{"enrichment":{"faq":[{"a":"Multi-factor ranks stocks by computing and standardizing multiple factors\u2014momentum, reversal, volatility, and volume\u2014then combines them into a composite score to select top performers for equal-weight portfolios. It standardizes each factor using z-score normalization across the stock universe, then weights and aggregates them to produce a single ranking that identifies the highest-quality candidates for portfolio construction.","q":"How does multi-factor build a multi-factor stock ranking system?"},{"a":"Multi-factor uses cross-sectional factor analysis to standardize and composite multiple factors across a stock universe at a single point in time. This approach normalizes each factor independently, allowing you to combine momentum, value, quality, and volatility metrics on equal footing. The cross-sectional design ensures fair comparison across all stocks regardless of their absolute values.","q":"What is cross sectional factor model methodology in multi-factor?"},{"a":"Yes. Multi-factor supports momentum, reversal, volatility, and volume factors natively, plus built-in value metrics like PE and ROE on supported markets. The ZooSignalEngine extension integrates 450+ pre-built alphas from the registry, enabling flexible composition of value, momentum, quality, and custom factors into long-only, short-only, or long-short strategies.","q":"Can multi-factor combine value, momentum, and quality factors?"},{"a":"Multi-factor standardizes each factor using z-score normalization across your stock universe, converting raw values into comparable scores centered at zero. This standardization ensures that factors with different scales\u2014like price momentum and earnings yield\u2014can be fairly weighted and combined into a composite ranking score for portfolio selection.","q":"How does multi-factor implement factor standardization for ranking?"},{"a":"Multi-factor selects top-ranked stocks using cross-sectional factor analysis and composite scoring. It supports equal-weight factor combination for straightforward multi-factor strategies, and IC-weighted factor scoring for backtesting scenarios where you want to weight factors by their information coefficient. Both methods feed into topN portfolio selection to build your final holdings.","q":"What portfolio selection methods does multi-factor support?"},{"a":"Multi-factor enables backtesting via equal-weight or IC-weighted factor scoring. Compute and standardize your chosen factors, combine them into a composite score, select your topN stocks, and rebalance at your chosen frequency. The ZooSignalEngine also lets you compose alphas from the Alpha Zoo registry into multi-factor strategies for more sophisticated signal generation and long-short portfolio testing.","q":"How can I use multi-factor for multi-factor strategy backtesting?"}],"shadow_tags":["factor-combination","cross-sectional-analysis","portfolio-construction","signal-generation","quantitative-ranking","composite-scoring","rebalancing-strategy","alpha-composition"],"summary_rewrite":"Multi-factor ranks stocks by computing and standardizing multiple factors\u2014momentum, reversal, volatility, and volume\u2014then combines them into a composite score to select top performers for equal-weight portfolios. Built-in support for value metrics like PE and ROE on supported markets. The newer ZooSignalEngine integrates 450+ pre-built alphas from the registry for flexible long-only, short-only, or long-short strategies."},"files":[{"bytes":4097,"path":"agent/src/skills/multi-factor/SKILL.md","sha256":"15de8ecafdb65dfe8bfecbdb0e3cb08278fcd5e36f0b5c6e69a2a8dc67e6e80d","url":"https://skillfed.io/files/HKUDS/Vibe-Trading/multi-factor/0c8e5d24/SKILL.md"}],"id":"HKUDS/Vibe-Trading/multi-factor","links":{"html":"https://skillfed.io/HKUDS/Vibe-Trading/multi-factor","md":"https://skillfed.io/HKUDS/Vibe-Trading/multi-factor.md","repo":"https://github.com/HKUDS/Vibe-Trading"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":4557,"language":"Python","last_updated":"2026-07-27","license":"MIT","name":"multi-factor","publisher":"HKUDS","stars":28096},"relations":{"similar":[{"id":"HKUDS/Vibe-Trading/factor-research"},{"id":"HKUDS/Vibe-Trading/alpha-zoo"},{"id":"HKUDS/Vibe-Trading/agent"},{"id":"HKUDS/Vibe-Trading/behavioral-finance"},{"id":"HKUDS/Vibe-Trading/social-media-intelligence"},{"id":"JoelLewis/finance_skills/factor-investing"},{"id":"foryourhealth111-pixel/Vibe-Skills/scientific-data-preprocessing"},{"id":"Geeksfino/finskills/findata-toolkit"},{"id":"longbridge/skills/longbridge-quant"},{"id":"HKUDS/Vibe-Trading/cross-market-strategy"}]},"slug":{"owner":"HKUDS","repo":"Vibe-Trading","skill":"multi-factor"},"version":"0c8e5d24"}