Prediction Markets OHLCV
In prediction markets, prices update continuously as participants trade. OHLCV organizes this activity into standardized time buckets, each containing open, high, low, close prices, and trading volume.
The prices represent outcome probability prices, not asset valuations. Open and close show how belief started and ended during a period, while high and low capture the full range of belief during that time. Volume adds essential context. It shows how much trading activity supported the price movement, helping distinguish stable consensus from fragile or thinly traded shifts.
OHLCV can be generated at multiple resolutions, such as hourly or daily intervals. Different resolutions reveal different behavior, from short-term reactions to long-term belief trends. Unlike raw tick data, OHLCV is compact and comparable. It allows analysts to study many markets, outcomes, or exchanges efficiently using consistent time-series structures.
For prediction markets data analysis, OHLCV is a foundational format. It supports charting, trend detection, volatility analysis, and historical backtesting at scale.
Why It Matters
OHLCV transforms raw prediction markets data into structured signals. It makes probability dynamics easier to analyze, compare, and model over time.
How is OHLCV defined for prediction markets?
In prediction markets, OHLCV uses outcome probability prices instead of asset prices. Each interval records the first price (open), highest price (high), lowest price (low), last price (close), and total traded volume. These values summarize belief and activity during the period. The structure mirrors financial markets but represents probability dynamics.
What insights can OHLCV reveal in prediction markets data?
OHLCV reveals volatility, belief stability, and participation strength within each time window. Large price ranges with low volume often indicate fragile signals. Tight ranges with high volume suggest strong consensus. Comparing OHLCV across periods highlights shifts in uncertainty and attention.
When should analysts use OHLCV instead of raw probability streams?
OHLCV is best used when analyzing longer time horizons, multiple markets, or large datasets. It reduces data size while preserving key behavior. Raw probability streams are better for micro-level analysis, while OHLCV supports trend analysis, visualization, and backtesting. Both formats serve different analytical needs.
Real-World Example
On Polymarket, an hourly OHLCV series for an election outcome may show wide price swings and high volume during a debate. This indicates active belief updating concentrated within that hour.
Prediction Markets OHLCV and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides native OHLCV data for prediction markets.
The API supports:
- Historical OHLCV data at the exchange level
- Historical OHLCV data for specific markets and outcomes
- Latest OHLCV data for active markets
Analysts can request OHLCV using custom time periods, date ranges, and limits. This enables consistent charting, historical analysis, and backtesting using standardized prediction markets data. FinFeedAPI allows OHLCV analysis across exchanges and markets without manual aggregation.