Anyone can fetch a probability.
The harder question is whether you can reconstruct what happened around it.
- Did a tradable outcome move from 42% to 61% in a single thin trade?
- Did the order book reprice with it? Did a comparable contract on another venue move earlier or later?
- Can you build a reproducible historical workflow around that movement?
If your data source only gives you the current probability, none of those questions are answerable.
That is the difference between an odds feed and a prediction markets API built as market-data infrastructure.
The Last Price Hides Most of the Market
Imagine a tradable outcome currently trading at 0.64.
That number tells you almost nothing about how it got there.
These two paths end at exactly the same price:
The first could represent hours of gradual repricing.
The second could represent one piece of information hitting the market and changing expectations almost instantly.
Same last price. Completely different path.
To distinguish them, you need the layers underneath:
tradable outcome → OHLCV → trades → quotes → order book
And you need consistent identifiers plus standardized timestamps, including exchange and processing timing where applicable.
OHLCV Is Where Probability Becomes Researchable
Raw trades are useful. They are also a bad default interface for most research.
Prediction markets can be extremely bursty. A contract can sit almost untouched and then reprice dramatically within minutes after an announcement, economic release, debate, court decision, geopolitical event, or other new information.
Fixed-period prediction market OHLCV gives that movement structure.
A one-minute candle can show the immediate information shock.
A 15-minute candle can show whether the move persisted.
An hourly series can show the broader repricing path.
A daily series can show how expectations evolved across the life of the contract.
This is why candles matter here. They are not simply a chart format. They are a compression layer between individual market events and the research question you actually want to ask.
But prediction market candles have their own traps.
A move from 0.41 to 0.67 is not equivalent to a stock moving from $41 to $67. It represents a change in implied probability.
Empty intervals matter too. No candle may mean there were no trades rather than missing data, depending on the dataset and interval.
And resolution creates an important boundary. A price approaching 1 before an event resolves is still a forecast. A resolved outcome represents something fundamentally different from that pre-resolution probability.
Treating those observations as equivalent can distort historical analysis.
OHLCV Alone Still Isn't Enough
Suppose you detect this candle:
Interesting.
But what actually happened?
OHLCV cannot tell you whether the move came from one aggressive trade, sustained trading activity, changing quotes, or a broader repricing of available liquidity.
That requires the underlying market layers.
OHLCV tells you where the probability moved.
Trades show the individual prints behind the movement.
Quotes add information about available prices.
Order book data provides another view into liquidity and depth.
For historical order book analysis, however, there is an important distinction: historical data may consist of order updates rather than ready-made historical book snapshots. Reconstructing the state of the book at a particular moment can therefore require replaying those updates correctly.
For serious research, the useful object is not simply the candle.
It is the candle plus the underlying market activity that produced it.
Then Add a Second Venue
This is where prediction market infrastructure becomes considerably harder.
A single Polymarket integration is manageable.
A single Kalshi integration is manageable.
Combining Polymarket, Kalshi, Gemini Prediction Markets, ForecastEx, Crypto.com Prediction Markets, Hyperliquid Outcome Markets, Myriad, Pascal, and Manifold is a different problem.
Because these platforms do not describe markets the same way.
They have different identifiers, market structures, APIs, timing conventions, collateral models, liquidity conventions, and settlement mechanisms.
The expensive part is no longer simply sending HTTP requests.
The expensive part is normalization.
FinFeedAPI provides consistent exchange_id and market_id fields and normalized schemas across supported venues.
Importantly, market_id identifies a specific tradable outcome or instrument. In a binary prediction market, YES and NO may therefore exist as separate records rather than one identifier representing the entire real-world question.
And normalization does not mean FinFeedAPI automatically determines that two contracts on different venues represent the same real-world event.
For cross-venue research, users still need to identify the contracts they consider comparable.
What the normalized API does is make the next step much easier: once those instruments have been selected, they can be queried through consistent fields and data structures instead of completely different venue APIs.
Normalize the Fields. Not the Economics.
Normalization has a dangerous failure mode of its own.
Making two datasets look similar does not make the underlying markets identical.
Consider volume.
Activity reported by Polymarket should not automatically be compared directly with activity on Manifold or a regulated event-contract venue. Units, collateral, market mechanisms, and economic meaning can differ.
The same caution applies to liquidity and spreads.
Different market structures can expose superficially similar numbers that describe different underlying behavior.
A useful unified API therefore should not erase those differences.
It should normalize the data model enough to make analysis practical while preserving the venue context needed to interpret it correctly.
That distinction becomes particularly important for cross-venue research.
The Interesting Question Is: Who Moved First?
Once you have identified comparable tradable outcomes across venues, normalized data makes a different class of analysis possible.
Instead of asking:
What probability does each platform show?
you can investigate:
Which venue repriced first?
Align comparable probability series around the same news event.
Then examine their paths.
Now the dataset can be used to study information transmission.
You can investigate which venue reacted first, how quickly comparable contracts elsewhere followed, where trading activity concentrated, and what happened to available liquidity around the move.
But that comparison begins after comparable contracts have been identified. A consistent API schema makes cross-venue analysis easier; it does not automatically create a canonical cross-venue event mapping.
That distinction matters.
A Prediction Markets API Should Let You Move Down the Stack
The useful workflow is simple.
Start with the aggregate.
Find the unusual candle.
Then move closer to the underlying market.
With FinFeedAPI, that means identifying a tradable outcome, retrieving its OHLCV history, and then querying activity or order book data using the corresponding exchange_id and market_id.
For example:
The important part is not the endpoint itself.
It is that the candle does not have to be the end of the investigation.
Researchers can move from the aggregate toward the underlying market activity and, where historical order book updates are available, reconstruct book state by replaying those updates as required.
This Also Changes What AI Can Do With Prediction Markets
There is another reason this structure matters.
AI systems are increasingly becoming consumers of financial APIs.
Giving an agent a webpage containing:
→ Probability: 64%
does not provide much analytical context.
Giving it structured access to market discovery, historical candles, activity, and order book data creates something different.
An agent can identify a probability movement, retrieve surrounding market activity, and compare selected instruments across venues.
FinFeedAPI exposes Prediction Markets data through MCP as well as REST and JSON-RPC.
The same platform also provides Stocks, SEC Filings, and Currencies data, opening the door to research workflows that combine prediction markets with other financial datasets.
For example, once the relevant historical stock data is available, a research workflow could examine a prediction-market probability movement alongside the historical price behavior of a related equity.
The important shift is from asking:
“What does the prediction market say?”
to asking:
“How did this probability change, what market activity accompanied the move, and what else was happening around the same event?”
That is where prediction market data becomes part of financial research infrastructure rather than an isolated probability feed.
What We Think a Prediction Markets API Should Provide
If you are evaluating a prediction market data provider, don't start with the number of endpoints.
Start with what the data lets you investigate.
You should be able to identify tradable outcomes consistently, retrieve their history, inspect fixed-period probability movements, drill into the activity behind those movements, and apply a similar workflow across supported venues.
That requires:
- consistent identifiers for tradable outcomes
- multi-venue schema normalization
- historical market activity
- current order book data and historical order updates where supported
- prediction market OHLCV across useful research periods
- standardized timestamps with exchange and processing timing where applicable
- interfaces that work for applications and AI agents
FinFeedAPI brings those layers together across nine prediction market venues through REST, JSON-RPC, and MCP.
Because prediction market data should tell you more than what the probability is now.
It should help you understand how it got there.
Build Research Around the Market, Not Just the Last Price
Explore FinFeedAPI Prediction Markets API for normalized market data, OHLCV, activity, and order book data across nine venues.
→ Start building with FinFeedAPI
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