How significantly can this reduce processing time?

By reducing a candidate pool from roughly 4,000 to 1,300 symbols, this skill can cut historical data download and processing times by approximately 70%.

Does this skill work with the Alpaca API?

Since Alpaca does not natively provide sector data, this skill specifically integrates yfinance to fetch and cache sector metadata into your local symbol database for use in filtering.

What are the database requirements?

The filtering logic requires SQLite version 3.25 or higher to support advanced window functions like ROW_NUMBER() and PARTITION BY.

Why use sector filtering instead of just picking the top volume symbols?

Selecting purely by volume often leads to over-representation in high-activity sectors like Tech or Finance. Sector filtering ensures your strategy evaluates opportunities across the entire market, preventing sector-specific risk.

Per-Sector Trading Candidate Filter

Name: Per-Sector Trading Candidate Filter
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Optimizes trading universe selection by applying sector-specific volume filters to ensure a diverse and manageable pool of candidates.

About

This skill provides a robust framework for reducing massive stock universes into curated, sector-balanced candidate lists for algorithmic trading. By integrating yfinance sector data into a local SQLite database, it employs advanced SQL window functions to filter symbols based on sector-specific volume rankings. It solves the common 'data bottleneck' problem where downloading historical data for thousands of symbols is time-prohibitive, allowing developers to reduce candidate pools by up to 70% while maintaining proportional representation across all 11 standard GICS sectors.

Key Features

Dynamic filtering logic with configurable min/max sector bounds
0 GitHub stars
Automated yfinance sector metadata integration and local caching
Advanced SQL window functions for proportional sector filtering
Cross-sector diversity preservation to prevent tech/finance over-concentration
Automated database schema migrations for sector data storage

Use Cases

Reducing historical data ingestion time for large-scale trading strategies
Optimizing API usage by filtering out low-probability candidates before analysis
Maintaining sector-neutral portfolios during automated universe selection

About

Key Features

Dynamic filtering logic with configurable min/max sector bounds
0 GitHub stars
Automated yfinance sector metadata integration and local caching
Advanced SQL window functions for proportional sector filtering
Cross-sector diversity preservation to prevent tech/finance over-concentration
Automated database schema migrations for sector data storage

Use Cases

Reducing historical data ingestion time for large-scale trading strategies
Optimizing API usage by filtering out low-probability candidates before analysis
Maintaining sector-neutral portfolios during automated universe selection