Time zones in data pipelines: the silent source of shifted signals

[Amanhasnoname]

Data from different sources arrives in different time zones: UTC, broker server time, New York time, local time. Combining them without careful conversion shifts signals by hours.

A subtle problem appears with daylight saving. Server time that follows New York changes twice a year, and a naive conversion creates duplicate or missing hours. Timestamps that look sequential may jump back one hour in autumn.

For scalpers testing session-based strategies, an hour of error moves the supposed London open into the middle of the Asian session. The results are meaningless.

The cure is standardizing on UTC for storage and analysis. Convert source timestamps to UTC at the point of ingestion, using proper time zone libraries that account for daylight saving rules. Store the original offset as metadata.

Validate by checking known events. Data releases at fixed times, such as US jobs at 13:30 UTC or 12:30 UTC depending on the season, should show volatility spikes at the expected timestamps.

If the spikes appear at the wrong time, the conversion is wrong.

How are the time zones handled, and has a data release been used to verify the alignment?