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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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下载 Zip 下载中... 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
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.pyOptional: --hints for human ideation input
Optional: --llm-ideas-cmd for 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-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_detectiongap_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.pyGenerate strategies/<candidate_id>/strategy.yaml and metadata.json from a research ticket YAML.
skills/edge-candidate-agent/scripts/validate_candidate.pyRun interface checks and optional StrategySpec/validate_spec checks against trade-strategy-pipeline.
skills/edge-candidate-agent/scripts/auto_detect_candidates.pyAuto-detect edge ideas from EOD OHLCV, generate exportable/research tickets, and optionally export/validate automatically.
references/pipeline_if_v1.mdCondensed integration contract for edge-finder-candidate/v1.
references/signal_mapping.mdMap hypothesis families to currently exportable signal families.
references/research_ticket_schema.mdTicket schema used by export_candidate.py.
references/ideation_loop.mdHint schema and external LLM ideation command contract.
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