Compute IC/IR metrics and quantile backtests for cross-sectional stock factors.
npx skills add https://github.com/GGwujun/SigmX --skill factor-research-ggwujun
Please help me install this Agent Skill. Skill: factor-research Source: https://github.com/GGwujun/SigmX/tree/main/agent/src/skills/factor-research Command: npx skills add https://github.com/GGwujun/SigmX --skill factor-research-ggwujun
Cross-sectional factor research often suffers from inconsistent methodologies and manual, error-prone analyses. This Skill provides a repeatable framework to evaluate the predictive power of stock factors using IC/IR statistics and quantile backtesting across multiple instruments.
Provide a factor CSV and a returns CSV, then run the factor_analysis workflow to evaluate factor validity and backtest performance.
High-intent search queries and answers about installing and using this skill.
Cross-sectional factor evaluation computes IC means, IR, and stability over time to assess predictive power. This Skill provides a repeatable framework to evaluate factor validity using IC/IR statistics and quantile backtesting across multiple instruments.
Quantile backtesting for stock selection factors performs group-by backtests to reveal discriminatory power and long-short potential. This Skill generates standardized outputs like group_equity.csv to evaluate backtest performance across multiple instruments.
Factor screening and combination identifies effective factors to build composite signals for multi-factor strategies. This Skill applies IC/IR analytics and quantile backtests to compare and combine cross-sectional factors for portfolio construction.
Running cross-sectional factor analysis requires factor_csv, return_csv, and output_dir inputs. You provide a factor CSV and a returns CSV, then execute the factor_analysis workflow to evaluate factor validity and backtest performance.
Yes, IC/IR analysis for multiple factors across different instruments is fully supported. The Skill applies standardized IC/IR metrics and quantile backtests across multiple instruments, outputting ic_series.csv and ic_summary.json for downstream comparison.
Inconsistent cross-sectional factor research results often stem from manual, error-prone analyses. This Skill provides a repeatable framework that standardizes IC/IR statistics and quantile backtesting to ensure consistent factor validity evaluation.
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