Time Consistency
Market data is all about time. Two prices can look “different” simply because one is newer than the other.
Time consistency is the discipline of making sure your inputs are aligned. That can mean requiring data to be updated within a freshness threshold, using consistent time zones, and avoiding mixing snapshots taken at different moments.
When you build benchmarks across many venues, time consistency is what prevents you from combining a recent quote from one exchange with an old quote from another and calling it a single rate.
Why It Matters
Without time consistency, benchmarks can drift, cross rates can become unstable, and outputs can be hard to reproduce.
What does “stale data” mean for exchange rate calculations?
Stale data is any input that hasn’t been updated recently enough to reflect the current market. In fast markets, even a few minutes can be too long. Many systems therefore set a freshness cutoff and exclude inputs that don’t meet it.
How do you enforce time consistency across many exchanges?
You need clear timestamp conventions and rules for acceptable delays. Some systems rely on multiple timestamps, such as an exchange timestamp and an observation timestamp, to understand lag. Consistency also depends on stable ingestion, because gaps during volatility can break alignment.
Real-World Example
An exchange stops updating a quote during a brief outage. A benchmark calculation excludes that venue until fresh data resumes, preventing a misleading rate.
Time Consistency and Exchange Rates API
Exchange-rate benchmarks require synchronized inputs. CoinAPI’s Exchange Rates API applies freshness and filtering rules so computed rates are less affected by stale or misaligned data.