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.

Output

• strategies/<candidate_id>/strategy.yaml: Phase I-compatible strategy spec.
• strategies/<candidate_id>/metadata.json: provenance metadata including interface version and ticket context.
• Validation status from scripts/validate_candidate.py (pass/fail + reasons).
• Daily detection artifacts:
  • daily_report.md
  • market_summary.json
  • anomalies.json
  • watchlist.csv
  • tickets/exportable/*.yaml
  • tickets/research_only/*.yaml

Position in Split Workflow

Recommended split workflow:

1. skills/edge-hint-extractor: observations/news -> hints.yaml
2. skills/edge-concept-synthesizer: tickets/hints -> edge_concepts.yaml
3. skills/edge-strategy-designer: concepts -> strategy_drafts + exportable ticket YAML
4. skills/edge-candidate-agent (this skill): export + validate for pipeline handoff

Workflow