Can I use this for non-trading Python projects?

Absolutely. While the context is trading universe selection, the patterns for implementing progress closures and notebook-safe reporting are applicable to any long-running Python data task.

Why does my universe selection appear to hang for 15+ minutes?

This usually happens during sequential loops like hard filtering or scoring which don't provide output by default. This skill implements callbacks to make these phases visible in real-time.

Is this skill compatible with Jupyter and Google Colab?

Yes, it specifically uses sys.stdout.flush() and carriage returns to ensure that progress updates appear immediately in notebook cells without flooding the output history.

How does this improve parallel processing visibility?

While many parallel tasks have internal logging, this skill ensures those callbacks are correctly exposed to the top-level runner, providing a unified view of all concurrent operations.

Selection Progress Reporting

Name: Selection Progress Reporting
Author: smith6jt-cop

bysmith6jt-cop

Analytics & Monitoring

Provides real-time visibility into silent universe selection phases by implementing standardized progress callbacks and notebook-friendly output.

About

This skill addresses the 'silent hang' problem in algorithmic trading workflows by adding comprehensive progress reporting to universe selection, filtering, and scoring phases. It implements a standardized callback mechanism that tracks progress across both parallel data fetching and sequential CPU-bound loops. By utilizing carriage returns and explicit output flushing, it ensures that developers receive immediate feedback in terminal environments and Jupyter notebooks, transforming opaque, multi-minute processes into transparent, monitorable tasks.

Key Features

End-to-end progress tracking for multi-phase universe selection
Jupyter-compatible output flushing to prevent terminal buffering
Visibility into previously silent hard filter and scoring phases
Standardized progress callback interface for library-wide consistency
0 GitHub stars
In-place CLI updates using carriage returns for clean output

Use Cases

Providing user feedback for data-heavy algorithmic trading initialization
Monitoring long-running market data filtering tasks in interactive environments
Debugging perceived process hangs during large-scale universe scoring

About

Key Features

End-to-end progress tracking for multi-phase universe selection
Jupyter-compatible output flushing to prevent terminal buffering
Visibility into previously silent hard filter and scoring phases
Standardized progress callback interface for library-wide consistency
0 GitHub stars
In-place CLI updates using carriage returns for clean output

Use Cases

Providing user feedback for data-heavy algorithmic trading initialization
Monitoring long-running market data filtering tasks in interactive environments
Debugging perceived process hangs during large-scale universe scoring