featured imageMarika
March 23, 2026

Technicalities of Prediction Market Data

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Prediction markets look simple on the surface… you see a price, you read it as probability, and that’s it.

But once you start working with prediction market data, things get more complicated.

The challenge isn’t the idea of “probability as price.” It’s everything underneath:

  • inconsistent schemas across platforms
  • outcome-based instruments instead of single assets
  • order books that behave differently than traditional markets
  • and a hard stop when markets resolve

This guide breaks down what actually matters when working with a prediction markets API and how to structure your pipeline so your forecasting data stays clean, consistent, and usable.

FinFeedAPI’s Prediction Markets API gives unified access to platforms like Polymarket and Kalshi, including order books, OHLCV candles, and market metadata via REST and JSON-RPC. That means you can focus on analysis instead of rebuilding infrastructure from scratch.

Most datasets in prediction markets are not “markets” in the traditional sense. They’re collections of outcome instruments.

Think of it like this:

  • Exchange → where the data comes from (e.g., Kalshi, Polymarket)
  • Market → the event (e.g., “Will X happen by date Y?”)
  • Outcome instrument → what you actually trade (YES / NO or multiple outcomes)

In real data, this typically looks like:

  • exchange_id (e.g., KALSHI)
  • market_id (e.g., KXGAMEAWARDS-2025-HK_YES)

That last part matters. The outcome (YES/NO) is baked into the identifier.

Practical rule:
Treat (exchange_id, market_id) as your primary key at the instrument level.

  • instrument_pk = (exchange_id, market_id)
  • event_pk (optional, if grouping outcomes later)

A lot of teams skip the event layer early on. That’s fine just make sure you store enough metadata to reconstruct it later.

A prediction market price is often treated like probability. But structurally, it behaves like any traded asset.

You have:

  • trades → actual executed transactions
  • quotes → the current market (bid/ask, liquidity)

FinFeedAPI exposes both together, but they represent different things.

1{
2  "trade": {
3    "price": 0.356,
4    "quantity": 56.179774
5  },
6  "quote": {
7    "bid": 0.338,
8    "ask": 0.36
9  }
10}

Even if they arrive in one payload, store them separately:

  • trades(exchange_id, market_id, trade_id, ts, price, qty, side)
  • quotes(exchange_id, market_id, ts, bid, ask, bid_size, ask_size)

Why this matters:

Most useful forecasting features don’t come from price alone. They come from microstructure:

  • spread widening
  • liquidity drops
  • order book imbalance

If you only store trades, you lose that signal.

OHLCV is familiar—but in prediction markets, it behaves differently.

Two common issues:

  1. Sparse trading → many intervals with zero trades
  2. Outcome instruments → each outcome has its own chart

Example structure:

1{
2  "price_open": 4.5,
3  "price_close": 3.5,
4  "volume_traded": 0,
5  "trades_count": 0
6}

Yes, zero-volume candles are normal.

They’re not bad data—they’re how you keep a consistent time grid.

  • time_period_start / end → candle boundaries
  • time_open → first trade in interval
  • time_close → last trade

If no trades happen, you still get the interval. That’s important for modeling.

  • Historical:

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

  • Latest:

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

This is where a good prediction markets API saves time—you get consistent structure across venues.

  • Never assume price is 0–1 (some venues use cents)
  • Always store period_id (don’t mix resolutions)
  • Treat candles as derived (verify against trades if needed)

Prediction market order books look familiar—but behave differently.

You’ll see:

  • different tick sizes
  • different price formats
  • shallow depth and sudden gaps

Example:

1{
2  "asks": [{ "price": 4, "size": 4740 }],
3  "bids": [{ "price": 3, "size": 6549 }]
4}

For most use cases:

  • store L1 (best bid/ask)
  • optionally store L5 or L10

You don’t need full depth unless you’re doing detailed microstructure research.

Raw price is just a number.

To treat it as probability, you need normalization.

If you skip this:

  • cross-exchange comparisons break
  • volatility metrics become inconsistent
  • backtests give misleading results

Resolution is not just a flag.

It’s a state change that affects everything downstream.

When a market resolves:

  • trading stops
  • order books disappear
  • one outcome becomes ground truth
  • time series ends
1resolutions(exchange_id, market_id, resolved_ts, winning_outcome, status)

Keep this separate from price data.

  1. Ingest data normally while market is active
  2. Mark resolution when it happens
  3. Always join resolution data in backtests

This prevents a common mistake:
using data that only exists after the outcome is known.

Most prediction market data pipelines fail in the same places:

  • inconsistent market IDs
  • different outcome formats
  • price scales (decimals vs cents)
  • unclear volume meaning
  • time mismatches
  • lifecycle differences (open, paused, resolved)

Store raw → then normalize.

Add fields like:

  • price_norm (0–1 scale if needed)
  • price_unit
  • event_group_id

This lets you fix assumptions later without reprocessing everything.

Backtesting prediction markets isn’t just:
“buy when model > price.”

Your results depend on:

  • spread (cost of entry)
  • available liquidity
  • execution timing
  • resolution cutoff
  • OHLCV (fast iteration)
  • quotes (execution-aware)
  • resolution (ground truth)

FinFeedAPI supports this directly with:

  • OHLCV endpoints
  • order book snapshots
  • combined trade + quote data
  • Use executable prices (buy at ask, sell at bid)
  • Never leak post-resolution data into features

Even something simple like “final candle close” can break your model if you're not careful.

If you want something that works long-term:

  • markets
  • ohlcv
  • trades
  • quotes
  • orderbook_snapshots
  • resolutions

This structure works well with both time-series databases and warehouses like BigQuery or Snowflake.

If you’re building analytics, trading models, or research pipelines, your biggest risk isn’t modeling—it’s messy data.

A clean, unified prediction markets API removes that friction.

FinFeedAPI gives you structured access to:

  • prediction market data across exchanges
  • OHLCV and order book data
  • resolution events and metadata

So you can focus on insights, not infrastructure.

→ Docs: https://docs.finfeedapi.com/prediction-markets-api/
→ Product: https://www.finfeedapi.com/products/prediction-markets-api

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