Prediction markets are often consumed as probability feeds, but for quantitative users the real problem is not retrieving probabilities… it is determining whether those probabilities contain information.
Two markets can both move from 45% to 55%.
In a deep, actively traded market with tight spreads and continuous trading, that move likely reflects information aggregation across many participants.
In a thin market, the same move can occur because a single order walks through the book.
For anyone building forecasting models, trading systems, or analytics dashboards, the critical distinction is therefore:
Is the probability update informational, or is it a liquidity artifact?
Answering this requires analyzing three components together:
- liquidity conditions
- trading activity
- persistence of probability changes
Only when these factors align do prediction markets behave like reliable information aggregation mechanisms.
Analyzing this properly requires access to multiple layers of market data: market metadata, OHLCV probability history, trade activity, and order book snapshots.
All of these datasets are accessible through the FinFeedAPI Prediction Markets API, which aggregates prediction market data across venues such as Polymarket, Kalshi, Myriad, and Manifold.
1. Data required for prediction market microstructure analysis
Prediction market analytics generally relies on four core data layers. Each layer captures a different part of market microstructure.
Endpoints are shown below simply to illustrate how these datasets are typically retrieved when building quantitative pipelines.
Exchange and market universe
The first step in most research pipelines is building a market universe.
This typically involves enumerating supported exchanges and retrieving market metadata. The resulting dataset becomes the foundation for research datasets, backtests, or monitoring systems.
Example endpoints:
GET /v1/exchangesGET /v1/exchanges/{exchange_id}
To retrieve market lists:
GET /v1/markets/{exchange_id}/historyGET /v1/markets/{exchange_id}/active
Historical market lists are commonly used to construct prediction market datasets for research, while active market lists are used for live monitoring or trading systems.
Probability price history (OHLCV)
Once the market universe is defined, the next dataset required is probability price history.
OHLCV time series allow researchers to analyze how market expectations evolve over time. These datasets support calculations such as:
- realized volatility
- volume regimes
- intraday patterns
- probability trend persistence
Example endpoint:
GET /v1/ohlcv/{exchange_id}/{market_id}/history
OHLCV datasets form the backbone of most prediction market analytics pipelines.
Trade activity
Trade-level data reveals how price changes occur, not just that they occurred.
From trade streams, analysts can estimate:
- trade intensity
- inter-trade timing
- directional order flow
- bursts of activity during information events
Example endpoint:
GET /v1/activity/{exchange_id}/{market_id}/latest
Trade data is particularly important when analyzing market microstructure behavior and estimating price impact.
Order book liquidity
Order book snapshots provide the instantaneous supply and demand around the current market price.
From these snapshots, analysts can estimate:
- bid–ask spreads
- available liquidity near the price
- directional imbalance between buyers and sellers
- sensitivity of prices to incoming orders
Example endpoint:
GET /v1/orderbook/{exchange_id}/{market_id}/current
Order book data is essential when estimating liquidity conditions.
2. Liquidity metrics that matter in prediction markets
Liquidity is multi-dimensional. Several indicators are typically monitored simultaneously.
Because prediction market prices are bounded between 0 and 1, liquidity effects often become more pronounced as markets approach resolution.
Bid–ask spread
The bid–ask spread measures the difference between the best buy order and the best sell order.
Tight spreads generally indicate active participation and competitive pricing. Wide spreads suggest thin liquidity and higher transaction costs.
Monitoring spreads over time helps identify liquidity regimes within a market.
Market depth
Market depth measures how much liquidity exists near the current price.
Markets with deep order books contain large quantities of buy and sell orders close to the current price, making them more resistant to price impact from individual trades.
Thin markets, by contrast, can experience significant price movement even when relatively small orders are executed.
Order book imbalance
Order book imbalance compares the amount of buy-side liquidity with sell-side liquidity.
Persistent buy-side dominance can signal upward pressure, while sell-side dominance may signal downward pressure.
However, imbalance signals should always be validated using executed trades, since resting orders alone may not reflect real trading intent.
Price impact sensitivity
Another key liquidity indicator is how easily trades move the market.
In fragile markets, relatively small trades can cause large probability movements. In deeper markets, even large trades may produce only minor price adjustments.
Estimating price impact typically involves analyzing the relationship between trade volume and short-term price changes.
Market resilience
Resilience measures how quickly prices recover after large trades.
In thin markets, price impact can persist for long periods because liquidity is limited.
In information-driven markets, price moves tend to persist because new information has genuinely changed expectations.
Distinguishing between these two scenarios is critical when evaluating prediction market signals.
3. Volume and activity signals
Trading activity provides another important layer of signal validation.
Large probability changes accompanied by strong trading activity are typically more reliable than similar changes occurring with minimal volume.
Trade intensity
Trade intensity measures how frequently trades occur within a given time window.
High trade intensity often appears during major information events such as election debates, macroeconomic releases, or policy announcements.
These bursts of activity indicate that markets are actively processing new information.
Inter-trade timing
The spacing between trades also provides useful information.
Short inter-trade intervals typically signal active participation, while long intervals often indicate thin markets.
Monitoring these patterns helps identify whether markets are actively updating probabilities or simply drifting due to limited liquidity.
Volume distribution across probabilities
In binary prediction markets, trading activity near 50% probability often carries more informational weight than trading near extreme probabilities.
When probabilities approach 0% or 100%, mechanical constraints often limit price movement. In contrast, trading near the center of the probability range typically reflects greater uncertainty and more active expectation updates.
For this reason, some analytics pipelines weight trading activity differently depending on where trades occur within the probability range.
4. Defining signal strength in prediction markets
Ultimately, signal strength answers one question:
How much should we trust a probability change as an information update?
Several indicators help answer this.
Persistence of probability moves
One useful measure is whether probability changes persist or reverse.
If probability moves continue in the same direction over time, the market is likely incorporating new information.
If moves quickly reverse, they may reflect temporary liquidity shocks or microstructure noise.
Log-odds probability space
Because prediction market probabilities are bounded between zero and one, many quantitative systems transform probabilities into log-odds space before analyzing changes.
This transformation stabilizes variance and makes statistical comparisons across different probability levels more consistent.
Large standardized changes in log-odds space are typically treated as stronger signals, especially when they occur during high-liquidity regimes.
Cross-exchange agreement
Some events are traded across multiple prediction market platforms.
When probabilities across exchanges converge toward similar values, the signal becomes stronger because multiple groups of participants are arriving at similar expectations.
Large probability discrepancies between exchanges may indicate liquidity differences, market segmentation, or contract rule differences.
Order book and trade consistency
Another indicator of signal strength is whether order book signals translate into executed trades.
For example, if buy-side pressure appears in the order book and is followed by buy-initiated trades and upward price movement, the signal is more credible.
If order book signals fail to translate into executed trades, the liquidity displayed in the book may be fragile or stale.
5. Building a composite signal strength score
Many research systems combine multiple indicators into a single signal strength score.
Typical inputs include:
- standardized probability changes
- recent trading volume
- current bid–ask spreads
- estimated price impact from recent trades
Combining these metrics helps identify markets where probability movements are more likely to represent genuine information rather than liquidity artifacts.
Machine learning models are sometimes used to learn optimal weights for these features when predicting future probability persistence.
6. Building prediction market datasets
Developers and researchers frequently construct structured datasets from prediction market data.
Common datasets include:
Polymarket historical datasets
These typically include market metadata, lifecycle timestamps, and probability price history across multiple granularities.
Kalshi event contracts data
Kalshi markets are often used in macroeconomic research pipelines, especially for scheduled events.
Manifold probability datasets
Manifold provides a large universe of markets, though liquidity conditions vary significantly.
Liquidity-aware signal scoring is often used to identify the most informative markets.
7. Common pitfalls
Several issues frequently appear in prediction market analysis.
Treating the last trade price as ground truth in thin markets can be misleading. Liquidity metrics such as spreads and depth should always be monitored.
Near contract resolution, prediction markets often exhibit unusual volatility patterns as probabilities approach extreme values.
Comparing markets across exchanges without verifying contract resolution rules can also produce misleading conclusions.
Finally, overly complex signal models can overfit historical data. Simple features such as liquidity, volume, and persistence often generalize better.
8. Turning probabilities into research-grade signals
Prediction markets can provide powerful forecasting signals, but only when liquidity and trading activity support price discovery.
For quantitative researchers and developers, the key task is not simply retrieving probabilities but identifying which probability changes represent genuine information updates.
A typical analytics pipeline therefore includes:
- building a market universe across exchanges
- retrieving probability price history
- monitoring order book liquidity conditions
- analyzing trade activity
- combining these features into signal strength indicators
Unified access to these datasets is available through the FinFeedAPI Prediction Markets API, which aggregates prediction market data across Polymarket, Kalshi, Myriad, and Manifold.
👉 Explore the Prediction Markets API at FinFeedAPI.com and start integrating structured prediction market data into your stack with FinFeedAPI.
