Edge Pipeline Orchestrator

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

• Load pipeline configuration from CLI arguments
• Run auto_detect stage if --from-ohlcv is provided (generates tickets from raw OHLCV data)
• Run hints stage to extract edge hints from market summary and anomalies
• Run concepts stage to synthesize abstract edge concepts from tickets and hints
• Run drafts stage to design strategy drafts from concepts
• 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
• Export eligible drafts (PASS + export_ready_v1 + exportable entry_family)
• 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

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.json

Claude Code LLM-Augmented Workflow

Run the LLM-augmented pipeline entirely within Claude Code:

• Run auto_detect to produce market_summary.json + anomalies.json
• Claude Code analyzes data and generates edge hints
• 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

• 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/

Optional Flags

• --as-of YYYY-MM-DD — forwarded to hints stage for date filtering
• --strict-export — export-eligible drafts with any warn finding get REVISE instead of PASS
• --max-synthetic-ratio N — cap synthetic tickets to N × real ticket count (floor: 3)
• --overlap-threshold F — condition overlap threshold for concept deduplication (default: 0.75)
• --no-dedup — disable concept deduplication

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.

Resources

• references/pipeline_flow.md — Pipeline stages, data contracts, and architecture
• references/revision_loop_rules.md — Review-revision feedback loop rules and heuristics