featured imageMarika
December 23, 2025

Why Working With Prediction Market Data Is Harder Than It Looks

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Prediction markets look simple from the outside.

A question.
A probability.
A chart.

But prediction market data is structurally different from almost every other form of market data. Treat it like stocks or crypto, and the output may look clean — while being fundamentally wrong.

This is not a tooling problem.
It’s a data modeling problem.

In prediction markets, one question creates multiple tradable instruments.

Each outcome is its own market.

This is why prediction market data must be modeled at the outcome level, not the question level.

In practice, this means:

  • Each outcome has its own price
  • Each outcome has its own volume
  • Each outcome has its own liquidity profile

When prediction market data collapses outcomes into one stream, probabilities lose meaning. A well-designed prediction market data model never does this.

In prediction market systems, identifiers like:

1will-it-rain-tomorrow_yes

are not cosmetic.

They encode what is actually being traded.

Prediction market data that does not include outcome-level identifiers forces developers to reconstruct meaning later — usually incorrectly. This leads to errors in backtesting, probability tracking, and cross-market comparison.

Outcome-aware identifiers are the foundation of usable prediction market data.

Prediction market prices behave differently because they are probabilities, not valuations.

This introduces constraints that traditional market data systems do not enforce:

  • Prices are bounded between 0 and 1
  • “High” and “low” do not imply volatility in the usual sense
  • A price change reflects belief shifts, not capital flows

Prediction market data that ignores these constraints leads to invalid indicators and misleading charts.

This is why outcome-level OHLCV is necessary — not a nice-to-have.

In prediction market data, OHLCV candles represent how belief changes over time, not how an asset appreciates.

This changes how the data should be interpreted:

  • A flat candle may still hide deep disagreement
  • Volume does not imply confidence
  • Closing price does not mean “consensus”

Prediction market data systems must preserve this nuance by exposing OHLCV per outcome, per exchange, with clear time boundaries.

A prediction market price without liquidity context is incomplete.

Order book data shows: how fragile a probability is, whether a price can move easily and how much resistance exists on each side.

Prediction market data that exposes bids and asks per outcome allows developers to distinguish stable belief from thin pricing.

Without this, prediction market data becomes reactive instead of explanatory.

Prediction markets do not share a single trading mechanism.

The two dominant models behave very differently:

MechanismWhat the data really means
CPMMSmooth price changes, volume is less informative
CLOBDepth and spread define probability stability

Prediction market data that does not expose the market mechanism forces analysts to guess how prices were formed.

Good prediction market data makes the mechanism explicit so models can adapt.

In prediction markets, a market can be:

  • open but inactive
  • active but near resolution
  • technically open with no meaningful liquidity

Prediction market data must therefore distinguish recent activity from historical existence.

Systems that mix these concepts cause:

  • noisy dashboards
  • false alerts
  • irrelevant signals

Separating active market identifiers from full historical market data reflects how prediction markets actually behave in production.

Prediction markets react to real-world events. Minutes — sometimes seconds — matter.

Prediction market data must clearly distinguish:

  • when the exchange generated the data
  • when it was observed and recorded

This is essential for:

  • multi-exchange comparison
  • latency-sensitive analysis
  • post-event reconstruction

Prediction market data without time clarity cannot support serious analysis.

The hardest part of prediction market data is not fetching it. It’s knowing what it represents.

Every data point depends on:

  • the question
  • the outcome
  • the mechanism
  • the market state
  • the timing

Prediction market data that removes this context becomes easier to consume — and less accurate.

Prediction market data that preserves context becomes harder to design — and far more valuable.

If you are building on prediction markets, you need more than prices.

You need prediction market data that:

  • is outcome-aware
  • respects probability constraints
  • exposes liquidity
  • reflects real activity
  • preserves context

FinFeedAPI’s Prediction Markets API is built around these principles, providing structured prediction market data designed for analysis, not guesswork.

👉 Explore the Prediction Markets API and start working with prediction market data that reflects how prediction markets actually function.


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