Prediction market probabilities,
structured for institutional workflows
Cross-venue probability distributions for FOMC, CPI, and payrolls outcomes across Kalshi and Polymarket. Normalized, historical, and delivered as Parquet, API, or real-time stream.
Federal Reserve Research · FEDS 2026-010
“Kalshi provides a statistically significant improvement over the Bloomberg consensus forecast”
on headline CPI
“A perfect forecast record on the day before the FOMC meeting, which represents a statistically significant improvement over the fed funds futures forecast”
Diercks (Federal Reserve Board), Katz (Northwestern), Wright (Johns Hopkins & NBER). February 2026. Read the paper
The data is informative. The problem is getting it into a research workflow. Two venues, incompatible schemas, no unified history, no cross-venue mapping. We solve that.
The data is fragmented across venues
The same FOMC outcome has different IDs, different labels, and different price formats on Kalshi versus Polymarket. “No change” on one venue is “Hold” on the other is “425-450 bps” on a third contract. Tickers rotate per meeting. Token IDs change per event.
To get a unified probability distribution for a single FOMC meeting, you need a verified mapping between every venue-specific identifier for every outcome. And you need to have been maintaining it from the start. There is no backfill API. The historical record only exists if someone was capturing and curating it in real time.
Single-venue feeds exist. Nobody maintains the cross-venue mapping. That's what we do.
What you get
Raw, per-venue probability data with full provenance back to the orderbook. We don't publish opinionated combined probabilities. You get the cleanest representation of what each venue is saying and decide what it means.
Cross-venue probability distributions
The full distribution for every FOMC, CPI, and payrolls event. Every outcome, both venues, updated on every tick. Per-venue bid/ask with depth in USD so you know the liquidity behind each price. Queryable as a historical time series.
For CPI and payrolls, this is the only continuously-updated full distribution available. Traditional markets don't produce a tradable equivalent to “CPI print distribution” — prediction markets are the only source.
Full market microstructure
Every orderbook delta, every trade execution, with millisecond timestamps. Not scraped headlines or hourly snapshots. The raw tick-level book, normalized into a single schema across both venues. See the liquidity behind every probability and know whether a price is real or a thin-book artifact.
Historical archive
Tick-level data for every tracked market since we started subscribing. Hive-partitioned Parquet, queryable with DuckDB, Polars, or Spark. Neither exchange provides historical backfill — this data only exists if someone was capturing it in real time.
Verified event mapping
The canonical registry linking Kalshi tickers to Polymarket condition_ids for the same real-world outcome. Versioned, auditable, maintained continuously as contract specs change. Without this, you can't reconstruct which Polymarket token corresponded to which FOMC outcome six months ago. No single-venue feed provides it.
Sample orderbook data
Loading orderbook data (Mar 17, 6am-10am PST)...
How desks use this
Rates / Macro
- A second opinion on FOMC, CPI, and payrolls from a structurally different participant base than fed funds futures. A complement to FedWatch, not a replacement.
- Run pre-release consensus checks against prediction market distributions. Backtest how distributions behaved before past rate decisions.
- For CPI and payrolls specifically, prediction markets offer something traditional instruments don't: a continuously-updated full distribution over outcomes.
Energy / Commodities
WTI price level distributions, gas price probabilities, sanctions and supply-side event markets. Structured as a covariate for commodity models. Track how policy event probabilities move alongside physical and futures data. Parquet delivery fits directly into existing research pipelines.
Political risk / Event-driven
Continuous probability time series for elections, legislation, sanctions, and geopolitical events. Real-money market prices, not polls. Full orderbook depth so you can see when thin liquidity is distorting headline odds. Cross-venue normalized so you can compare the same event across Kalshi and Polymarket in one query.
Technical specifications
Coverage
Every market and event across Kalshi and Polymarket, excluding parlays.
Data types
Orderbook snapshots, orderbook deltas (every price-level change, signed quantity), trade executions (price, quantity, taker side).
Normalization
Unified market_id, yes/no sides, decimal dollar prices, millisecond timestamps.
Storage format
Hive-partitioned Parquet. No proprietary format.
Historical archive
Compounding daily. No exchange backfill exists.
Real-time
Kafka-compatible feed. Normalized topic keyed by market_id.
REST + WebSocket
Real-time and historical queries. Dedicated API keys.
S3 Parquet
Daily Hive-partitioned files to your bucket.
Kafka stream
Kafka-compatible real-time feed.
Custom
SFTP, Snowflake, or other formats.
Book a call
See sample prediction market data and how it fits into your research workflow.
Prefer email? Reach us at founders@oddpool.com