Why a Custom Python Data Task Can Fail After Running in AIStudio

Summary

This BigQuant forum post describes a custom Python module that runs successfully in AIStudio 3.0 but fails when submitted as a scheduled data task. The example reads the latest stored date, loads a local CSV of Bitfinex symbols, requests newer daily market records from an external data provider, builds a dataframe, and inserts it into a platform data source. The author asks why task execution differs from an interactive run, but supplies no error log or answer identifying the cause.

The code highlights several dependencies that can behave differently across execution environments: a local file path, external network requests, an API key, the input dataset, and data-source writes. These are useful areas to inspect when reproducing the failure in the task environment. However, the post contains no confirmed diagnosis or tested remedy, and the sample alone cannot establish which dependency failed. It teaches a debugging context rather than a trading strategy or a resolved platform procedure.

Key ideas

• The module succeeds interactively but fails when run as a data task.
• The example depends on a local CSV file, an external API, an input dataset, and a platform data source.
• The author provides no error message or confirmed explanation for the failure.
• Comparing task and interactive environments may help isolate differences in files, network access, credentials, and inputs.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.