Prediction Market Data API

A prediction market price can look deceptively simple.

A YES contract trading at 0.83 may suggest roughly a 83% implied probability. But for developers, analysts, and AI systems, that number alone is rarely enough.

Was it the last trade or the current midpoint?

How liquid is the market?

When did it last trade?

Is the spread 2% or 30%?

Did the probability move on meaningful volume?

Is the market still active?

A useful prediction market data API needs to provide the underlying data required to answer those questions.

FinFeedAPI provides normalized prediction market data across multiple venues, giving developers access to market metadata, activity, trades, quotes, OHLCV, order books, and historical data through REST, JSON-RPC, and MCP.

The important part is what you build from that data.

From Market Data to a Forecast Signal

Think about prediction market data in three layers:

The API provides the raw data layer. Your application can then transform it into the probability series and signals required for a particular use case.

A typical pipeline looks like this:

1Prediction Markets API
2        ↓
3Market discovery
4        ↓
5Trades / Quotes / Order Books / OHLCV
6        ↓
7Probability normalization
8        ↓
9Historical probability series
10        ↓
11Signal quality filters
12        ↓
13Alerts / Models / AI Agents / Research

This distinction matters: Prediction market data is not automatically a forecast signal.

1. Start With the Market, Not the Price

Before interpreting a price, you need to know exactly what the contract represents.

Market metadata can include information such as the exchange, market identifier, title, description, status, outcomes, timestamps, and settlement information where available.

This gives applications the context required to distinguish between:

• active markets
• closed markets
• resolved markets
• different outcomes within the same market
• similar contracts on different venues

A market at 72% is meaningless to a model if the system does not reliably know which outcome that price belongs to or whether the contract is still trading.

This is especially important when building across multiple prediction-market venues, where identifiers, naming conventions, contract structures, timestamps, and API formats can differ.

FinFeedAPI normalizes access to this data so applications can use a common ingestion layer rather than maintaining a separate integration for every venue.

2. Turn Market Prices Into Implied Probabilities

For many prediction-market contracts, the first transformation is straightforward.

If the price is represented between 0 and 1:

1probability = price

A price of:

10.83

can therefore be interpreted as roughly:

183% implied probability

But deciding which price to use is more important than the conversion itself.

Last Trade vs. Quote Midpoint

The last traded price may be old or based on very little volume.

When bid and ask data are available, one alternative is the midpoint:

1mid_probability = (best_bid + best_ask) / 2

For example:

1best_bid = 0.61
2best_ask = 0.65
3
4mid_probability = 0.63

That produces a 63% midpoint probability.

But now consider:

1best_bid = 0.45
2best_ask = 0.75

The midpoint is still 60%, but the 30 percentage-point spread tells a very different story.

The estimate exists, but the underlying signal is weak.

3. Use Trades, Quotes, and Order Books to Measure Signal Quality

Two markets can both show a 70% implied probability while providing very different levels of confidence.

One might have frequent trades, significant depth, and a narrow spread. The other may not have traded for hours and could have very little liquidity.

That is why production systems should look beyond price.

Trades

Trade data can help determine whether a probability move is supported by actual market activity.

Useful calculations include:

1trade_count = number of trades during window
2
3volume = sum(trade size during window)
4
5VWAP = sum(price × size) / sum(size)

VWAP can be particularly useful when a small trade temporarily moves the latest price away from the level where most activity occurred.

Quotes

Quotes provide another view of current expectations.

From the best bid and ask, developers can calculate:

1mid = (best_bid + best_ask) / 2
2
3spread = best_ask - best_bid
4
5relative_spread = spread / mid

A narrow spread generally provides a stronger basis for interpreting the midpoint than a very wide one.

Order Books

Order books add information about available liquidity around the current probability.

Developers can measure:

1bid_depth = sum(bid sizes)
2
3ask_depth = sum(ask sizes)
4
5imbalance =
6(bid_depth - ask_depth)
7/
8(bid_depth + ask_depth)

They can also calculate depth around the midpoint or estimate how much trading would be required to materially move the market.

This helps distinguish a meaningful repricing from a probability jump caused by a thin book.

4. Build Historical Probability Time Series With OHLCV

Current probability answers:

What does the market imply now?

Historical prediction market data lets you ask:

How did expectations get here?

OHLCV data provides a practical way to turn market activity into historical probability series.

In this context, candles can be interpreted as:

From those observations, developers can calculate metrics such as:

1probability_change = close - open
2
3range = high - low
4
5momentum = close_now - close_N_periods_ago

Historical data can then support backtesting, event studies, volatility analysis, forecast evaluation, and AI research workflows.

Instead of storing only the current probability, applications can maintain the complete path of changing market expectations.

5. Do Not Treat Every Probability as Equally Strong

A useful forecast pipeline should evaluate both the signal and the quality of the signal.

Consider two markets:

The headline probability is identical. The underlying market conditions are not.

Developers can therefore create their own signal-quality model based on factors such as:

• recency
• liquidity
• spread
• volume
• market status
• outcome structure

A simplified custom score might look like:

1signal_score =
2    recency_weight
3    × liquidity_weight
4    × volume_weight
5    × spread_penalty

This is derived analytics built on top of the API, rather than a probability score that FinFeedAPI itself assigns.

The exact formula should depend on the application. A hedge fund research model may use different thresholds from a consumer forecast dashboard.

6. Detect When the Market Changes Its Mind

Once probabilities are stored as a time series, prediction market data becomes useful for event monitoring.

Instead of continuously checking individual markets, applications can detect significant changes automatically.

For example:

1Probability moved > 5 percentage points in 1 hour
2
3Probability crossed above 50%
4
5Volume increased to 3× its normal level
6
7Bid/ask spread narrowed significantly
8
9Order book imbalance changed direction
10
11Market status changed to resolved

A stronger monitoring rule can combine multiple conditions.

1if abs(probability_now - probability_1h_ago) >= 0.05
2and volume_1h > minimum_volume
3and spread < maximum_spread:
4    trigger_alert()

The volume and spread filters help prevent an application from treating every move in an illiquid market as a meaningful change in expectations.

This turns an API for prediction markets into an event-monitoring layer.

7. Multi-Outcome Markets Require More Care

Not every prediction market is binary.

Markets can represent:

• election candidates
• sports winners
• interest-rate ranges
• economic-data ranges
• nominations
• crypto price bands

In these markets, raw implied probabilities may not add up exactly to 100%.

For analytical purposes, developers may choose to normalize them:

1normalized_probability_i =
2raw_probability_i
3/
4sum(raw_probabilities_for_all_outcomes)

Suppose:

1Outcome A = 0.52
2Outcome B = 0.35
3Outcome C = 0.20
4
5Total = 1.07

The normalized values become approximately:

1A = 48.6%
2B = 32.7%
3C = 18.7%

But the original prices should still be preserved.

Raw prices represent actual market conditions.

Normalized probabilities are derived analytics.

Keeping both makes it possible to reproduce calculations and understand what the market actually displayed at a particular point in time.

8. Cross-Venue Data Needs Context

Prediction market venues can differ in market IDs, outcome naming, price representation, timestamps, contract structures, liquidity, resolution rules, and historical-data availability.

A normalized prediction market data API reduces the engineering required to consume these different sources.

But normalization does not mean that two apparently similar markets are automatically equivalent.

For example, two venues may offer contracts that appear to ask the same question but have different:

• resolution criteria
• close times
• wording
• eligible outcomes
• settlement rules

Applications should verify those details before treating the markets as directly comparable.

FinFeedAPI provides normalized access across venues. Determining that two contracts represent equivalent real-world questions remains part of the analytical layer.

9. REST, JSON-RPC, or MCP?

Different applications may need different ways to interact with prediction market data.

REST

REST fits conventional application and data infrastructure.

Typical workflows include market discovery, retrieving market metadata, accessing historical OHLCV, querying activity, and retrieving order books.

It works well for dashboards, backend services, historical analysis, and scheduled data pipelines.

JSON-RPC

JSON-RPC provides method-style access for applications that prefer an RPC architecture.

It can fit infrastructure where data operations are exposed through centralized programmatic methods rather than resource-style endpoints.

MCP

MCP makes the prediction-market data layer accessible to AI-agent workflows.

An agent could use available tools to find relevant markets, retrieve historical information, inspect current activity, and analyze changes.

For example:

1Find active markets related to the next Fed decision.
2
3Summarize how probabilities changed this week.
4
5Find markets with significant recent probability moves.
6
7Compare similar contracts across venues after verifying their terms.

MCP gives the model access to the data layer. It does not remove the need for deterministic calculations, validation, liquidity checks, and other guardrails.

10. What Can You Build With a Prediction Markets API?

Once raw prediction market data has been converted into structured probability observations, several applications become possible.

Forecast Dashboard

A dashboard could combine:

1Current probability
224h probability change
37d probability change
4Volume
5Spread
6Last activity
7Market status

Instead of showing a single number, users see both the forecast and the market conditions behind it.

Event Alerting System

An alerting service could monitor hundreds of markets and surface only meaningful changes.

For example:

1Probability +10 percentage points in 24h
2Probability crossed 50%
3Volume > 3× moving average
4New market appeared for tracked topic

AI Research Agent

An AI agent could discover markets, retrieve historical probability data, summarize changes, identify stale markets, and create watchlists.

The underlying calculations can remain deterministic while the agent handles discovery and interpretation.

Quant and Hedge Fund Research

Historical prediction market datasets can support questions such as:

1Do prediction markets move before or after major news?
2
3How stable are high-probability forecasts?
4
5Does trading volume predict forecast stability?
6
7Does order book imbalance precede probability changes?

Resolved markets can also be used for forecast evaluation.

For a binary outcome, one common metric is the Brier score:

1Brier score = (forecast_probability - actual_outcome)²

where the final outcome is represented as either 0 or 1.

11. A Practical Prediction Market Data Architecture

A production implementation might look like this:

1FinFeedAPI Prediction Markets API
2              ↓
3       Ingestion Service
4              ↓
5       Normalization Layer
6              ↓
7   ┌──────────┴──────────┐
8   ↓                     ↓
9Market Metadata     Time-Series Data
10   ↓                     ↓
11   └──────────┬──────────┘
12              ↓
13        Signal Engine
14              ↓
15 Dashboards / Alerts / AI Agents
16       / Models / Research

The ingestion layer retrieves the required prediction market data and handles pagination, retries, rate limits, and synchronization.

The normalization layer converts values into a consistent internal representation while preserving the original raw data.

Storage separates relatively stable market metadata from high-volume time-series observations.

Finally, the signal layer calculates probability changes, spread and liquidity filters, volume changes, status transitions, and application-specific signal scores.

The Biggest Mistake: Treating Probability as the Whole Dataset

A prediction market displaying 80% does not mean your application has an 80% forecast signal ready to use.

You still need to ask:

• How recent is it?
• How wide is the spread?
• How much volume supports it?
• How deep is the market?
• What happened to the probability over time?
• Is the market still active?
• Does another venue define the same event differently?

And a 90% probability can still resolve false. Probability expresses uncertainty; it is not a guarantee.

That is why useful prediction market infrastructure needs more than a probability endpoint.

Build Forecast Signals With FinFeedAPI

FinFeedAPI's Prediction Markets API gives developers a normalized data layer for working with prediction markets across multiple venues.

Access market metadata, activity, trades, quotes, OHLCV, order books, and historical data through developer-friendly interfaces including REST, JSON-RPC, and MCP.

Use the raw data to build your own probability time series, event monitoring, AI-agent workflows, research datasets, and forecast signals.

Explore the Prediction Markets API

Start with FinFeedAPI

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