Some stories are not only about what happened. They are also about how prediction-market pricing around an unresolved outcome is changing.
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. Newsrooms can use this data as an additional data source for covering elections, policy, macroeconomic events, geopolitical developments, cultural events, and other topics where relevant supported prediction markets exist.
Breaking stories often develop over hours, days, or months.
A debate happens. A court ruling lands. An economic release surprises. A candidate withdraws. A policy proposal advances.
The reporting challenge is not only to describe the event, but also to explain how market-derived expectations around the unresolved outcome changed afterward.
Relevant prediction markets may exist across different venues with different identifiers, outcomes, and contract structures.
The current price does not show whether prediction-market pricing moved gradually or abruptly.
Prices are not the same as public opinion, polling, or an official forecast.
A thin market or unusual resolution rule can materially affect how a price should be interpreted.
Reporters may spend time moving between platforms, collecting screenshots, and reconstructing historical moves.
Imagine a supported market tied to an election, policy decision, or other unresolved public event.
A reporter can ask:
This creates a stronger reporting frame than simply quoting one market price.
FinFeedAPI provides normalized listings and metadata across supported prediction-market venues. Editorial and data teams can identify markets related to political, economic, geopolitical, cultural, or other public events where relevant supported contracts exist.
Historical OHLCV provides structured market-price histories across available periods. Reporters can use this data to show how prediction-market pricing changed before and after major developments.
Latest, recent, and historical trades and quotes provide additional context around market-price changes where supported. This helps journalists describe recorded market activity without claiming it represents the views of the broader public.
Current order-book snapshots provide bids, asks, spread, and available depth where supported. This gives journalists more context before highlighting a market price in a story or graphic.
Historical order-book updates where supported can help data teams examine how liquidity and depth changed during earlier periods.
Coverage can vary by venue and market.
FinFeedAPI normalizes data across supported prediction-market platforms. Newsrooms can build cross-venue research workflows without manually integrating every source independently.
Comparable markets still need to be checked for differences in wording, outcomes, resolution rules, and liquidity.
When a major development occurs and a relevant supported prediction market exists, compare market pricing before and after the event. This can help illustrate how a specific market responded without implying that the repricing reflects the entire public.
Where relevant supported markets exist, use historical OHLCV to show how prediction-market pricing changed through debates, endorsements, withdrawals, polling releases, or other campaign events.
Prediction-market data should be presented alongside, not instead of, polls and other election evidence.
Where relevant supported contracts exist, track prediction-market pricing around economic releases, policy decisions, or regulatory outcomes. Journalists can use price histories to illustrate how market-derived expectations changed as new information emerged.
Where relevant supported contracts exist, historical market data can provide another data source for understanding prediction-market pricing around unresolved geopolitical outcomes.
Care is especially important around contract wording, liquidity, and uncertainty.
Where relevant supported markets exist, use OHLCV histories to build charts showing how prediction-market pricing changed over time.
Trades, quotes, and order-book context where supported can help explain why a single latest value should not be read in isolation.
Return to the same supported market over time and show how its pricing evolved as the story developed.
This turns prediction-market coverage from a one-off quote into a historical narrative.
| Prediction Markets Capability | Newsroom Application | Example |
|---|---|---|
| Market Listings & Metadata | Story research | Identify supported markets related to developing public events |
| Latest Market Data | Current market context | Retrieve the latest market price or price-based implied expectation where applicable |
| Historical OHLCV | Story evolution | Show how prediction-market pricing changed before and after developments |
| Trades & Quotes | Activity context | Examine latest, recent, or historical activity where supported |
| Current Order Book | Liquidity context | Inspect bids, asks, spread, and depth where supported |
| Historical Order-Book Updates | Historical market-structure research | Study past liquidity and depth where supported |
| Historical Market Records | Contract context | Retrieve historical metadata and context for supported markets |
| REST / JSON-RPC / MCP | Editorial tooling | Connect supported prediction-market data to newsroom research and data-journalism workflows |
Prediction-market data reflects activity within specific markets and participant populations. It should not be presented as equivalent to public opinion, polling, representative sentiment, or an official forecast.
| News Workflow | Before | After With Prediction Markets API |
|---|---|---|
| Market discovery | Reporters browse prediction platforms manually | Supported markets can be discovered through a normalized API |
| Story evolution | Historical price changes may be reconstructed from screenshots or manual notes | OHLCV provides structured historical market-price series |
| Activity context | A market price may be quoted without surrounding trade or quote data | Supported trades and quotes add additional context |
| Liquidity awareness | Thin markets may be reported without depth context | Current order books provide spread and depth information where supported |
| Cross-venue research | Different prediction platforms require separate research workflows | Supported venues can be accessed through a common data layer |
| Data visualization | Historical series require manual collection | Structured OHLCV can feed charts and editorial graphics |
| Follow-up reporting | Previous market states may be difficult to reconstruct | Historical data supports longitudinal coverage |
| Current monitoring | Teams may expect unsupported streaming data | Current endpoints can be polled at an appropriate cadence |
REST can support market discovery, individual market retrieval, OHLCV history, trades and quotes, current order books, and other supported Prediction Markets resources.
This can fit newsroom dashboards, internal research tools, data-journalism projects, and scheduled monitoring.
Read-only JSON-RPC provides another interface for supported Prediction Markets resources.
It can fit internal tools that prefer RPC-style data retrieval.
MCP gives compatible AI applications structured access to supported Prediction Markets tools.
This can help journalists retrieve market records or historical data while editorial interpretation and publication decisions remain with the newsroom.
Prediction Markets API does not provide WebSocket streaming.
Newsroom tools that need refreshed current market data can poll supported endpoints at an appropriate cadence.
A newsroom workflow can follow a clear sequence:
Prediction-market data can add context to journalism.
Where relevant supported prediction markets exist, use market discovery to find event contracts, historical OHLCV to show how prediction-market pricing changed, trades and quotes to add activity context, and current order books to examine liquidity where supported.
FinFeedAPI Prediction Markets API gives news and media teams a normalized data layer for event research, historical market-derived context, data journalism, and newsroom monitoring without replacing editorial judgment or traditional reporting sources.
