
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-candidate-agent- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads edge-candidate-agent automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.jsonfortrade-strategy-pipelinePhase I. - Run preflight compatibility checks for
edge-finder-candidate/v1before pipeline execution.
Prerequisites
- Python 3.9+ with
PyYAMLinstalled. - Access to the target
trade-strategy-pipelinerepository for schema/stage validation. uvavailable 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.mdmarket_summary.jsonanomalies.jsonwatchlist.csvtickets/exportable/*.yamltickets/research_only/*.yaml
Position in Split Workflow
Recommended split workflow:
skills/edge-hint-extractor: observations/news ->hints.yamlskills/edge-concept-synthesizer: tickets/hints ->edge_concepts.yamlskills/edge-strategy-designer: concepts ->strategy_drafts+ exportable ticket YAMLskills/edge-candidate-agent(this skill): export + validate for pipeline handoff
Workflow
- Run auto-detection from EOD OHLCV:
skills/edge-candidate-agent/scripts/auto_detect_candidates.py- Optional:
--hintsfor human ideation input - Optional:
--llm-ideas-cmdfor external LLM ideation loop
- Load the contract and mapping references:
references/pipeline_if_v1.mdreferences/signal_mapping.mdreferences/research_ticket_schema.mdreferences/ideation_loop.md
- Build or update a research ticket using
references/research_ticket_schema.md. - Export candidate artifacts with
skills/edge-candidate-agent/scripts/export_candidate.py. - Validate interface and Phase I constraints with
skills/edge-candidate-agent/scripts/validate_candidate.py. - Hand off candidate directory to
trade-strategy-pipelineand run dry-run first.
Quick Commands
Daily auto-detection (with optional export/validation):
python3 skills/edge-candidate-agent/scripts/auto_detect_candidates.py \
--ohlcv /path/to/ohlcv.parquet \
--output-dir reports/edge_candidate_auto \
--top-n 10 \
--hints path/to/hints.yaml \
--export-strategies-dir /path/to/trade-strategy-pipeline/strategies \
--pipeline-root /path/to/trade-strategy-pipeline
Create a candidate directory from a ticket:
python3 skills/edge-candidate-agent/scripts/export_candidate.py \
--ticket path/to/ticket.yaml \
--strategies-dir /path/to/trade-strategy-pipeline/strategies
Validate interface contract only:
python3 skills/edge-candidate-agent/scripts/validate_candidate.py \
--strategy /path/to/trade-strategy-pipeline/strategies/my_candidate_v1/strategy.yaml
Validate both interface contract and pipeline schema/stage rules:
python3 skills/edge-candidate-agent/scripts/validate_candidate.py \
--strategy /path/to/trade-strategy-pipeline/strategies/my_candidate_v1/strategy.yaml \
--pipeline-root /path/to/trade-strategy-pipeline \
--stage phase1
Export Rules
- Keep
validation.method: full_sample. - Keep
validation.oos_ratioomitted ornull. - Export only supported entry families for v1:
pivot_breakoutwithvcp_detectiongap_up_continuationwithgap_up_detection
- Mark unsupported hypothesis families as research-only in ticket notes, not as export candidates.
Guardrails
- Reject candidates that violate schema bounds (risk, exits, empty conditions).
- Reject candidate when folder name and
idmismatch. - Require deterministic metadata with
interface_version: edge-finder-candidate/v1. - Use
--dry-runin pipeline before full execution.
Resources
skills/edge-candidate-agent/scripts/export_candidate.py
Generate strategies/<candidate_id>/strategy.yaml and metadata.json from a research ticket YAML.
skills/edge-candidate-agent/scripts/validate_candidate.py
Run interface checks and optional StrategySpec/validate_spec checks against trade-strategy-pipeline.
skills/edge-candidate-agent/scripts/auto_detect_candidates.py
Auto-detect edge ideas from EOD OHLCV, generate exportable/research tickets, and optionally export/validate automatically.
references/pipeline_if_v1.md
Condensed integration contract for edge-finder-candidate/v1.
references/signal_mapping.md
Map hypothesis families to currently exportable signal families.
references/research_ticket_schema.md
Ticket schema used by export_candidate.py.
references/ideation_loop.md
Hint schema and external LLM ideation command contract.
FAQ
What does the edge-candidate-agent skill do?
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.
How do I install edge-candidate-agent?
Run npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-candidate-agent in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/edge-candidate-agent/ (personal) or .claude/skills/edge-candidate-agent/ (project).
Which AI agents can use edge-candidate-agent?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is edge-candidate-agent safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains edge-candidate-agent?
It is published by tradermonty in tradermonty/claude-trading-skills. It has 3,348 recorded installs.
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