Individual skill

pybroker-optimize

Tune PyBroker strategy hyperparameters with Optuna-backed search using the bundled PyBroker wiki references generated from the local docs. Use when an agent needs to declare tunable values with pybroker.hyperparam, run Strategy.optimize with grid, TPE, or random samplers, choose n_trials, direction, train_size, or seed, write score functions over TestResult metrics, wire hyperparams into indicator kwargs or ctx.hyperparam via add_execution(hyperparams=...), pass custom Optuna samplers or a supplied study, inspect OptimizeResult, WindowOptimizeResult, or study.trials_dataframe(), run walkforward optimization with windows, pin winning values with backtest(params=...), or debug failed trials and grid explosions.

skills/pybroker-optimize/SKILL.md

Source commit 01f11fdd42b76ef564a7f550c247a3dfce81330e

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Nov 2, 2026

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