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edge-pipeline-orchestrator

tradermonty/claude-trading-skills

Orchestrate multi-stage edge research pipelines from candidate detection through strategy export.

What is edge-pipeline-orchestrator?

Automates the full edge research workflow from raw market data or tickets through hints extraction, concept synthesis, strategy drafting, review-revision feedback loops, and final export. Use this to coordinate end-to-end edge research runs, resume partial pipelines, or dry-run before committing to exports.

  • Load and execute pipeline configuration from CLI arguments
  • Run auto_detect stage to generate tickets from raw OHLCV data
  • Extract edge hints from market summary and anomalies
  • Synthesize abstract edge concepts from tickets and hints
  • Design strategy drafts from concepts with configurable parameters
  • Execute review-revision feedback loop with up to 2 iterations and verdict accumulation

How to install edge-pipeline-orchestrator

npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-pipeline-orchestrator
Prerequisites
  • Python 3 environment with orchestration scripts installed
  • Input data: either tickets directory or OHLCV CSV file
  • Optional: LLM-generated hints file (YAML format) for hybrid workflows
Claude Code
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How to use edge-pipeline-orchestrator

  1. 1.Prepare input data: tickets directory or OHLCV CSV file
  2. 2.Run orchestrator with appropriate CLI flags (full pipeline, resume, review-only, or dry-run mode)
  3. 3.Monitor pipeline execution and review intermediate outputs in output directory
  4. 4.For review-revision mode: examine draft verdicts and apply feedback via REVISE verdicts
  5. 5.Export eligible strategies from final output directory or integrate into downstream systems

Use cases

Good for
  • Run the complete edge research pipeline from raw OHLCV data to exported trading strategies
  • Resume a partially completed pipeline from the drafts stage without re-running earlier stages
  • Review and revise existing strategy drafts with feedback loop and revision tracking
  • Dry-run the pipeline to preview results and validate configuration before exporting
  • Integrate LLM-generated edge hints into the pipeline for hybrid human-AI research workflows
Who it's for
  • Quantitative researchers building systematic edge detection pipelines
  • Trading strategy developers coordinating multi-stage research workflows
  • Teams using Claude Code for LLM-augmented market analysis and strategy synthesis
  • Researchers validating edge concepts before committing to strategy export

edge-pipeline-orchestrator FAQ

Can I resume a pipeline that was interrupted?

Yes, use --resume-from drafts with --drafts-dir to skip earlier stages and continue from the drafts stage.

What does dry-run mode do?

Dry-run executes the full pipeline but skips the export stage, allowing you to preview results without writing final strategy files.

How does the review-revision feedback loop work?

Drafts are reviewed (max 2 iterations); PASS verdicts are accumulated, REJECT verdicts are accumulated, and REVISE verdicts trigger revisions and re-review. Remaining REVISE after max iterations are downgraded to research_probe.

Can I integrate LLM-generated hints into the pipeline?

Yes, use --llm-ideas-file with --promote-hints during full pipeline runs to inject Claude-generated edge hints into the concepts stage.

What output files are generated?

Pipeline produces pipeline_run_manifest.json, intermediate stage outputs (tickets, hints, concepts, drafts), review iterations, and final exportable strategies organized by candidate_id.

Full instructions (SKILL.md)

Source of truth, from tradermonty/claude-trading-skills.


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.

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

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:

  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
  1. 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