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Parameter optimization tests several values for parameters exposed by the strategy. It helps you explore sensitivity; it does not remove the need for out-of-sample validation or risk review.

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How optimization works

1

Choose the strategy version

Start from the version whose code and backtest settings you understand.
2

Define the search space

Ask Qube to identify tunable parameters or specify the candidate values yourself. Keep the grid small enough to inspect and explain.
3

Run the combinations

Qube runs the selected combinations and tracks each result in the optimization task. Failed combinations remain visible with their failure reason.
4

Review the best result and sensitivity

Compare the objective metric, equity curves, trade count, and drawdown. Check whether nearby parameter values behave similarly.
5

Apply and verify

Applying the selected parameters saves a new strategy version. Run a fresh backtest and compare it with the prior version before treating the change as an improvement.

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What to review

  • The objective used to rank combinations, such as Sharpe ratio.
  • Total return, annualized return, maximum drawdown, and trade count.
  • Whether the best result is an isolated peak or part of a stable region.
  • Whether the selected period and costs are realistic.
  • Whether the result survives a different or later validation period.
Do not select a parameter set only because it has the highest historical return. A more stable result with lower drawdown may be a better research candidate.