The Hard Part of Birth Chart Calculations Isn't the Zodiac. It's Time.

The most challenging aspect of creating a birth chart calculation engine isn't the zodiac signs, but rather dealing with time. The seemingly straightforward input of a local datetime is far more complex than it appears. A datetime alone doesn't specify a precise instant without a timezone. For instance, 1990-05-15 09:30 only looks precise, but without a timezone, it fails to pinpoint a specific moment. To accurately calculate the chart, the API requires both the local datetime and the IANA timezone, such as Europe/London.

Daylight-saving transitions introduce further complications. A time like 01:30 during a fall-back may represent two different UTC instants, causing ambiguity in the input. The engine should not silently choose one value, but rather make this ambiguity clear to the user. Similarly, during spring transitions, certain local times may not exist, making 02:30 invalid if the clock jumps from 01:59 to 03:00. In this case, making the user's data "unknown" is a better approach than silently "fixing" it.

However, when a birth time is unknown, some values like the Ascendant, houses, and Midheaven cannot be derived from the supplied data. Instead of filling these fields with fake data, it's more accurate to represent the incomplete information by setting them to null. This approach acknowledges that the user knows the value of some fields, while others have missing information.

The Moon's rapid movement across the sky adds another layer of complexity, as its exact sign and degree can change depending on the time of day. The engine should communicate this uncertainty clearly, as omitting the Ascendant or assuming it to be accurate would be misleading. The warning should be part of the structured output, guiding the UI to reflect the calculation's true state rather than making up its own rules.

These considerations extend beyond astrology to other fields that handle incomplete or uncertain inputs, such as geolocation, financial records, sensor data, AI data extraction, and user demographics. The temptation to fill missing data to make downstream processing easier can lead to inaccurate results. Instead, it's better to acknowledge the unknown with an explicit "unknown" value, fostering trust by demonstrating a transparent approach to handling incomplete data.