Manage factor metadata with CRUD operations in a SQLite-backed registry.
npx skills add https://github.com/VernonOY/alpha-skills --skill alpha-library
Please help me install this Agent Skill. Skill: alpha-library Source: https://github.com/VernonOY/alpha-skills/tree/main/skills/alpha-library Command: npx skills add https://github.com/VernonOY/alpha-skills --skill alpha-library
The Factor Library helps data scientists and quants manage their registered factors in a local SQLite database, enabling easy registration, listing, searching, and retirement of factors with persistent storage.
Register a factor with its name and expression, then list or search to manage your factor registry.
High-intent search queries and answers about installing and using this skill.
You can manage a local factor library using a SQLite-backed Python API that supports registering, listing, searching, and retiring factors with persistent local storage. It stores factor metadata like ic_mean and icir for multi-market analysis.
When registering factors, you can store name, expression, category, market, description, and evaluation metrics including ic_mean, icir, best_holding_period, and quality. This metadata enables effective factor search and multi-market analysis.
You can persistently register and retire factors across multi-market contexts using a lightweight Python API with CRUD operations. The SQLite storage ensures your factor registry remains safely persisted locally.
No, the factor library relies on a self-contained SQLite database and requires no external dependencies. It provides a lightweight Python API for all CRUD operations directly on your local machine.
You search and list factors in a SQLite registry using the provided Python API functions. The API allows you to retrieve factor details, update status, and list registered factors based on your multi-market search criteria.
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santoosaraujoBrowse, validate, and benchmark alpha factor libraries with IC/IR analysis.
