backtest-expert
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating…
Orchestrate the full edge research pipeline from candidate detection through strategy design, review, revision, and export. Use when coordinating multi-stage edge research workflows end-to-end.
$ npx -y skills add BaggaT236/AI-Trading-Skills --skill edge-pipeline-orchestrator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/edge-pipeline-orchestratorContext preview
The summary Claude sees to decide when to auto-load this skill.
Orchestrate the full edge research pipeline from candidate detection through strategy design, review, revision, and export. Use when coordinating multi-stage edge research workflows end-to-end.
name: edge-pipeline-orchestrator description: Orchestrate the full edge research pipeline from candidate detection through strategy design, review, revision, and export. Use when coordinating multi-stage edge research workflows end-to-end.
Coordinate all edge research stages into a single automated pipeline run.
1. Load pipeline configuration from CLI arguments 2. Run auto_detect stage if --from-ohlcv is provided (generates tickets from raw OHLCV data) 3. Run hints stage to extract edge hints from market summary and anomalies 4. Run concepts stage to synthesize abstract edge concepts from tickets and hints 5. Run drafts stage to design strategy drafts from concepts 6. Run review-revision feedback loop:
7. Export eligible drafts (PASS + export_ready_v1 + exportable entry_family) 8. Write pipeline_run_manifest.json with full execution trace
# Full pipeline from tickets python3 scripts/orchestrate_edge_pipeline.py \ --tickets-dir path/to/tickets/ \ --output-dir reports/edge_pipeline/ # Full pipeline from OHLCV python3 scripts/orchestrate_edge_pipeline.py \ --from-ohlcv path/to/ohlcv.csv \ --output-dir reports/edge_pipeline/ # Resume from drafts stage python3 scripts/orchestrate_edge_pipeline.py \ --resume-from drafts \ --drafts-dir path/to/drafts/ \ --output-dir reports/edge_pipeline/ # Review-only mode python3 scripts/orchestrate_edge_pipeline.py \ --review-only \ --drafts-dir path/to/drafts/ \ --output-dir reports/edge_pipeline/ # Dry run (no export) python3 scripts/orchestrate_edge_pipeline.py \ --tickets-dir path/to/tickets/ \ --output-dir reports/edge_pipeline/ \ --dry-run
All artifacts are written to `--output-dir`:
output-dir/
├── pipeline_run_manifest.json
├── tickets/ (from auto_detect)
├── hints/hints.yaml (from hints)
├── concepts/edge_concepts.yaml
├── drafts/*.yaml
├── exportable_tickets/*.yaml
├── reviews_iter_0/*.yaml
├── reviews_iter_1/*.yaml (if needed)
└── strategies/<candidate_id>/
├── strategy.yaml
└── metadata.jsonRun the LLM-augmented pipeline entirely within Claude Code:
1. Run auto_detect to produce `market_summary.json` + `anomalies.json` 2. Claude Code analyzes data and generates edge hints 3. Save hints to a YAML file:
- title: Sector rotation into industrials observation: Tech underperforming while industrials show relative strength symbols: [CAT, DE, GE] regime_bias: Neutral mechanism_tag: flow preferred_entry_family: pivot_breakout hypothesis_type: sector_x_stock
4. Run orchestrator with `--llm-ideas-file` and `--promote-hints`:
python3 scripts/orchestrate_edge_pipeline.py \ --tickets-dir path/to/tickets/ \ --llm-ideas-file llm_hints.yaml \ --promote-hints \ --as-of 2026-02-28 \ --max-synthetic-ratio 1.5 \ --strict-export \ --output-dir reports/edge_pipeline/
Note: `--llm-ideas-file` and `--promote-hints` are effective only during full pipeline runs. `--resume-from drafts` and `--review-only` skip hints/concepts stages, so these flags are ignored.
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