PyPortTickerSelector: A top tickers selection library using multiple indicators, performance metrics, strategies with benchmark

This paper presents PyPortTickerSelector, an automated ticker selection library designed to identify top-performing tickers based on predefined and user-defined strategies. The library supports various methods to calculate multiple indicators and performance-metrics. Users have the flexibility to customize the ticker selection process at every step, using built-in options or their own methods. The library achieves improved computational efficiency over manual analysis while maintaining approx 90 % test coverage for business logic. Validation includes comparison against benchmark performance, latency profiling, memory usage optimization, and statistical significance testing, addressing critical gaps in quantitative finance tooling. The library allows seamless integration with the PyPortOptimization Pipeline for portfolio construction.