alpha-signal

Generate daily trading signals from an SQLite factor registry and export a target portfolio.

81|13|Updated Apr 3, 2026

Search Tags:#automation #finance #portfolio #signal #icir #factors

Ready to run

npx skills add https://github.com/VernonOY/alpha-skills --skill alpha-signal

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: alpha-signal
Source: https://github.com/VernonOY/alpha-skills/tree/main/skills/alpha-signal
Command: npx skills add https://github.com/VernonOY/alpha-skills --skill alpha-signal

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill generates daily signals by reading active factors, scoring them on the latest data, and outputting a target portfolio to guide trading decisions.

Core Features & Use Cases

Reads factors from the factor registry (alpha_skills.db) and computes scores on the most recent data.
Weighs signals by factor ICIR to produce a composite target portfolio.
Outputs signals to the signals/ directory for daily tracking, backtesting, and review.

Quick Start

Run the signal generator to produce today's signals using your configured data sources.

Frequently Asked Questions about alpha-signal

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate daily trading signals for a quantitative portfolio?▼

You generate daily trading signals by reading active factors from a registry, scoring them on the latest data, and applying ICIR-weighted ranking to produce a target portfolio. This outputs a ready-to-use signal file for the next trading session.

What is ICIR-weighted ranking in factor scoring?▼

ICIR-weighted ranking combines multiple factor scores into a composite target portfolio by weighing individual factor signals based on their historical Information Ratio, prioritizing factors with stronger risk-adjusted performance.

How do I set up the environment for automated factor signal generation?▼

Setting up automated factor signal generation requires an internal SQLite registry database, a signals history directory, and a markdown configuration file. These components store active factors, track output history, and define data sources for the workflow.

Can I use this workflow for backtesting quantitative portfolios across different markets?▼

Yes, this workflow applies to quantitative portfolios across various markets. It outputs daily signal files to a dedicated directory, which can be directly used for tracking, backtesting, and reviewing portfolio performance over time.

What do I need to configure before running a daily portfolio signal generator?▼

Before running a daily portfolio signal generator, you must configure your data sources and ensure the SQLite factor registry is populated. The configuration file specifies how the system loads data and scores factors for target portfolio export.