A prediction market can move from 42% to 68% in a few hours.
Interesting… but that is only part of the story.
For a developer, the harder question is: what happened to this market from the moment you discovered it until the event was finished?
Was it still open? When did trading stop? Which outcome were you actually tracking? What did prices look like before the market closed? And can you come back weeks later and reconstruct that lifecycle?
That requires more than a price request. It requires a prediction-market lifecycle monitor.
With the FinFeedAPI Prediction Markets API, you can build one around a relatively simple pattern: discover the market, identify its tradable outcomes, monitor its status and activity, preserve history, and recognize when it moves out of the active universe.
A Prediction Market Is More Than a Price
A normal market-data dashboard can often start with a symbol and continuously update a price.
Prediction markets introduce another dimension: state.
A contract exists because something has not happened yet. While the question remains unsettled, participants trade different possible outcomes. Eventually the market can close or resolve.
That means your application needs to answer several questions at once:
- Which event am I tracking?
- Which specific outcome does this
market_idrepresent? - Is the market still active?
- What is its latest price?
- What trading and quote activity has occurred?
- How has its price changed over time?
- Has its status changed since my last check?
FinFeedAPI exposes market metadata, activity, order books, and OHLCV through a common Prediction Markets API, making these pieces easier to connect into one workflow.
Step 1: Discover the Market
The first job is finding the market you want to monitor.
FinFeedAPI provides an active-markets endpoint:
There is an important detail here: /active is designed as a lightweight discovery signal and returns currently active market IDs only.
It does not return complete market objects.
Once you discover an ID, you can retrieve its full market information with:
For a broader, paginated market universe, including markets with statuses such as Open, Closed, and Resolved, you can use:
This gives a lifecycle monitor a clean pattern.
Use /active to discover currently tradable IDs. Use the single-market endpoint when you need the current metadata and status of a market you are already tracking. Use /history when you need to search or revisit the wider historical market universe.
Step 2: Treat market_id as an Outcome Identifier
One detail can easily trip up a new integration.
In the FinFeedAPI Prediction Markets API, a market_id identifies a specific tradable outcome, not simply the question as a whole.
A binary prediction market may therefore appear as two records:
Each is a separate tradable outcome with its own identifier.
A market record can include fields such as:
This has an important architectural consequence.
Do not use the human-readable title as your primary key.
Titles are useful for search and display, but your monitor should store at least the exchange_id and market_id, along with metadata such as the title, outcome name, outcome type, and observed status.
That gives you a stable reference for subsequent activity, OHLCV, and order-book requests.
Step 3: Separate Metadata From Market Activity
Once you know what you are tracking, avoid treating every request as the same type of update.
Your monitor really has two jobs.
The first is state monitoring: is the market Open, Closed, or Resolved? Has its metadata changed?
For a known market, the single-market endpoint provides a straightforward way to check its current information:
The second job is market-data monitoring: what are traders doing?
FinFeedAPI provides two useful activity patterns:
Use current when you want the current/latest trade and quote. Keep in mind that these values are not guaranteed to be populated: either the trade or quote can be null, and low-activity markets can produce sparse or no recent activity.
Use latest when you want several recent trades and quotes and therefore a broader view of recent market activity.
This matters for lifecycle logic. A quiet market is not necessarily a closed market, and missing recent activity should not be treated as a status change.
If market depth matters, you can also retrieve the current order book:
This returns the current snapshot with bids and asks for the selected outcome.
Historical order-book data is different: it contains raw order updates rather than reconstructed historical snapshots. Applications that need historical book state should account for that distinction in their data pipeline.
Step 4: Build a Price History, Not Just a Latest-Price Cache
A lifecycle monitor becomes much more valuable when it remembers what happened.
Suppose an outcome moves from 0.35 to 0.70 and eventually leaves the active market set.
If your system only stores the latest observation, most of the useful information disappears.
Historical OHLCV gives you a compact way to preserve the price path:
You select a period_id and can bound the request with time_start and time_end.
Historical rows are returned in ascending time order and can contain fields such as:
For a dashboard, hourly candles might be enough. For research around fast-moving events, shorter intervals may be more useful.
The important part is choosing the resolution based on the question you want to answer rather than simply collecting the highest possible granularity.
Step 5: Keep Raw Activity When the Sequence Matters
Candles are compact, but compression has a cost.
If you are studying exactly how a prediction changed around an announcement, election result, economic release, or other event, OHLCV may hide the sequence you care about.
FinFeedAPI also provides historical trades and quotes. Historical quote data includes exchange and processing timing, allowing applications to distinguish when an event occurred at the exchange from when it was received and processed.
That gives you two useful levels of history:
Compact history: OHLCV for charts, broad research, and longer-term monitoring.
Event-level history: trades and quotes when individual market events and their timing matter.
You do not necessarily need the heavier dataset for every market.
A practical system can monitor many markets using metadata and OHLCV, then collect or backfill event-level history for the subset that becomes important.
Step 6: Watch for Lifecycle Changes
This is where a price tracker becomes a lifecycle monitor.
Store the last known state of each tracked outcome and compare it with later observations.
Conceptually:
And later:
The /active endpoint gives you currently active market IDs, while /history provides the broader market universe, including Open, Closed, and Resolved markets.
This creates a useful monitoring signal.
If a previously tracked market_id disappears from /active, do not automatically mark it as resolved.
Instead, re-check the market:
Then inspect its actual status.
That small distinction prevents an important monitoring mistake: assuming that “not currently active” and “resolved” mean the same thing.
Step 7: Preserve the Final Record
When your monitor observes that a market has reached its final state, do not simply stop polling and discard the context.
Finalize the record.
A useful stored lifecycle might look conceptually like this:
The exact schema depends on your application.
The principle is more important: resolution should turn a live market into a durable historical record.
That record can then feed research, backtests, market-accuracy studies, AI training datasets, dashboards, or event-driven analytics without requiring you to reconstruct everything from scratch.
A Simple Monitoring Loop
You do not need a complicated architecture to get started.
A basic implementation can follow this pattern:
The polling frequency should depend on the application.
A research archive does not need to check every market every second. A live probability dashboard may refresh much more frequently.
Separating market discovery, status checks, and market-data collection can also reduce unnecessary requests.
What This Lets You Build
Once lifecycle monitoring is in place, the interesting work starts.
You can measure how probabilities reacted to new information. Compare early expectations with later market states. Study volatility before markets closed. Build datasets of historical prediction-market behavior. Or examine how different venues represented similar events.
The same architecture can also support historical research. Instead of only monitoring markets from the moment your application discovers them, historical market, OHLCV, trade, and quote data can be brought into the same research pipeline.
That means the lifecycle model is useful for both live monitoring and historical reconstruction.
One Market, One Lifecycle
A prediction-market integration becomes much more useful when you stop thinking of a contract as a number that needs refreshing.
It is an object with a lifecycle.
It appears. It trades. Its implied probability changes. New information reaches the market. Activity accelerates or disappears. Its state changes. Eventually, it leaves the active universe and can become part of your historical dataset.
Your application should be able to follow that entire path.
FinFeedAPI provides the building blocks to connect market discovery and metadata, current activity, order books, OHLCV, and historical trades and quotes into one monitoring workflow.
Build Your Prediction-Market Monitor
Use the FinFeedAPI Prediction Markets API to discover active market IDs, retrieve market metadata, track individual outcomes, monitor activity, and preserve historical data throughout the market lifecycle.
Instead of storing only the latest probability, build a record you can still analyze after the market is over.
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