Agent skill · data analytics · tradermonty
edge-candidate-agent
Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests.
Why this skill is useful
Adds executable scripts for auto-detection, export, and validation of trading strategy candidates that are not commonly found in public resources.
What it needs
Requires python, pyyaml installed locally. About 2k tokens when loaded. Last updated 2026-08-06. 2,581 stars on the source repository.
What this skill does
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/<candidateid>/strategy.yaml: Phase I-compatible strategy spec. strategies/<candidateid>/metadata.json: provenance metadata including interface version and ticket context. Validation status from scripts/validatecandidate.py (pass/fail + reasons). Daily detection artifacts: dailyreport.md marketsummary.json anomalies.json watchlist.csv tickets/exportable/.yaml tickets/researchonly/.yaml Position in Split Workflow Recommended split workflow: 1. skills/edge-hint-extractor: observations/news -> hints.yaml 2. skills/edge-concept-synthesizer: tickets/hints -> edgeconcepts.yaml 3. skills/edge-strategy-designer: concepts -> strategydrafts + exportable ticket YAML 4. skills/edge-candidate-agent (this skill): export + validate for pipeline handoff Workflow 1. Run auto-detection from EOD OHLCV: skills/edge-candidate-agent/scripts/autodetectcandidates.py Optional: --hints for human ideation input Optional: --llm-ideas-cmd for external LLM ideation loop 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/edge-candidate-agent