Uses of Class
com.quantfinlib.backtest.validation.GridSearchOptimizer.Candidate

Packages that use GridSearchOptimizer.Candidate

Package	Description
com.quantfinlib.backtest.validation	Validation tools that test a backtest for overfitting.

Uses of GridSearchOptimizer.Candidate in com.quantfinlib.backtest.validation

Methods in com.quantfinlib.backtest.validation that return GridSearchOptimizer.Candidate

Modifier and Type	Method	Description
static GridSearchOptimizer.Candidate	GridSearchOptimizer.best(ParameterGrid grid, StrategyFactory factory, BarSeries series, BacktestConfig config, ToDoubleFunction<PerformanceMetrics> objective)	The winning parameter set only.

Methods in com.quantfinlib.backtest.validation that return types with arguments of type GridSearchOptimizer.Candidate

Modifier and Type	Method	Description
static List<GridSearchOptimizer.Candidate>	GridSearchOptimizer.search(ParameterGrid grid, StrategyFactory factory, BarSeries series, BacktestConfig config, ToDoubleFunction<PerformanceMetrics> objective)

Method parameters in com.quantfinlib.backtest.validation with type arguments of type GridSearchOptimizer.Candidate

Modifier and Type	Method	Description
static double	GridSearchOptimizer.deflatedSharpeOfWinner(List<GridSearchOptimizer.Candidate> ranked, double[] winnerReturns, int periodsPerYear)	The multiple-testing haircut for the grid's winner: the probability that the top-ranked candidate's Sharpe beats what the best of GridSearchOptimizer.effectiveTrials(List) zero-skill trials would have scored anyway (the deflated Sharpe of SharpeValidation.probabilisticSharpe(double, double, int, double, double) against E[max] with N̂ in place of the raw grid size).
static double	GridSearchOptimizer.effectiveTrials(List<GridSearchOptimizer.Candidate> ranked)	Effective number of independent trials in a search, N̂ = ρ̄ + (1 − ρ̄)·M for M trials whose per-bar return streams have mean pairwise correlation ρ̄ (clamped to [0, 1]); the average-correlation approximation in Bailey and López de Prado (2014, J.