Optimizer
Search the parameter space for stronger settings — and treat the result with suspicion.
What it tunes
The optimizer searches the parameters you've declared as ranges — indicator periods, stop and target distances, thresholds — and looks for the combination that scored best on your chosen metric over the history you ran. It explores far more combinations than you could by hand.
Optimize vs walk-forward
A plain optimize fits the single best parameters to the whole window — fast, but the most prone to curve-fitting. Walk-forward optimization instead tunes on an early window and tests on the next, unseen one, rolling forward, so the score reflects out-of-sample performance. Prefer walk-forward whenever you can afford the extra runs.
Apply to strategy
When a run finishes, the best parameters are shown as tiles. Apply to «strategy» patches those values back into your saved rule-set in one click — so you can move straight from search to a concrete, versioned strategy without retyping.
The overfitting caveat
Optimized numbers are hypotheses, not guarantees. The more combinations you try, the more likely the winner is just luck. Always re-check an optimized strategy with walk-forward and the Validation tools before trusting it, and be suspicious of results that look too clean.