edge-candidate-agent

Name: edge-candidate-agent
Availability: InStock
Author: fabio29t

Generates and prioritizes US equity edge research tickets from EOD observations into strategy.yaml and metadata.json.

2|Updated Mar 4, 2026

Search Tags:#automation #research #pipeline #backtesting #trading #strategy generation

Ready to run

npx skills add https://github.com/Fabio29T/Trading-Skills --skill edge-candidate-agent

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: edge-candidate-agent
Source: https://github.com/Fabio29T/Trading-Skills/tree/main/skills/edge-candidate-agent
Command: npx skills add https://github.com/Fabio29T/Trading-Skills --skill edge-candidate-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of converting raw market observations and hypotheses into structured, exportable research tickets and Phase I-compatible strategy specifications for the trade-strategy-pipeline.

Core Features & Use Cases

Automated Candidate Detection: Identifies potential trading edge candidates from EOD OHLCV data.
Research Ticket Generation: Converts market observations into reproducible research tickets.
Pipeline Export: Exports validated candidates as strategy.yaml and metadata.json files, ready for the trade-strategy-pipeline.
Compatibility Checks: Preflight checks interface compatibility before pipeline execution.

Quick Start

Run auto-detection from EOD OHLCV data using the provided command.

Frequently Asked Questions about edge-candidate-agent

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

FAQPage Schema

How do I convert market observations into reproducible research tickets for backtesting?▼

Yes, you can generate trading strategy candidates directly from EOD OHLCV data. The automated candidate detection feature scans end-of-day observations to identify potential US equity long-side edge opportunities and structures them into pipeline-ready research tickets.

What file formats do I need for exporting strategy candidates to a backtesting pipeline?▼

Exporting strategy candidates to a backtesting pipeline requires `strategy.yaml` and `metadata.json` files. This Skill generates both formats after validating ideas, ensuring interface compatibility with the trade-strategy-pipeline Phase I before execution.

Does the trade-strategy-pipeline require preflight compatibility checks before backtesting?▼

Yes, the trade-strategy-pipeline requires preflight compatibility checks before backtesting. This Skill performs interface compatibility validation on generated candidate specifications to ensure the exported `strategy.yaml` and `metadata.json` files are ready for Phase I execution.

Can I automate US equity long-side edge research from end-of-day data?▼

Automating US equity long-side edge research from end-of-day data is the core function of this Skill. It identifies potential trading edge candidates from EOD OHLCV observations and prioritizes them into structured research tickets automatically.

What is the best way to structure trading hypotheses for pipeline backtests?▼

The best way to structure trading hypotheses for pipeline backtests is converting them into standardized `strategy.yaml` and `metadata.json` specifications. This Skill transforms raw anomalies into reproducible research tickets, ensuring validated ideas meet pipeline interface requirements before backtesting.