Edge Candidate Agent

Turns market observations and anomalies into structured research tickets that feed directly into trade-strategy-pipeline Phase I backtests. You run auto_detect_candidates.py on EOD OHLCV data, it generates prioritized tickets as YAML, then export_candidate.py converts them into strategy.yaml and metadata.json that match the edge-finder-candidate/v1 interface contract. The validate_candidate.py script checks schema compliance before handoff. Sits at the end of a four-stage workflow after hint extraction, concept synthesis, and strategy design. Useful if you're running systematic edge research and need the format boundaries enforced before expensive backtesting. The guardrails are strict: only pivot_breakout and gap_up_continuation families export, everything else stays research-only.

skills.sh installs: 3.5K
GitHub stars: 3.7K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add tradermonty/claude-trading-skills --skill edge-candidate-agent --agent claude-code

Installs into .claude/skills of the current project.

Files

Edge Candidate Agent

Overview

Convert daily market observations into reproducible research tickets and Phase I-compatible candidate specs. Prioritize signal quality and interface compatibility over aggressive strategy proliferation. This skill can run end-to-end standalone, but in the split workflow it primarily serves the final export/validation stage.

When to Use

Convert market observations, anomalies, or hypotheses into structured research tickets.
Run daily auto-detection to discover new edge candidates from EOD OHLCV and optional hints.
Export validated tickets as strategy.yaml + metadata.json for trade-strategy-pipeline Phase I.
Run preflight compatibility checks for edge-finder-candidate/v1 before pipeline execution.

Prerequisites

Python 3.9+ with PyYAML installed.
Access to the target trade-strategy-pipeline repository for schema/stage validation.
uv available when running pipeline-managed validation via --pipeline-root.