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Edge Candidate Agent

Free

Transform market insights into actionable research tickets.

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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 source

1. Install with the skills CLI

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

2. 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 tradermonty

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

  1. Run auto-detection from EOD OHLCV:
    • skills/edge-candidate-agent/scripts/auto_detect_candidates.py
    • Optional: --hints for human ideation input
    • Optional: --llm-ideas-cmd for external LLM ideation loop
  2. Load the contract and mapping references:
    • references/pipeline_if_v1.md
    • references/signal_mapping.md
    • references/research_ticket_schema.md
    • references/ideation_loop.md
  3. Build or update a research ticket using references/research_ticket_schema.md.
  4. Export candidate artifacts with skills/edge-candidate-agent/scripts/export_candidate.py.
  5. Validate interface and Phase I constraints with skills/edge-candidate-agent/scripts/validate_candidate.py.
  6. Hand off candidate directory to trade-strategy-pipeline and 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_ratio omitted or null.
  • Export only supported entry families for v1:
    • pivot_breakout with vcp_detection
    • gap_up_continuation with gap_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 id mismatch.
  • Require deterministic metadata with interface_version: edge-finder-candidate/v1.
  • Use --dry-run in 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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