Edge candidate agent

Skill xonevn-ai/xone-trading-skills/skills/edge-candidate-agent

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Install

npx -y skills add xonevn-ai/xone-trading-skills --skill edge-candidate-agent

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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.

SKILL.md

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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:

skills/edge-hint-extractor: observations/news -> hints.yaml
skills/edge-concept-synthesizer: tickets/hints -> edge_concepts.yaml
skills/edge-strategy-designer: concepts -> strategy_drafts + exportable ticket YAML
skills/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: --hints for human ideation input
- Optional: --llm-ideas-cmd for external LLM ideation loop
Load the contract and mapping references:
- references/pipeline_if_v1.md
- references/signal_mapping.md
- references/research_ticket_schema.md
- references/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-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.