Data Science & Analytics
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.
- Publisher / source
- edtechre/pybroker (found on GitHub top repositories)
- Upstream record
- edtechre/pybroker
- Version
- Version 1
- Published
- Publication review
- Approved for publication
- Content fingerprint
ec16ef0ab3bea114357ce4d765b38d5dc8b8affc0d37c5e4f5c0bd75de19de0c