multi-factor-ranking
CommunityRank stocks by multi-factor signals.
Finance & Accounting#backtesting#multi-factor#portfolio construction#long-short#stock ranking#alpha zoo#zscore standardization
Authorloanntc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
It helps you select a top-performing basket of stocks by combining several style factors into one cross-sectional ranking signal, reducing reliance on any single metric.
Core Features & Use Cases
- Multi-factor scoring: Computes momentum, reversal, volatility, and volume ratio per instrument and combines them into a composite score.
- Cross-sectional standardization: Applies Z-score normalization across the universe at each date so different factor scales become comparable.
- TopN portfolio construction: Ranks by composite score and assigns equal weights to the top N names while keeping others at 0 (with optional rebalance frequency).
- Zoo-factor composition support: Provides a panel-aware engine to blend Alpha Zoo factors with optional cross-sectional standardization and long-only or long-short discretization.
Quick Start
Use the multi-factor-ranking skill to generate per-date TopN long signals from a set of OHLCV dataframes for multiple instruments.
Dependency Matrix
Required Modules
pandasnumpy
Components
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: multi-factor-ranking Download link: https://github.com/loanntc/Paave/archive/main.zip#multi-factor-ranking Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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