Event-driven strategies depend on how markets price uncertainty before and around important 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.
Trading and research teams can use this data as an additional input when studying event expectations, market repricing, liquidity, and historical behavior around macro, policy, geopolitical, corporate-related, and other event-driven scenarios where relevant prediction markets exist.
Event-driven trading often starts with a calendar.
An earnings announcement, economic release, policy decision, election, legal ruling, or another event may be scheduled in advance.
What remains uncertain is the outcome, how prediction markets price it, and how other markets may respond.
News, analyst commentary, options, price action, and prediction markets each provide different signals about what the market expects.
Relevant markets may exist across different venues with different identifiers, structures, and contract rules.
The current market price does not show how expectations changed during the days or weeks before the event.
A price from a thin market should not automatically be treated the same way as one supported by deeper activity.
Without structured historical data, teams may rely on screenshots, manual notes, or ad hoc observations.
Imagine a prediction market related to an upcoming event.
Instead of asking only:
An event-driven researcher can ask:
The prediction-market observation becomes one event-related input.
FinFeedAPI provides normalized market listings and metadata across supported prediction-market venues. Teams can identify markets related to macroeconomic, policy, geopolitical, corporate-related, or other public events where relevant contracts exist.
Historical OHLCV provides structured market-price histories across available periods. Researchers can study how market prices changed before, during, and after selected event windows.
Latest, recent, and historical trades and quotes provide additional context around price changes where supported. This helps teams examine recorded market activity without assuming that a price move necessarily represents broad consensus.
Current order-book snapshots provide bids, asks, spreads, and available depth where supported. This allows researchers to evaluate current liquidity conditions alongside the market price.
Historical order-book updates where supported can help teams investigate how liquidity and market depth changed around earlier event periods.
Coverage can vary by venue and market.
Follow a relevant supported prediction market in the period leading up to a scheduled event. Use historical OHLCV to study whether market pricing remained stable, trended gradually, or repriced sharply.
Define a window around an announcement or outcome and compare prediction-market prices before and after the event.
This can become one component of broader event-study research.
Align prediction-market histories with external asset data such as equities, rates, FX, commodities, or crypto.
The trading team can then test whether prediction-market repricing has any useful relationship with another market under its own methodology.
FinFeedAPI Prediction Markets API itself does not provide or establish that relationship.
Use current order-book information and trades and quotes where supported to avoid treating every market price as equally informative. Teams can define their own thresholds for spread, depth, activity, or market eligibility.
Compare similar supported contracts across venues where available.
Before combining signals, verify differences in market wording, outcome definitions, resolution criteria, liquidity, and status.
Use OHLCV, trades, quotes, and other supported historical data to test event-related hypotheses across earlier market periods. This can support historical research and backtesting-style analysis without implying that historical results predict future trading performance.
| Prediction Markets Capability | Trading Research Application | Example |
|---|---|---|
| Market Listings & Metadata | Event-market discovery | Identify supported markets related to upcoming events |
| Latest Market Data | Current event signal | Retrieve the latest market price or price-based implied expectation where applicable |
| Historical OHLCV | Repricing analysis | Study how market prices changed before and around an event |
| Trades & Quotes | Activity context | Examine latest, recent, and historical activity where supported |
| Current Order Book | Liquidity research | Inspect bids, asks, spread, and depth where supported |
| Historical Order-Book Updates | Historical liquidity analysis | Study past order-book conditions where supported |
| Historical Market Records | Contract context | Retrieve historical metadata and context for supported markets |
| REST / JSON-RPC / MCP | Trading research integration | Connect supported prediction-market data to event-driven research workflows |
Prediction-market data provides market observations. Strategy logic, signal construction, position sizing, execution, risk management, and trading decisions remain the responsibility of the trading team.
| Event-Driven Workflow | Before | After With Prediction Markets API |
|---|---|---|
| Event-market discovery | Traders manually browse prediction platforms | Supported markets can be discovered through a normalized API |
| Expectation history | Historical market-price changes may be difficult to collect consistently | OHLCV provides structured historical price series |
| Activity context | Price changes may be analyzed without trade or quote data | Supported trades and quotes provide additional context |
| Liquidity research | Latest prices may be viewed without depth information | Current order books provide spread and depth context where supported |
| Historical liquidity | Past order-book conditions may be difficult to reconstruct | Historical order-book updates can support research where available |
| Cross-venue analysis | Each platform requires separate integration and normalization | Supported venues can be accessed through a common data layer |
| Strategy research | Event signals may rely on manual snapshots and notes | Structured historical data can feed systematic research pipelines |
| 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 fits event calendars, research notebooks, analytical pipelines, and dashboards.
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 support market discovery, historical research, and event analysis while trading decisions remain outside the data layer.
Prediction Markets API does not provide WebSocket streaming.
Applications that need refreshed current market information can poll supported endpoints at a cadence appropriate to their research and monitoring requirements.
An event-driven research workflow can follow a clear sequence:
Prediction-market data can strengthen the event research layer.
Use market discovery to find relevant event contracts, historical OHLCV to study repricing, trades and quotes to add activity context, and current order books to evaluate liquidity where supported.
FinFeedAPI Prediction Markets API gives event-driven trading teams a normalized data layer for researching how prediction markets price upcoming outcomes across supported venues.
