Angie · learn

Grid search

Testing every combination of the settings you chose. Exhaustive and certain, and the count explodes as soon as you add a third or fourth knob.

Updated 2026-09-03

A grid search tries every combination of the parameter values you specify. Three parameters with ten values each is a thousand backtests, and it will find the best of those thousand.

Its strength is that it is exhaustive and completely reproducible: run it twice and you get the same answer, and you can see the whole surface rather than a single point. Its weakness is that the number of tests explodes as parameters are added, so it is only practical in low dimensions.

The exhaustiveness is also its danger. Running a thousand backtests and keeping the best one is close to a definition of curve fitting. The useful output of a grid search is not the winning combination, it is the shape of the surface: look for a broad region that works, and distrust a lone spike surrounded by failures.

See also

  • Random search — Sampling combinations at random instead of testing all of them. Cheap coverage of a space too large to walk, at the cost of possibly missing the best corner.
  • Evolutionary search — Seeding a population of settings, keeping the strongest, then mutating and recombining them over generations to home in on what works.
  • Curve fitting — Tuning a strategy until it memorises one stretch of history. It produces beautiful backtests and no future returns, which is why results are held out and re-tested.

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Educational content, not investment advice. Angie explains how a valuation is built so you can judge it yourself. What you do with that is your call.

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