Rank stocks by standardizing momentum, reversal, volatility, and volume_ratio factors.
npx skills add https://github.com/GGwujun/SigmX --skill multi-factor-ggwujun
Please help me install this Agent Skill. Skill: multi-factor Source: https://github.com/GGwujun/SigmX/tree/main/agent/src/skills/multi-factor Command: npx skills add https://github.com/GGwujun/SigmX --skill multi-factor-ggwujun
Automates the cross-sectional ranking of many stocks by aggregating multiple factors into a single score, reducing manual tuning and enabling scalable portfolio construction.
Provide your universe of tickers and run the multi-factor ranking to generate a TopN portfolio.
High-intent search queries and answers about installing and using this skill.
Cross-sectional multi-factor stock ranking standardizes factors like momentum and volatility via Z-scores, combines them into a composite score, and selects TopN stocks for portfolio construction. This automates aggregation to reduce manual tuning.
Standardize momentum and volatility factors by applying cross-sectional Z-scores across your stock universe. These normalized values are then combined using equal or IC-based weighting to generate a composite score for ranking.
Yes, you can generate both long and short positions using this multi-factor model. It selects TopN ranked stocks for long positions and BottomN ranked stocks for potential shorts, supporting long-only or long-short strategies.
Handling missing data during composite score calculation involves built-in NaN handling and weight normalization. This ensures that missing factor values do not break the cross-sectional ranking process and maintains portfolio weight integrity.
IC-weighted scoring uses Information Coefficient weights to emphasize factors with stronger predictive power, whereas equal weighting treats all factors uniformly. Both methods generate composite scores for TopN portfolio construction.
A cross-sectional multi-factor stock ranking model calculates momentum, reversal, volatility, and volume_ratio factors. These are standardized across multiple instruments and time frames to produce composite scores.
DaddyElonMusk69Rank stocks by cross-sectional Z-score factor scores to form TopN long portfolios.
ebrahim-saniRanks stocks by composite z-score factors to produce equal-weight TopN portfolios.
wudyeCompute IC/IR statistics and quantile backtests for cross-sectional factor research.
hxhyyyStandardizes multiple financial factors and aggregates them into a composite stock-ranking signal.
