How much faster is this compared to sequential processing?

Internal benchmarks show a 3-4x speedup, reducing data fetching for 500 symbols from approximately 25 minutes down to 6-8 minutes.

Which Python libraries are required for this skill?

It utilizes Python's concurrent.futures for threading, along with standard data tools like pandas and database connectors for SQL-level filtering.

Can this be used for crypto trading?

Yes, the skill includes specialized logic for asset type detection and caching, making it highly efficient for both equity and crypto universe selection.

How does it handle existing data in the cache?

The skill implements an early-return mechanism that checks if cached data is current before initiating remote fetches, saving significant network I/O.

Does this skill help avoid API rate limits?

Yes, it includes a configurable worker cap (defaulting to 8) specifically designed to prevent triggering 429 Too Many Requests errors from financial APIs.

Parallel Universe Selection Optimizer

Name: Parallel Universe Selection Optimizer
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Accelerates quantitative trading universe selection by 3-4x through parallel processing and optimized caching strategies.

About

This skill provides a high-performance framework for filtering and analyzing financial asset universes, specifically optimized for Alpaca API workflows. It addresses common bottlenecks in I/O-bound data fetching and CPU-bound statistical analysis by implementing robust ThreadPoolExecutor patterns, smart incremental caching, and SQL-level filtering. By capping parallel workers to prevent API rate limits and eliminating redundant computations like asset type detection, it significantly reduces processing time for large candidate lists, turning a 30-minute task into a sub-8-minute operation.

Key Features

Multi-threaded data fetching using ThreadPoolExecutor with API rate limit protection
In-memory asset type detection (is_crypto) to minimize repeated string operations
Smart incremental caching to eliminate false 'No data' warnings and redundant fetches
SQL-based symbol filtering to reduce processing overhead at the database level
Parallelized statistical analysis for high-speed candidate screening
0 GitHub stars

Use Cases

Reducing research latency by accelerating the pre-analysis phase of trading algorithms
Optimizing Alpaca API data ingestion workflows to maximize throughput without 429 errors
Speeding up daily universe selection for quantitative strategies with 1000+ symbols

About

Key Features

Multi-threaded data fetching using ThreadPoolExecutor with API rate limit protection
In-memory asset type detection (is_crypto) to minimize repeated string operations
Smart incremental caching to eliminate false 'No data' warnings and redundant fetches
SQL-based symbol filtering to reduce processing overhead at the database level
Parallelized statistical analysis for high-speed candidate screening
0 GitHub stars

Use Cases

Reducing research latency by accelerating the pre-analysis phase of trading algorithms
Optimizing Alpaca API data ingestion workflows to maximize throughput without 429 errors
Speeding up daily universe selection for quantitative strategies with 1000+ symbols