Consolidate multiple sources
Normalize prediction-market observations into a consistent research format so events can be compared across venues.
THE DATA PROBLEM
A displayed probability is not automatically a reliable signal.
A stale quote can look like new information. An anomalous tick can resemble a material probability move. Similar event contracts can use different wording or resolution criteria.
Without consolidation and quality controls, these differences can contaminate research features and make backtests difficult to reproduce. Our data-pipeline framework addresses this before the modelling stage.
OUR DATA-QUALITY OBJECTIVE
Consolidated, toxic-tick-filtered probability data for financial markets.
“Toxic” here refers to observations that can distort the research signal, including stale prices, anomalous ticks and anomalous observations.
Filtering aims to reduce contamination. It does not make market probabilities certain or eliminate every data error.
FOUR DATA-QUALITY PRIORITIES
Consolidate. Reconcile. Filter. Preserve history.
The framework is developed; production-grade datasets and delivery services remain under development.
Reconcile equivalent events
Identify contracts that describe the same underlying event while accounting for differences in wording and resolution criteria.
Filter toxic ticks and stale prices
Apply data-quality checks to flag or exclude stale observations, anomalous ticks and anomalous observations that could distort a probability-change signal.
Preserve point-in-time context
Keep timestamps and historical context aligned with what was available at the time, supporting reproducible research and reducing look-ahead bias.
WHY THIS MATTERS FOR FINANCIAL MODELS
Research changes in probability, with usable context.
Our focus is how an event’s probability changes over time and which financial exposures that change may reach. A consistent event definition, reliable observation history and point-in-time alignment make that research more meaningful.
RiskGraph Institutional Data is designed to support quantitative feature research and historical backtesting. Its intended outputs are extracted information signals rather than raw venue feed resale. API and streaming delivery are planned; signal coverage, quality requirements and delivery formats can be discussed with institutional research teams.
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