| name | brain-improve-alpha-performance |
| description | Provides a systematic 5-step workflow for improving WorldQuant BRAIN alphas. Includes steps for gathering alpha info, evaluating datafields, proposing idea-focused improvements (using arXiv), simulating variants, and validating. Use when the user wants to improve an existing alpha or fix failing submission tests. |
Alpha Improvement Workflow
This repeatable workflow enhances alphas by focusing on core idea refinements rather than just mechanical tweaks.
For the detailed steps, analysis techniques, and best practices, see reference.md.
Step 1: Gather Alpha Information (5-10 mins)
Goal: Identify weaknesses (low Sharpe, high correlation, etc.).
- Fetch alpha details (
get_alpha_details).
- Check PnL, Sharpe, Fitness, Turnover.
- Run submission checks (
get_submission_check) and correlation checks (check_correlation).
Step 2: Evaluate Core Datafield(s) (5-10 mins)
Goal: Understand data properties (sparsity, frequency).
- Run 6 evaluation simulations (Coverage, Non-Zero, Update Frequency, Bounds, Central Tendency, Distribution) using
brain-datafield-exploration skill methods.
Step 3: Propose Idea-Focused Improvements (10-15 mins)
Goal: Evolve the signal with theory-backed concepts.
- Review docs for tips (ATOM principle, flipping negatives).
- Search arXiv for concepts (e.g., "persistence", "momentum").
- Brainstorm 4-6 variants (e.g., add decay, change normalization).
Step 4: Simulate and Test Variants (10-20 mins)
Goal: Compare ideas via metrics.
- Use
create_multiSim to test variants.
- Compare Fitness, Sharpe, and Sub-universe performance.
Step 5: Validate and Iterate (5-10 mins)
Goal: Confirm submittability.
- Run final checks.
- If failing, repeat from Step 3 with new ideas.
- If passing, submit!
Best Practices
- Cycle Limit: 3-5 iterations per alpha.
- Focus: 70% on ideas, 30% on parameter tweaks.
- Goal: Passing checks + stable yearly stats.
