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FinFeedAPI Prediction Markets

FinFeedAPI Prediction Markets unifies listings, metadata, trades, quotes, candles and books behind several protocols. It supports both ready-made historical series and raw order-book changes, letting applications choose between a dashboard view and their own reconstruction pipeline.

Available on
API · MCP

Screenshots and interface

What is FinFeedAPI Prediction Markets?

Begin by listing exchanges and active market identifiers, then retrieve complete records with outcomes, status and prices. The documented venue list includes Polymarket, Kalshi, Manifold and Myriad; those venues span different market types, so verify endpoint coverage for each one rather than assuming identical data everywhere.

REST, JSON-RPC and hosted MCP expose the normalized datasets to ordinary applications and compatible assistants. Historical book files contain adds, updates and deletions rather than reconstructed snapshots. A replay therefore needs both book reconstruction and deliberate handling of processing-time selection versus exchange-event timestamps.

Who it is for

  • Developers consolidating data from several prediction venues.
  • Researchers building historical replay and backtesting datasets.
  • AI applications requiring discoverable market-data tools.

Features and coverage

  • Exchange and market discovery

    List supported venues and active IDs, then obtain full records with outcomes, status and prices.

  • Trades and quotes

    Retrieve recent or historical activity in a common schema that retains timing information.

  • OHLCV history

    Request market-level or exchange-level candles using available periods and ascending chronological order.

  • Current outcome books

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

  • Raw book events

    Download flat-file additions, updates and deletions for your own historical book reconstruction.

  • REST, JSON-RPC and hosted MCP

    Use the same normalized datasets through these protocols. The hosted MCP exposes venue discovery, market lookup, activity, candles and book queries to compatible AI clients.

Workflows with FinFeedAPI Prediction Markets

Build a multi-venue dashboard

Discover exchanges and active markets, store identifiers, then request selected metadata and quotes. Fetch current books only where users need depth information for execution.

Reconstruct a historical book

Bound the requested period, preserve both event and processing timestamps, and reconstruct the book before comparing its state with trades.

Connect a market-data assistant

Configure the hosted MCP endpoint with an API key. Start with exchange discovery, then issue a request for a specific market.

Pricing and total cost

Billing uses credits. Pay-as-you-go pricing is $5 per 1,000 requests for the first 1,000 requests each day, then $1 per 1,000 requests beyond that daily threshold. Monthly commitments discount purchased credits.

Pay as you go

$1 per credit

  • No committed monthly spend

Committed 64

$64/month

  • Each credit costs $0.85

Committed 256

$256/month

  • Each credit costs $0.75

Committed 512

$512/month

  • Each credit costs $0.70

Committed 1024

$1,024/month

  • Each credit costs $0.65

Fees and usage costs

  • Usage beyond committed credits is charged at the pay-as-you-go rate.

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

Getting started

Before you start: Processing timestamps and exchange-event timestamps are distinct; choosing the wrong one can misalign a replay.

  1. Create an account and obtain an API key, then list exchanges and select the venue IDs needed by the project.
  2. Request active market IDs and retrieve complete metadata for a small sample.
  3. Choose REST, JSON-RPC or hosted MCP for the application.
  4. Set historical ranges, request limits and a credit budget before scaling collection.

Historical selection may use processing time while records also carry exchange-event time; a replay must use the appropriate timestamp.

Read the data API documentation

Useful links

What to check

  • Processing timestamps and exchange-event timestamps are distinct; choosing the wrong one can misalign a replay.
  • Raw historical book changes need reconstruction before they form a usable snapshot.
  • Listed venues have different market types; verify actual endpoint coverage for each target venue.

A closer look

Ratings & reviews

Editorial rating
3.5/ 5
0 user reviews

Our take on FinFeedAPI Prediction Markets

Normalized discovery, activity and history across several interfaces provide a practical integration base. Workload design still needs to account for credit pricing and the distinction between processing and exchange timestamps.

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

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Frequently asked questions about FinFeedAPI Prediction Markets

Can I obtain raw order-book changes?

Yes. Historical book files provide adds, updates and deletions for your own reconstruction.

Can an MCP client query the data?

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

Does a commitment change request counting?

The commitment discounts purchased credits. Usage after those credits is billed at the pay-as-you-go rate.

Does this API execute orders?

The listed product is a market-data API; its documented role is data discovery and retrieval.