How to Evaluate a Market Data Provider

A Practical Checklist for Exchanges and Trading Venues

Choosing a market data provider isn’t a branding decision. It’s a risk decision.

For exchanges and trading venues, data quality directly impacts liquidations, margin safety, funding stability, regulatory exposure, and user trust. This checklist reflects how institutional buyers actually evaluate providers in practice.

1. Data Provenance

Start with the simplest question: who sets the price?

Buyers look for:

• First-party market participants as sources

• Disclosed publisher identities

• Transparent aggregation logic

Opaque sourcing makes manipulation risk impossible to assess.

2. Performance Under Stress

Average latency numbers don’t matter if feeds fail during volatility.

Institutional buyers ask for evidence of:

• Update frequency during market stress

• Latency under load

• Behavior during liquidity fragmentation

If it hasn’t been measured, it hasn’t been engineered.

3. Accuracy vs Benchmarks

Accuracy must be quantified, not claimed. Serious providers show:

• Performance against NBBO or futures curves

• Median deviation, not just averages

• Results outside regular trading hours

If accuracy isn’t measured, it’s marketing.

4. Confidence Intervals and Outlier Handling

Binary prices hide uncertainty, which is dangerous for liquidation and margin systems.

Institutional-grade feeds expose:

• Confidence intervals

• Explicit uncertainty during thin liquidity

• Automated outlier suppression

This is a strong signal of whether a provider understands real-world usage.

5. Operational and Legal Reality

Many deals fail on details discovered late:

• Corporate actions and symbol changes

• Trading halts and settlement assumptions

• Licensing terms that prohibit redistribution or derivatives usage

These constraints surface eventually. The only question is when.

How Pyth Pro Maps to Institutional Criteria

Pyth prices fall within NBBO 94-96% of the time for US equities, with median deviation of 1.3-1.5 basis points when outside.

Pyth Pro was built for exchanges, perpetual venues, and synthetic markets operating at scale. It combines:

• First-party institutional sourcing

• Measurable accuracy

• Explicit confidence intervals

• 24/7 and overnight coverage

• Clear redistribution rights

• One integration across asset classes

That alignment is intentional. It reflects how real venues evaluate risk — not how vendors pitch features.

Pyth Pro delivers institutional-grade price data across equities, crypto, FX, commodities, and fixed income. One integration, all asset classes.

See how Pyth Pro performs against your current feeds and book a demo here.