Edge Pipeline Orchestrator

Coordinate all edge research stages into a single automated pipeline run.

When to Use

• Run the full edge pipeline from tickets (or OHLCV) to exported strategies
• Resume a partially completed pipeline from the drafts stage
• Review and revise existing strategy drafts with feedback loop
• Dry-run the pipeline to preview results without exporting

Workflow

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:
   • Review all drafts (max 2 iterations)
   • PASS verdicts accumulated; REJECT verdicts accumulated
   • REVISE verdicts trigger apply_revisions and re-review
   • Remaining REVISE after max iterations downgraded to research_probe
7. Export eligible drafts (PASS + export_ready_v1 + exportable entry_family)
8. Write pipeline_run_manifest.json with full execution trace

CLI Usage

# 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

Output