Does this skill affect cryptocurrency symbols?

No, crypto symbols typically use a slash (e.g., BTC/USD) rather than a dot, so they are not affected by the 'dotted-symbol' exclusion rule.

Is the filtering applied at the database level?

Yes, this skill provides SQL patterns to filter symbols directly during database queries, ensuring that bad data is caught before it ever reaches your application code.

Why does this skill exclude all symbols with a dot?

Symbols with dots usually represent warrants (.WS), preferred shares (.PR), or units (.U), which are often unsupported by the yfinance sector lookup API and cause runtime errors.

How much faster is the sector update process with this skill?

By implementing parallel fetching with ThreadPoolExecutor, the sector update process can be up to 10 times faster, reducing a 10-minute task to approximately 60 seconds.

Dotted Symbol Exclusion for Trading Data

Name: Dotted Symbol Exclusion for Trading Data
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Optimizes financial data pipelines by automatically filtering non-standard ticker symbols that cause API errors during sector lookups.

About

The Dotted Symbol Exclusion skill is designed to prevent failures in trading workflows when interacting with the yfinance API. It addresses common errors caused by special security types—such as warrants, units, preferred shares, and specific class shares (e.g., BRK.A)—which often lack standardized sector data. By implementing a unified 'exclude all dots' rule across both SQL queries and Python filtering logic, this skill ensures data consistency between your database and training notebooks while significantly improving performance through parallelized API fetching.

Key Features

Automatic identification and exclusion of warrants, units, and preferred shares
Prevention of silent API failures and empty sector assignments in financial datasets
Unified SQL and Python filtering to ensure cross-environment data consistency
Parallelized sector fetching using ThreadPoolExecutor for 10x faster updates
0 GitHub stars
Standardized regex-free logic using simple dot-notation detection

Use Cases

Reducing API overhead by pre-filtering unsupported symbols
Cleaning financial datasets before training machine learning models
Optimizing bulk sector updates for Alpaca-based trading systems

About

Key Features

Automatic identification and exclusion of warrants, units, and preferred shares
Prevention of silent API failures and empty sector assignments in financial datasets
Unified SQL and Python filtering to ensure cross-environment data consistency
Parallelized sector fetching using ThreadPoolExecutor for 10x faster updates
0 GitHub stars
Standardized regex-free logic using simple dot-notation detection

Use Cases

Reducing API overhead by pre-filtering unsupported symbols
Cleaning financial datasets before training machine learning models
Optimizing bulk sector updates for Alpaca-based trading systems