evaluate-cross-sectional

Cross-sectional factor evaluation computes standardized metrics like IC, Rank IC, ICIR, multi-horizon decay, and turnover to assess predictive power and signal stability for stock selection. This skill automates that workflow while preventing forward-return leakage.

Calculating IC and ICIR for multiple assets on the same date requires a cross-sectional evaluation workflow that enforces data alignment, ensures sufficient symbol counts, and prevents forward-return leakage to guarantee reproducible asset ranking results.

Manual cross-sectional factor calculation produces inconsistent results due to error-prone data alignment and forward-return leakage. Standardized evaluation workflows solve this by enforcing built-in best practices, review checklists, and explicit red lines for trading cost impacts.

Yes, this cross-sectional factor evaluation handles trading cost impact from high turnover by including it as an explicit red line in its review checklist. This ensures turnover metrics are properly contextualized before factors are added to live trading strategies.

Yes, you can assess multi-horizon decay behavior for a momentum factor across 500 stocks. This skill computes multi-horizon decay alongside IC, ICIR, and turnover to evaluate predictive power and signal stability for quantitative research workflows.

You should not use a cross-sectional factor evaluation workflow when ranking assets across different dates or when lacking sufficient symbol counts. It is specifically designed for factors ranking assets on the same date with strict data alignment requirements.