> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tqx.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Optimize factor parameters

> Search a bounded factor parameter grid and apply the selected result as a new version.

Factor optimization runs several factor analyses with different parameter combinations. It is useful for sensitivity checks, but it does not replace out-of-sample validation.

## How optimization works

<Steps>
  <Step title="Save the current factor">
    Qube saves the current code and uses the latest factor version as the optimization baseline.
  </Step>

  <Step title="Design the search grid">
    Ask Qube to identify sensible code or analysis parameters. Keep the grid at 64 combinations or fewer so you can inspect each result.
  </Step>

  <Step title="Run the combinations">
    Each combination creates a child factor analysis. The task keeps progress, status, metrics, and failure reasons for every child.
  </Step>

  <Step title="Review the best and nearby results">
    Compare the objective, IC metrics, return curves, and sensitivity across neighboring combinations.
  </Step>

  <Step title="Apply and validate">
    Apply the best parameters to create a new factor version, then run a fresh analysis on a separate period.
  </Step>
</Steps>

The default objective is `ic_mean`. Applying the best parameters changes the analysis parameters, not the factor code.

<Warning>
  Do not select an isolated historical peak as proof of quality. Prefer a stable region of parameter values and confirm it outside the optimization period.
</Warning>

After validation, use [Turn a factor into a strategy](/research/strategy/factor-to-strategy) to add position and risk rules.