
Edge Candidate Agent
FreeTransform market insights into actionable research tickets.
Free · Opens the source repo
What Edge Candidate Agent does
The Edge Candidate Agent is designed to streamline the process of converting end-of-day market observations into structured research tickets that are ready for trading strategies. By prioritizing signal quality and interface compatibility, this skill ensures that users can efficiently turn hypotheses and anomalies into reproducible research. It can operate as a standalone solution or as part of a larger workflow, primarily focusing on the final steps of exporting and validating candidate specifications for the trade strategy pipeline.
Users can leverage this skill to automate the detection of potential trading candidates based on daily market data, utilizing scripts that analyze end-of-day OHLCV data. The auto-detection feature can run with or without human input, allowing for flexibility in generating insights. Once candidates are identified, the skill facilitates the creation of candidate specifications in the required formats, such as strategy.yaml and metadata.json, which are essential for the subsequent phases of the trade strategy pipeline.
The Edge Candidate Agent is particularly beneficial for traders and quantitative researchers who need to systematically document and validate their findings. By providing a structured approach to research ticket creation and ensuring compatibility with the trade strategy pipeline, this skill helps users maintain a high standard of quality in their trading strategies. Additionally, it includes validation mechanisms to ensure that candidates meet the necessary schema and contract requirements before they are executed in the pipeline.
Overall, this skill is a valuable tool for anyone involved in trading strategy development who seeks to enhance their research capabilities and operational efficiency.
When to use it
Use this skill when you need to convert daily market data into actionable research tickets or validate candidate specifications for trading strategies.
When not to use it
This skill may not be suitable for users looking for a comprehensive trading strategy development tool, as it focuses specifically on ticket generation and validation.
What you can build with it
Daily Market Analysis
Run auto-detection scripts daily to identify new edge candidates based on market observations.
Research Ticket Creation
Convert validated trading hypotheses into structured research tickets for further analysis.
Candidate Validation
Ensure that candidate specifications meet the necessary schema and interface requirements before execution.
How to install Edge Candidate Agent
View source1. Install with the skills CLI
npx skills add tradermonty/claude-trading-skills/edge-candidate-agent --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by tradermontyEdge 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.
Frequently asked questions about Edge Candidate Agent
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