Prediction Market Data Sources: Complete Guide for Investors
Polymarket, Kalshi, Metaculus, FRED, ECB, and more - a complete map of free and paid data sources for building probability-informed investment models. Covers API access, data types, limitations, and how to aggregate signals across platforms.
Prediction market data is now accessible to any investor willing to aggregate it. Polymarket, Kalshi, and Metaculus each offer public or semi-public APIs. Combined with free macro data from FRED, the ECB, and national statistics offices, individual investors can build probability-informed models that rival institutional forecasting. This guide maps every major data source, its strengths, limitations, and how to combine them.
Prediction market platforms: data access comparison
Platform data access: features and limitations
| Platform | API access | Cost | Data type | Historical |
|---|---|---|---|---|
| Polymarket | Public REST + WebSocket | Free | Real-time prices, volumes, order books | Yes (via CLOB API) |
| Kalshi | REST API (account required) | Free tier | Market data, order management | Yes |
| Metaculus | Public REST API | Free | Community forecasts, calibration data | Yes |
| PredictIt | Limited public API | Free | Prices, volumes (US politics focus) | Partial |
| Manifold Markets | Public REST API | Free | Play-money + real-money markets | Yes |
| Good Judgment | Enterprise only | $50K+/yr | Expert panel forecasts, reports | Yes (for clients) |
The democratisation of prediction market data is one of the most significant shifts in investor information access in the past decade. Five years ago, this quality of forward-looking probability data was available only to institutional subscribers of services like Good Judgment at $50K+ annually. Today, Polymarket's public API gives anyone the same raw signal.
The gap isn't in data access - it's in interpretation. Raw prediction market prices need context: What's the volume? How does this price compare across platforms? What macro conditions are driving it? What's the calibration history for this type of question? That's the layer Futuratty adds.
Macro data: the structural foundation
Free macro data sources for investment analysis
| Source | Coverage | Series | API | Best for |
|---|---|---|---|---|
| FRED | US + global | 800,000+ | Free API key | Rates, inflation, employment |
| ECB SDW | Eurozone | 50,000+ | Free | Monetary policy, financial data |
| Eurostat | EU member states | 100,000+ | Free | Economic statistics, trade |
| ONS | United Kingdom | 30,000+ | Free | UK GDP, inflation, property |
| World Bank | Global (190+ countries) | 16,000+ | Free | Development, structural |
| CoinGecko | Crypto markets | 10,000+ coins | Free tier | Crypto prices, volumes, market cap |
Prediction market prices exist in a vacuum without macro context. A 45% probability of an ECB rate cut means more when you know eurozone core inflation is at 2.3%, services inflation at 3.4%, and Germany contracted in Q4 2025. The macro data provides the "why" behind the prediction market "what."
FRED alone provides over 800,000 time series - more data than most Bloomberg Terminal users access. The challenge isn't availability; it's knowing which series matter for which investment decisions, and how to weight them against forward- looking prediction market signals.
How Futuratty aggregates sources
If: Multiple platforms show converging probabilities (within 5pp)
Then: High-confidence signal: treat the liquidity-weighted average as a reliable base rate for scenario planning
If: Platforms diverge significantly (10pp+ spread between sources)
Then: Investigate the divergence: platform-specific factors (liquidity, participant base, market structure) may explain the gap; this often reveals an opportunity for deeper analysis
If: Prediction markets and macro data conflict (market prices bullish, structural data bearish)
Then: Weight structural analysis more heavily for horizons beyond 6 months; prediction markets are better at near-term probability but weaker on structural regime changes
Building your own data stack
DIY vs managed intelligence: cost-benefit comparison
| Approach | Setup cost | Ongoing cost | Time requirement | Best for |
|---|---|---|---|---|
| Manual monitoring | $0 | $0 | 2-4 hrs/week | Individual investors tracking 3-5 events |
| Custom data pipeline | $5-15K | $200-500/mo | 40+ hrs setup, 5 hrs/week | Tech-savvy family offices |
| Bloomberg Terminal | $0 | $24K/yr | Existing workflow | Institutions already subscribed |
| Futuratty Intelligence | $0 | Free (public) / bespoke | 30 min/week reading | Investors who want signal, not infrastructure |
Data sources
- Polymarket - API documentation and platform data, March 2026
- Kalshi - Developer API documentation, March 2026
- Metaculus - Public API and calibration data, March 2026
- FRED (Federal Reserve Bank of St. Louis) - API documentation, March 2026
- ECB Statistical Data Warehouse - API and data catalogue, March 2026
- Eurostat - Data browser and API documentation, March 2026
- Office for National Statistics - Developer hub, March 2026
- CoinGecko - Public API documentation, March 2026
- Futuratty data methodology documentation, March 2026
Frequently asked questions
What are the best prediction market data sources in 2026?
The three primary sources are Polymarket (crypto-based, strongest for political and macro events, public API), Kalshi (CFTC-regulated, integrated with Robinhood, API with account requirement), and Metaculus (reputation-based community forecasting, public API, strongest for scientific and long-range questions). Secondary sources include PredictIt (declining but still active for US politics), Manifold Markets (play-money with real-money features), and Good Judgment (enterprise-grade, $50K+).
Is prediction market data free to access?
Most major platforms offer free data access. Polymarket's API is publicly accessible without authentication. Kalshi requires a free account for API access. Metaculus provides public data and a developer API. FRED (Federal Reserve Economic Data) and ECB Statistical Data Warehouse are free and complement prediction market data with macro indicators. The main cost is in aggregation, cleaning, and interpretation - which is what services like Futuratty provide.
How do you aggregate data from multiple prediction markets?
Futuratty weights platform data by liquidity, track record, and question specificity. For events covered by multiple platforms (e.g., ECB rate decisions), we calculate a liquidity-weighted average. If Polymarket has $5M volume showing 73% and Kalshi has $2M showing 68%, the weighted estimate is approximately 71.5%. We also cross-reference with Metaculus community forecasts and structural macro models to produce our final probability estimate.
What macro data sources complement prediction markets?
FRED (Federal Reserve Economic Data) provides 800,000+ time series covering interest rates, inflation, employment, and GDP for the US. The ECB Statistical Data Warehouse covers eurozone monetary and financial data. Eurostat provides EU economic statistics. The ONS covers UK data. The World Bank provides global development indicators. CoinGecko and CoinMarketCap cover crypto markets. Together, these provide the structural backdrop that contextualises prediction market signals.
How often should prediction market data be updated?
For active investment decisions, daily monitoring of high-volume contracts is appropriate. Weekly aggregation is sufficient for scenario planning and portfolio review. Futuratty updates its forecasts when prediction market prices move more than 5 percentage points or when significant new data releases (central bank decisions, economic data, policy announcements) change the underlying conditions.
Can I build my own prediction market data pipeline?
Yes. Polymarket's API (REST and WebSocket) provides real-time pricing data. Kalshi offers a REST API for market data and order management. Metaculus has a public API for question data and community forecasts. FRED and ECB APIs are well-documented and free. The engineering challenge is in normalisation (different platforms structure data differently), resolution matching (aligning similar questions across platforms), and maintaining historical data for calibration analysis.
What is the Futuratty data methodology?
Futuratty combines prediction market signals with macro-economic data and structural analysis. We weight sources by: (1) liquidity and volume of prediction market contracts, (2) calibration track record of the platform or community, (3) recency of the data, and (4) structural analysis from fundamental macro models. Our confidence rating reflects the quality and convergence of these inputs. Full methodology is published on our methodology page.
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