Prediction Market APIs: A Read-Only Research Workflow
Build a research dataset you can explain: preserve contract rules, distinguish prices, track timestamps, and make missing data visible.
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Build on better data.
A read-only prediction market API workflow for collecting useful observations without losing the context that makes them meaningful.
Begin with one bounded research question
Decide what the dataset needs to explain before collecting it. A daily research brief, a historical price study, and an order-book analysis need different observations. Write the question, required fields, collection interval, and acceptable age of the data in plain language.
Keep collection separate from execution. A research tool that only reads public information has a different responsibility from software that can access an account or place an order. Do not grant a capability just because an integration supports it.
Preserve the identity of the observation
Polymarket’s market-data overview describes an event-to-market-to-outcome workflow. Its outcome token identifier is relevant to reading that outcome’s prices. Other platforms expose different structures and units. Preserve the provider’s identifiers and add your own normalization only when its meaning is documented.
| Field | Why it matters |
|---|---|
| Provider and product | Identifies which documentation and terms apply. |
| Event, market, outcome | Connects the observation to the exact question. |
| Collected time and source time | Distinguishes when data was observed from when it changed. |
| Price type and unit | Separates a bid, ask, midpoint, or trade and prevents unit confusion. |
| Size and depth | Adds context to the amount available at a price. |
| Rule snapshot and status | Preserves the event definition and stage of its lifecycle. |
Make failures visible
Define how the pipeline handles missing pages, exhausted pagination, throttling, stale data, and changed response fields. A successful HTTP response does not prove that the collected result is complete. Preserve the raw response where appropriate and record why a normalized record was rejected.
Never silently turn a missing price into zero. Never merge two similarly named markets without checking their definitions. Those choices can create a convincing but invalid dataset.
Hand a model evidence it can cite
If an LLM consumes the dataset, pass the relevant timestamps and rule text with the values. Give it a bounded question and require it to identify missing information. Use the AI research-brief workflow to separate source extraction from editorial interpretation.
Build the checklist before the integration
The complete prediction market API guide develops a collection and review process. It intentionally leaves endpoint versions and authentication details to the current provider documentation. This page is a research guide; PredictionPodcast.com does not offer an API key, a hosted data feed, or a trading connection.
Primary reference: Polymarket: Market Data Overview. Check the current documentation for product-specific details.
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