How to evaluate a market data vendor

A cautious, research‑integrity‑oriented approach to evaluating market data inputs emphasizes gap audits, revision transparency, point‑in‑time handling, and verifiable samples as sensible acceptance criteria because poor data can undermine backtests and statistical interpretation.

A comparative, evidence-led framework for evaluating market data vendors can use four acceptance tests: a gap census, a transparent revision policy, point-in-time support, and a verifiable sample.

Rigorous validation inputs are needed in quantitative research because backtests can be distorted by data and modeling choices.

If you are evaluating a market data vendor for research or live-system use, your acceptance process should be designed to reduce avoidable distortions in backtests and inference. Sonar’s backtest overfitting material emphasizes that research conclusions can be made unreliable when the inputs or testing process permit hidden bias or repeated tuning against the same evidence. In practical vendor evaluation, that means the dataset itself should be treated as a testable component rather than a black box. A gap census is one way to do that: before relying on a feed, a researcher should inventory missing observations and ask whether the vendor exposes coverage clearly enough to determine where the series is incomplete.

If a vendor changes historical records after initial publication, a researcher needs to know what changed, when it changed, and whether earlier values remain recoverable for audit. Robust quantitative research depends on avoiding hidden forms of look-ahead, data‑mining, and other integrity failures in the research stack. Revision transparency is therefore relevant to research integrity.

Point-in-time support is another key criterion. The glossary entry on deflated Sharpe ratio is about evaluating whether an observed backtest result is statistically credible after accounting for multiple testing and selection effects. That framework is downstream of data construction, but it reinforces a key acceptance‑test idea: if the historical dataset available to a strategy includes information that would not actually have been known at the decision time, the resulting performance statistics become difficult to interpret. Point-in-time data support is therefore a logically important criterion when choosing a vendor.

A verifiable sample is a defensible acceptance test. Transparent research comparisons and careful methodology are emphasized. This supports the broader idea that a buyer of market data should ask for a sample that can be independently checked, reproduced, and inspected before broader adoption.

Quantitative researchers should evaluate data inputs with explicit acceptance tests because unreliable inputs can contaminate backtests, inflate apparent signal quality, and undermine statistical interpretation. Disciplined validation and skepticism toward apparently strong research outputs that may rest on flawed inputs or repeated testing are essential in a robust manner.

Drafted with AI assistance from cited sources. Reviewed and approved by Sonar Sciences Quant & Research Team.