How to Retrieve OHLCV Data from Prediction Markets?
Prediction markets used to be niche.
Now they’re everywhere — powering election dashboards, economic forecasts, trading tools, and AI models hungry for real-time probability data.
But here’s the challenge:
If you want clean, structured OHLCV candles from markets like Polymarket, Kalshi, Myriad, or Manifold, you need more than raw feeds or scattered price snapshots.
You need an API that delivers ready-to-use OHLCV data.
Let’s break down how prediction-market OHLCV works, where the data comes from, and the easiest way to retrieve it — without drowning in technical complexity.
Why Prediction Market OHLCV Is Suddenly in Demand
Prediction markets have evolved from a curiosity into a key signal source for analysts tracking:
election odds
macro-risk sentiment
geopolitical events
economic expectations
public probability shifts
And there’s a new player in the mix: AI. AI models love prediction markets because the data is:
structured
timestamped
probability-based
ideal for time-series forecasting
OHLCV is the cleanest format for turning all that into charts, features, or real-time predictions.
Where Prediction-Market OHLCV Data Actually Comes From
Every candle — every open, high, low, close, and volume point — is built from tiny pieces:
individual trades
order changes
price updates
liquidity shifts
But here’s the catch: there is no standard across exchanges.
Each market logs data differently, using:
different event formats
different time resolutions
different naming systems
different quote structures
This creates two possible ways to retrieve OHLCV: the slow, painful way… and the smart, clean way.
1. Pulling Data Directly From Exchanges (The Difficult Path)
Some prediction markets do publish raw data.
But working with it directly is usually frustrating because raw feeds are:
Inconsistent - Each exchange format is unique.
Missing candle structure - They give you trades, not OHLCV. You have to manually rebuild candles.
Split by outcome - “YES” and “NO” often appear as separate markets.
Incomplete historically - Many platforms don’t provide full archives.
Engineering-heavy - You must parse, clean, merge, resample, timestamp, and backfill everything. If you’ve ever tried to build your own OHLCV from raw feeds, you know it quickly becomes a full-time engineering problem.
2. Using a Market-Data API (The Fast Route)
A market-data API turns raw feeds into usable, standardized, ready-to-chart OHLCV.
A good API gives you:
One format across all exchanges -No custom parsers.
Candles for any timeframe -1MIN, 5MIN, 1HRS, 1DAY — whatever you need.
Historical + latest data -No need to build your own archive.
Once the data is structured, you can build anything.
FAQ
How do I get historical prediction-market data?
Use FinFeedAPI’s /history endpoint to retrieve full OHLCV timeseries for any supported market.
What is the best API for Polymarket OHLCV?
FinFeedAPI — it standardizes Polymarket data into clean candle formats.
Does Kalshi support OHLCV?
Raw Kalshi feeds don’t, but FinFeedAPI converts them into consistent OHLCV candles.
Can I get 1-minute prediction-market candles?
Yes — FinFeedAPI supports intervals from 1 second up to multi-year periods.
Can I use this data for AI or forecasting models?
Absolutely — OHLCV is the ideal input for time-series AI models and prediction systems.
Start building your products with the Prediction Market API
If you need clean, unified OHLCV data across Polymarket, Kalshi, Myriad, and Manifold, FinFeedAPIPrediction Market APIgives you everything in one simple API call — historical, latest, any timeframe.
No cleanup. No normalization. No wasted engineering time.
