Consumer research often looks at what people buy, search for, watch, discuss, or say in surveys. Prediction markets add another perspective: how market participants price the likelihood of specific future outcomes.
FinFeedAPI Prediction Markets API provides structured access to supported market listings and metadata, latest and historical trades and quotes, OHLCV candles, current order-book snapshots, historical order-book updates where supported, and historical market records. Research teams can use this data to study changing market expectations around products, media, cultural events, public trends, and other consumer-facing outcomes where relevant prediction markets exist.
Surveys and focus groups capture answers at specific moments.
Search trends, social activity, and transaction data capture other parts of consumer behavior.
But researchers may also want to understand how expectations around a future outcome changed before the result became known.
Surveys, social data, search behavior, sales data, and prediction markets each measure different things.
Where relevant markets exist, a product announcement, trailer, review, campaign, or cultural event may coincide with changes in market pricing before another formal research round takes place.
A latest market price does not show how expectations evolved.
Market identifiers, outcome structures, and data formats vary between prediction platforms.
Market data needs to be interpreted as a market-derived signal, not as a representative sample of consumers.
Imagine a prediction market related to a consumer-facing event.
The useful research questions might include:
This does not tell researchers what the general population believes.
FinFeedAPI provides normalized market listings and metadata across supported prediction-market venues. Researchers can identify markets related to products, media, cultural events, public outcomes, or other consumer-facing subjects where relevant prediction markets exist.
Historical OHLCV provides structured price histories across available periods. Researchers can use these series to study how market-derived expectations changed before and after selected public events.
Latest, recent, and historical trades and quotes provide additional context around price movements where supported. This helps researchers examine recorded market activity without making claims about individual participants or their motivations.
Current order-book snapshots provide bids, asks, spread, and available depth where supported. This can help researchers understand whether a current price comes from a deeper market or a relatively thin one.
Historical order-book updates where supported can help researchers study past market-structure changes.
Coverage can vary by venue and market.
FinFeedAPI normalizes supported Prediction Markets data through a common API layer. Research teams can build cross-market and cross-venue workflows without maintaining a completely separate integration for every supported platform.
Where a relevant prediction market exists, researchers can track how market prices evolve before a product launch or another measurable product-related outcome. Those movements can then be compared with campaign activity, search trends, surveys, or internal research.
Where relevant markets exist, prediction markets around awards, releases, audience outcomes, or other cultural events can provide historical expectation signals.
Researchers can study how prices changed around trailers, reviews, announcements, or other public information.
Markets associated with public cultural or commercial outcomes can provide another way to study how expectations evolve.
This should complement, not replace, broader consumer-behavior data.
Use historical OHLCV to identify periods when market prices moved materially. The research team can then investigate which external events occurred around those timestamps and use trades and quotes where supported for additional market context.
Where similar contracts exist across supported venues, compare their historical price trajectories.
Researchers should verify wording, outcome structure, resolution criteria, and liquidity before treating markets as comparable.
Combine prediction-market observations with surveys, web analytics, search trends, social data, or internal product metrics.
The goal is not to make one source authoritative, but to study whether different signals move together or diverge.
| Prediction Markets Capability | Research Application | Example |
|---|---|---|
| Market Listings & Metadata | Topic discovery | Identify supported markets related to consumer-facing outcomes |
| Latest Market Data | Current expectation signal | Retrieve the latest market price or price-based implied expectation where applicable |
| Historical OHLCV | Expectation-history research | Study how market prices changed before and after selected events |
| Trades & Quotes | Market context | Examine latest, recent, or historical trades and quotes where supported |
| Current Order Book | Liquidity context | Inspect bids, asks, spread, and available depth where supported |
| Historical Order-Book Updates | Market-structure research | Study past order-book conditions where supported |
| Historical Market Records | Market context | Retrieve historical metadata and context for supported markets |
| REST / JSON-RPC / MCP | Research integration | Connect supported prediction-market data to analytical workflows |
Prediction-market data reflects activity within specific markets and participant populations. It should not be interpreted as a representative consumer survey unless the research methodology independently establishes that connection.
| Consumer Research Workflow | Before | After With Prediction Markets API |
|---|---|---|
| Market discovery | Researchers browse prediction platforms manually | Supported markets can be discovered through a normalized API |
| Expectation history | Historical market-price changes are difficult to collect consistently | OHLCV provides structured historical price series |
| Trades and quotes | Price moves may lack surrounding market context | Supported latest, recent, and historical trades and quotes add context |
| Liquidity analysis | Researchers may see a price without understanding market depth | Current order-book snapshots add spread and depth context where supported |
| Historical market structure | Past order-book conditions may be difficult to study consistently | Historical order-book updates can support market-structure research where available |
| Cross-venue comparison | Each platform requires separate integration and normalization | Supported venues can be accessed through a common data layer |
| Mixed-signal research | Prediction-market data is difficult to combine consistently with other datasets | Normalized API data can feed broader research pipelines |
| Current monitoring | Teams may expect unsupported streaming access | Current resources can be polled through supported interfaces |
REST can support market discovery, individual market retrieval, OHLCV history, trades and quotes, current order books, and other supported resources.
This fits research notebooks, internal dashboards, analytical pipelines, and scheduled monitoring.
Read-only JSON-RPC access provides another interface for supported Prediction Markets resources.
It can fit research systems that prefer RPC-style data retrieval.
MCP gives compatible AI applications structured access to supported Prediction Markets tools.
This can help applications discover markets, retrieve historical data, or inspect supported market resources while interpretation remains with the research team.
Prediction Markets API does not provide WebSocket streaming.
Applications that need refreshed current market data can poll supported endpoints at an appropriate cadence.
A research workflow can follow a clear sequence:
Use structured market discovery to find relevant contracts, historical OHLCV to study changing expectations, trades and quotes to add market context, and current order books to examine liquidity where supported.
FinFeedAPI Prediction Markets API gives research teams a normalized way to incorporate prediction-market data into broader consumer, product, media, and cultural research workflows without relying on manual collection across multiple venues.
