EVENT-SECURITY MAPPING
Prediction Market Intelligence
Cross-platform prediction market events mapped to affected securities with real-time probability streams.
See the data
Representative records in the exact shape we deliver. Real provenance and full slices are shared under license.
Geopolitical event to the energy complex (single venue)
Representative shape, modeled on the real record. Six mapped securities with signed exposure and sensitivity 0 to 1.
{
"event_id": "pm-iran-strait-hormuz",
"event_title": "Will Iran close the Strait of Hormuz by March 31?",
"category": "geopolitical",
"source_platform": ["polymarket"],
"current_probability": 0.68,
"probability_7d_change": 0.11,
"volume_total": 8385760,
"observation_timestamp": "2026-03-03T14:30:00Z",
"mapped_securities": [
{ "ticker": "CL=F", "exposure_type": "positive", "sensitivity_score": 0.93, "asset_class": "commodity" },
{ "ticker": "XOM", "exposure_type": "positive", "sensitivity_score": 0.82, "asset_class": "equity" },
{ "ticker": "SPY", "exposure_type": "negative", "sensitivity_score": 0.65, "asset_class": "etf" }
],
"status": "active"
}Monetary-policy event, cross-platform spread populated
Representative. Tracked on two venues, so cross_platform_spread is set.
{
"event_id": "pm-fed-march-2026-no-change",
"category": "monetary_policy",
"source_platform": ["polymarket", "kalshi"],
"current_probability": 0.97,
"cross_platform_spread": 0.01,
"observation_timestamp": "2026-03-03T14:30:00Z",
"mapped_securities": [
{ "ticker": "TLT", "exposure_type": "negative", "sensitivity_score": 0.88, "asset_class": "etf" },
{ "ticker": "XLF", "exposure_type": "positive", "sensitivity_score": 0.72, "asset_class": "etf" }
],
"status": "active"
}Record shape
Every field, its type, whether it can be null, and a representative value.
| Field | Type | Constraint | Description |
|---|---|---|---|
| event_id | string | required | Canonical event id, unified across venues. e.g. pm-iran-strait-hormuz |
| event_title | string | required | The natural-language question being traded. e.g. Will Iran close the Strait of Hormuz by March 31? |
| category | string | required | Taxonomy: geopolitical, monetary_policy, economic, corporate, election, regulatory, m_and_a, trade_policy. e.g. geopolitical |
| source_platform | string[] | required | Venues carrying the event (Polymarket, Kalshi, Limitless, Metaculus). e.g. ["polymarket","kalshi"] |
| current_probability | float · 0..1 | required | Latest implied probability. e.g. 0.68 |
| probability_7d_change | float | required | Change in implied probability over 7 days. e.g. 0.11 |
| volume_total | float · USD | required | Cumulative traded volume. e.g. 8385760 |
| open_interest | float · USD | nullable | Open interest where reported by the venue. e.g. 4200000 |
| observation_timestamp | timestamp | required | Point-in-time stamp at trade-execution time. History is never restated. e.g. 2026-03-03T14:30:00Z |
| cross_platform_spread | float | nullable | Probability divergence when the same event trades on more than one venue. e.g. 0.06 |
| resolution_date | date | required | When the event resolves. e.g. 2026-12-31 |
| status | string | required | active or resolved. e.g. active |
| mapped_securities | object[] | required | The 2 to 8 affected securities: {ticker, name, exposure_type, sensitivity_score, asset_class}. e.g. [{ticker:"CL=F", exposure_type:"positive", sensitivity_score:0.93}] |
Event Probability Stream
Per-event probability time series with 24h/7d/30d changes, volume, open interest, and cross-platform normalization.
Security Sensitivity Scoring
Event-to-security mapping with sensitivity scores validated against historical price reactions.
Cross-Platform Normalization
Unified schema across Polymarket, Kalshi, Limitless, and Metaculus. Compare identical events across venues.
How it is built
- 01
Real-time ingestion
Automated WebSocket and API ingestion from four platforms - one CFTC-regulated exchange, one ICE-backed decentralized exchange, one DeFi market, and one calibrated forecasting aggregator - at 5-minute resolution. No web scraping.
- 02
Cross-platform normalization
Native identifiers (Polymarket condition_id, Kalshi ticker, Limitless market id, Metaculus question_id) resolve to one canonical event, so the same question on multiple venues collapses to one event with multiple probability sources.
- 03
Event categorization
Events are tagged with a standardized taxonomy across geopolitical, monetary policy, regulatory, M&A, election, economic, and corporate categories.
- 04
Event-to-security mapping
Proprietary rules link each event to 2 to 8 public securities with a signed exposure type - trade policy to country and sector ETFs, monetary policy to rates and banks, M&A to target, acquirer, and peers.
- 05
Sensitivity scoring
Each event-security link carries a sensitivity score derived from historical co-movement between probability changes and security price changes.
- 06
Point-in-time stamping
Every observation is stamped at trade-execution time (on-chain settlement or exchange-reported execution). Historical data is immutable, so a strategy at time T sees only observations at or before T.
How we validate
What each evaluation measures and how it is run. Where no benchmark is published, we show the methodology and say so.
Lead/lag on resolved events
Measures
Whether mapped securities subsequently move in the predicted direction after a probability move.
Method
Tested across 500+ historical probability moves greater than 10% in events that have since resolved, spanning a US election cycle, tariff announcements, and Fed decisions; resolved events retained, so no survivorship bias.
Result
Qualitative, reported honestly: probability changes tend to lead the mapped security moves by hours to days. The research declines to publish a single hit-rate because it varies by category, liquidity, and mapping cleanliness.
Per-category sensitivity validation
Measures
Whether mapped securities actually move when an event probability changes, by category.
Method
Backtest realized price moves against probability moves with directional correctness, magnitude vs sensitivity score, and lead time, reported per event category.
Result
Methodology-stage. Per-category metrics are computed on request; no fixed published figure is asserted.
Ground truth
What correct means for this data, and how it is established.
Ground truth
The realized security price reaction following a probability move, on events that have resolved. Mappings are validated by asking whether the mapped securities actually moved when the event probability changed.
How it is established
For each mapped event-security pair, measure directional correctness on moves greater than 10%, magnitude vs the sensitivity score, and lead time. Cross-platform event matching is verified manually for the top events by volume; probability series are checked for gaps, outliers, and stale prices.
Agreement
No inter-rater figure is published; top events are matched under manual QA rather than a rater panel.
Event-Driven Trading
Use prediction market probabilities as a real-time gauge of political and economic event likelihood. Map probability shifts directly to affected securities.
Macro Hedging
Track probability of tariff changes, rate decisions, and regulatory actions. Pre-position before consensus shifts using crowd-sourced probability.
Cross-Market Arbitrage
Identify divergences between prediction market implied probabilities and options-implied probabilities on the same underlying events.
How you load it
Delivery
S3, REST API, WebSocket, Email
Formats
JSON, CSV, Parquet
Auth
A derived analytical product. No raw prediction-market data is redistributed. Public-API sourced; no MNPI or PII. Identifiers are also mapped to CUSIP and ISIN.
Cadence
Real-time at 5-minute resolution (processing lag under one second) or daily batch. Full history is roughly 15 GB.
Request access.
Restricted-scope evaluation access for qualified teams. We share real samples, full schema, and provenance under a mutual NDA.