/edge-candidate-agent

name: edge-candidate-agent
description: 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.

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.