Outliers
When you collect prices from many exchanges, you usually expect them to be in the same general range. But sometimes one number sticks out.
That number could be wrong, or it could be real but misleading. Thin order books, sudden spread widening, or exchange-specific issues can make a venue look far away from the rest.
Because outliers can distort averages and benchmarks, many market data systems try to detect them and either filter them out or reduce their influence.
Why It Matters
If outliers are not handled, a single bad data point can skew a benchmark price and create incorrect conversions across many assets.
Are outliers always “bad data”?
Not always. During fast market moves, an early-moving venue can look like an outlier for a short period. That’s why robust systems combine outlier checks with other signals like liquidity, spreads, and data freshness.
What causes outliers in crypto market data?
Common causes include low liquidity, sudden volatility, stale quotes, and inconsistent market types (like mixing spot and derivatives). Data pipeline issues can also create outliers through duplication, timestamp errors, or missing updates. Good quality controls are designed to catch these before they affect downstream users.
Real-World Example
Most exchanges show ETH/USD around the same level, but one venue is far off after a sudden liquidity drop. The pricing system flags that venue’s value as an outlier.
Outliers and Exchange Rates API
Exchange-rate benchmarks rely on combining many sources without letting any single source dominate incorrectly. CoinAPI’s Exchange Rates API applies filtering logic so outlier venues are less likely to distort the published rates.