featured imageMarika
February 20, 2026

Why Building a Prediction Markets Data Layer Is a Startup Opportunity

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Prediction markets are having a quiet moment that looks a lot like crypto in 2019 and sports betting in 2015.

Not because everyone is “trading probabilities.”

Because prediction markets are becoming a new kind of data layer… a real-time signal about what crowds believe will happen, and how strongly they believe it.

But there’s a catch.

Most teams still treat prediction markets like a chart you screenshot. If you want to do serious prediction markets research or build a forecast product that approach collapses immediately.

You need a pipeline.

A boring-sounding system that quietly becomes your biggest competitive advantage.

This guide explains how to design that pipeline using the FinFeedAPI Prediction Markets API, and more importantly… why building this layer is a real startup opportunity.

Prediction markets don’t just produce prices.

They produce:

  • Probabilities over time
  • Volatility patterns
  • Liquidity signals
  • Reaction speed to news
  • Trade intensity
  • Conviction under uncertainty

That’s structured belief.

And structured belief is rare.

Surveys ask opinions.
Markets price conviction.

If you capture that data correctly, you’re not building a dashboard.

You’re building a forecasting intelligence system.

Exchanges like Polymarket and Kalshi host markets. But hosting is not the only valuable layer.

The real opportunity is above them:

  • Ranking markets
  • Comparing exchanges
  • Scoring forecast stability
  • Measuring liquidity depth
  • Tracking probability shifts
  • Publishing research

Crypto had exchanges before CoinMarketCap.
Stocks had exchanges before Bloomberg.

Prediction markets have exchanges.

But the analytics layer is still early. That’s where a structured prediction markets data pipeline becomes strategic.

Let’s move from vision to architecture.

If you’re serious about building in this space, you’ll typically need four core data layers:

  1. Market discovery
  2. Historical price data (OHLCV)
  3. Trade & quote activity
  4. Order book depth

The FinFeedAPI Prediction Markets API maps directly to this structure.

Start with exchanges:

GET /v1/exchanges

This gives you supported exchanges (e.g., POLYMARKET, KALSHI) along with metadata.

Then track active markets:

GET /v1/markets/:exchange_id/active

This returns market IDs only — lightweight and ideal for polling.

Each market_id includes the outcome (e.g., will-it-rain-tomorrow_yes), which is critical for outcome-level modeling.

If you need full market metadata:

GET /v1/markets/:exchange_id/history

This includes:

  • title
  • description
  • status (Open, Closed, Resolved, Suspended)
  • mechanism (CPMM, CLOB)
  • outcome_type (Binary, MultipleChoice, Numeric)

Why this layer matters:

It lets you build:

  • “New markets today”
  • “Markets gaining activity”
  • Cross-exchange topic comparisons
  • Market lifecycle analytics

Without this layer, you’re blind to what exists.

If you want real prediction markets forecast analysis, this is your core dataset.

GET /v1/ohlcv/:exchange_id/:market_id/history

Required:

  • period_id (e.g., 1MIN, 1HRS, 1DAY)

Optional:

  • time_start
  • time_end
  • limit

Each record includes:

  • price_open
  • price_high
  • price_low
  • price_close
  • volume_traded
  • trades_count

All timestamps follow ISO 8601.
All values follow consistent precision rules.

This enables:

  • Volatility measurement
  • Drift analysis
  • Pre-resolution behavior modeling
  • Backtesting strategies
  • Forecast stability scoring

You can also use:

GET /v1/ohlcv/:exchange_id/:market_id/latest

For incremental updates and live dashboards.

This is where your product moves from “price viewer” to “research platform.”

Price changes alone are misleading.

You need to know whether moves are backed by activity.

Latest trade & quote:

GET /v1/activity/:exchange_id/:market_id/current

Recent trades & quotes:

GET /v1/activity/:exchange_id/:market_id/latest

This lets you measure:

  • Trade bursts
  • News reaction speed
  • Short-term attention spikes
  • Liquidity waves

Now you can build:

  • “Unusual activity” alerts
  • Probability shift notifications
  • Market attention rankings

That’s product differentiation.

Most analysis ignores order book depth.

Serious platforms don’t.

GET /v1/orderbook/:exchange_id/:market_id/current

You receive:

  • bids
  • asks
  • exchange timestamps
  • ingestion timestamps

This enables:

  • Spread analysis
  • Slippage modeling
  • Liquidity scoring
  • Depth-adjusted probability confidence

Thin markets behave differently from deep ones.

If you want institutional-grade analytics, you need this layer.

Once your pipeline runs, you unlock product opportunities.

Rank markets by:

  • volume_traded
  • probability change
  • trades_count
  • spread compression

This becomes:

  • SEO landing pages
  • daily newsletter content
  • media dashboards

Combine:

  • OHLCV volatility
  • trades_count
  • order book depth

Generate:

  • confidence index
  • liquidity score
  • forecast stability rating

Now you’re not just showing probability.

You’re interpreting it.

Store long-term historical data.

Publish insights like:

  • How prediction markets behave before resolution
  • Cross-exchange volatility comparison
  • Reaction patterns around macro events

This builds authority.

Authority builds defensibility.

Using activity + orderbook endpoints, you can:

  • Detect breaking-news reactions
  • Alert when probabilities jump > X%
  • Identify liquidity withdrawal

Prediction markets often move before headlines catch up. That’s signal.

Prediction markets are event-driven. Event-driven datasets become more valuable the longer they exist.

Six months of data gives you patterns.

Two years gives you research.

Five years gives you a moat.

Structured APIs — with consistent identifiers, ISO timestamps, and defined period intervals — make long-term compounding possible. Scraping does not.

Building a prediction markets data pipeline isn’t about charts. It’s about building the infrastructure that:

  • Structures probability
  • Measures volatility
  • Quantifies liquidity
  • Tracks conviction over time
  • Compares crowd intelligence across exchanges

The exchanges create markets. A data-driven startup can create the intelligence layer above them… and in emerging ecosystems, the intelligence layer often wins.

If you’re building:

  • A prediction markets analytics platform
  • A forecasting research product
  • A real-time event intelligence system
  • Or a multi-exchange market dashboard

The first step is structured, reliable data.

The FinFeedAPI Prediction Markets API gives you:

  • Exchange metadata
  • Active and historical markets
  • OHLCV time series
  • Trade & quote activity
  • Order book snapshots
  • REST and JSON-RPC access
  • Secure API key + JWT authentication

Start by pulling your market universe. Store your first OHLCV dataset.
Track probability changes over time.

That’s where the real opportunity begins.

👉 Explore the Prediction Markets API and start building your data layer today.

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