How to Track a Prediction Market From Listing to Resolution

A prediction market might start with a 20% probability, climb to 75%, and eventually resolve in the opposite direction.

But how do you track everything that happens between those moments?

For developers building prediction market dashboards, monitoring tools, or research applications, tracking the latest price isn't enough.

You also need to know when a market appeared, how trading activity changed, when it closed, and which outcome ultimately won.

This is called prediction market lifecycle tracking.

Let’s discuss how to track a prediction market from listing to resolution using market IDs, activity data, and the FinFeedAPI Prediction Markets API.

1. Understanding the Prediction Market Lifecycle

Every prediction market follows a lifecycle, although not every market reaches resolution in the same way.

Some markets may also be suspended, cancelled, or invalidated.

One distinction matters particularly: a closed market is not necessarily a resolved market.

For example, a market predicting an election result might stop trading when voting ends, but remain unresolved until the official result is confirmed.

A reliable tracking system needs to recognize that difference.

2. Why Market IDs Matter More Than Market Titles

The first step is identifying the market you want to track.

Market titles are useful for humans, but unreliable as database identifiers. Different exchanges can list similar questions, while individual outcomes may have their own tradable identifiers.

For example, a prediction market might expose two outcomes:

BITCOIN-ABOVE-58K-ON-JUNE-5-2026_YES

BITCOIN-ABOVE-58K-ON-JUNE-5-2026_NO

These identifiers distinguish the two outcomes associated with the same underlying question.

For ongoing tracking, developers should use:

exchange_id + market_id

And preserve outcome-specific information whenever available.

FinFeedAPI provides an endpoint to discover active market IDs:

GET /v1/markets/{exchange_id}/active

Once an ID is found, developers can retrieve its metadata, including the title, description, outcome, and current status.

This allows the application to identify the same contract across repeated requests without relying on its name.

See the Prediction Markets API documentation.

3. Tracking Activity and Probability Changes

Once a market is listed, the next step is monitoring what happens while people trade.

FinFeedAPI offers two useful activity endpoints:

This information helps developers monitor price changes, trading activity, and available liquidity.

However, the latest trade price isn't always the best representation of current market expectations.

Consider this example:

• Last traded price: $0.58
• Best bid: $0.62
• Best ask: $0.64
• Quote midpoint: $0.63

The last trade suggests a 58% implied probability, while the current quote midpoint suggests approximately 63%.

Both are useful, but they measure different things.

Developers should also account for missing activity. A market without recent trades isn't necessarily closed or invalid. It may simply have low liquidity.

For historical analysis, FinFeedAPI also provides prediction market OHLCV data, allowing researchers to examine how prices evolved over time rather than relying on individual snapshots.

4. How to Detect Market Closure and Resolution

What happens when a market disappears from the active market list?

It might have closed, resolved, been suspended, or become temporarily unavailable.

This is why a lifecycle tracker shouldn't assume that disappearing from the active list means the outcome is settled.

Instead, developers should continue checking the market's metadata and status.

FinFeedAPI standardizes market statuses such as:

• Open — the market is available for trading.
• Closed — trading has ended.
• Resolved — the final outcome has been determined.
• Suspended — market activity has been temporarily halted.

When resolution is confirmed, the application should preserve the final outcome information available from the venue.

Useful records include the winning outcome, resolution time, settlement details where available, and the last observed probability before closure.

These details are especially important for applications evaluating whether historical market expectations matched actual results.

5. Building a Simple Prediction Market Tracking Workflow

A prediction market tracker doesn't need to be overly complicated.

A practical workflow can follow five steps:

Step 1: Discover markets. Retrieve active market IDs for the selected exchange and identify newly listed contracts.

Step 2: Save market metadata. Store the exchange ID, market ID, outcome information, and initial status.

Step 3: Monitor activity. Periodically retrieve recent trades, quotes, and price observations. Save timestamps so historical changes can be reconstructed.

Step 4: Check lifecycle status. Detect when a market becomes inactive, closes, or resolves. Don't treat missing activity as proof of resolution.

Step 5: Archive resolved markets. Preserve the final result alongside historical probability observations for future analysis.

Polling frequency can vary. An actively traded market might be checked every few minutes, while a closed market may only need occasional resolution checks.

The goal is simple: maintain a continuous record of the same market without losing its history when its status changes.

6. Why Tracking the Full Lifecycle Matters

Prediction market data becomes considerably more valuable when you can connect historical expectations with actual outcomes.

For example, a researcher might want to understand:

• Did the market predict the final result correctly?
• How quickly did probabilities react to new information?
• Did trading activity increase before a major price movement?
• How did expectations change in the hours before resolution?

Answering these questions requires more than the latest probability.

It requires market identifiers, historical activity, outcome data, and reliable lifecycle tracking.

With the FinFeedAPI Prediction Markets API, developers can access standardized market data across supported prediction market venues through REST, JSON-RPC, and MCP, without building a separate integration for every exchange.

The principle is straightforward: a market ID tells you what you're tracking, activity data shows how expectations change, and resolution data tells you what ultimately happened.

Together, they turn individual market prices into a useful historical record.

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