cross-venue dashboard

Discover exchanges and active markets, store their identifiers, then fetch selected metadata and quotes. Add current books only where users need execution-depth information.

Replay market structure

Request historical book events for a bounded period, preserve their event and processing timestamps, then reconstruct the book before comparing it with trades.

Connect an assistant

Configure the hosted MCP endpoint with your API key and start with exchange discovery, followed by a specific market request.

Pricing and total cost

Usage is credit-based. Pay-as-you-go costs $5 per 1,000 requests for the first 1,000 requests each day and $1 per 1,000 beyond that daily threshold.

Pay as you go

$1 per credit

  • No committed monthly spend

Committed 64

$64/month

  • Credits priced at $0.85 each

Committed 256

$256/month

  • Credits priced at $0.75 each

Committed 512

$512/month

  • Credits priced at $0.70 each

Committed 1024

$1,024/month

  • Credits priced at $0.65 each

Fees and usage costs

  • Usage after committed credits is billed at the pay-as-you-go rate.

Trial: Qualified new organizations receive $25 in usage credits.

Getting started

Before you start: Historical activity can be selected by processing time while also carrying exchange-event timing; use the correct timestamp for a replay.

  1. Create an account and obtain an API key.
  2. List exchanges and choose the venue IDs required by your project.
  3. Request active market IDs, then fetch complete metadata for a small sample.
  4. Choose REST, JSON-RPC or hosted MCP for the consuming application.
  5. Define historical date ranges, request limits and a credit budget before scaling collection.

Open documentation

Useful links

What to check

  • Historical activity can be selected by processing time while also carrying exchange-event timing; use the correct timestamp for a replay.
  • Raw historical book updates require reconstruction before they become a usable book snapshot.
  • The published venue list spans different market types; check actual endpoint coverage for each target venue.

A closer look

Ratings & reviews

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Editorial rating
3.5/ 5
0 user reviews

Our take on FinFeedAPI Prediction Markets

Normalized discovery, trades and history through several interfaces provide a useful integration base. Credit pricing and the distinction between processing and exchange timestamps need to be modeled in the workload.

Based on documented features, setup and access terms. How we rate

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Frequently asked questions about FinFeedAPI Prediction Markets

Can I get raw book changes?

Yes. The historical order-book route supplies adds, updates and deletions for reconstruction.

Can an MCP client access the data?

Yes. The hosted server authenticates with an API key and exposes data-discovery and retrieval tools.

Does a committed plan change how calls are counted?

It discounts purchased credits. Additional usage after those credits uses the pay-as-you-go rate.

Is it an order-execution API?

This product is a market-data API.