EVENT-SECURITY MAPPING

Prediction Market Probabilities

Cross-platform prediction market events mapped to affected securities with real-time probability streams.

500+ SECURITIESJSON · CSV · ParquetReal-time (5-minute resolution)

300+

Active events

500+

Mapped securities

Platforms

5-min

Resolution

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01Download

Inspect a real sample

Representative records in the delivery format, ready to inspect before licensing the full dataset.

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.

events.jsonlrepresentative

{
  "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.

events.jsonlrepresentative

{
  "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"
}

02Schema

Record shape

Every field, its type, whether it can be null, and a representative value.

event_id

required

Canonical event id, unified across venues.

Type: string

Example pm-iran-strait-hormuz

event_title

required

The natural-language question being traded.

Type: string

Example Will Iran close the Strait of Hormuz by March 31?

source_platform

required

Venues carrying the event (Polymarket, Kalshi, Limitless, Metaculus).

Type: string[]

Example ["polymarket","kalshi"]

current_probability

required

Latest implied probability.

Type: float
Unit: 0..1

Example 0.68

probability_7d_change

required

Change in implied probability over 7 days.

Type: float

Example 0.11

volume_total

required

Cumulative traded volume.

Type: float
Unit: USD

Example 8385760

open_interest

nullable

Open interest where reported by the venue.

Type: float
Unit: USD

Example 4200000

observation_timestamp

required

Point-in-time stamp at trade-execution time. History is never restated.

Type: timestamp

Example 2026-03-03T14:30:00Z

cross_platform_spread

nullable

Probability divergence when the same event trades on more than one venue.

Type: float

Example 0.06

resolution_date

required

When the event resolves.

Type: date

Example 2026-12-31

status

required

active or resolved.

Type: string

Example active

mapped_securities

required

The 2 to 8 affected securities: {ticker, name, exposure_type, sensitivity_score, asset_class}.

Type: object[]

Example [{ticker:"CL=F", exposure_type:"positive", sensitivity_score:0.93}]

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}]

03What's included

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.

04Methodology

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.

05Evals

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.

06Graders

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.

07Application

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.

08Environment & integration

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.

09Related research

Mapping Prediction Markets to SecuritiesRead →

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