Greedy Parameter Sweep
Try every combination of settings, pick the best. Brute force but reliable when done right.
A greedy parameter sweep tries every combination in a defined grid and picks the highest-scoring one. No clever algorithms, no statistical shortcuts : just exhaustive testing. The “greedy” label means you’re optimizing for the single best measurable outcome right now, without thinking about exploration-exploitation tradeoffs. The key property other methods lack: completeness. If the best answer is on your grid, the sweep will find it. Practical refinement: coarse-to-fine sweeping (wide grid first, fine grid around the best region) gets precision without the cost of a full fine-grained sweep.
How It Works
Define grid (list of values per parameter) → run every combination → sort by metric → inspect top 5–10 (if the winner is an outlier surrounded by poor results, it may be overfitting) → validate on held-out data.
Example
The oil model’s v14b sweep covered 9 parameters with over 200 configurations, each evaluated on 90 days of WTI data using MAE, MAPE, and R². Sweet spot: 360-day time window, 10,000 MC samples. Seven of nine parameters then locked via Karpathy ratchet; two flagged for further exploration. Referenced in Oil v16.