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.
What this skill does
# 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): ```bash 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: ```bash 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: ```bash 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: ```bash 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.
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