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.