Class GridSearchOptimizer

java.lang.Object

com.quantfinlib.backtest.validation.GridSearchOptimizer

public final class GridSearchOptimizer
extends Object

Exhaustive strategy parameter search: backtests every grid combination and ranks by an objective (e.g. PerformanceMetrics::sharpeRatio). Non-finite objectives rank last. Feed the resulting in-sample winners into WalkForwardAnalyzer — never trust them raw.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final record	GridSearchOptimizer.Candidate

Method Summary

Modifier and Type	Method	Description
static GridSearchOptimizer.Candidate	best(ParameterGrid grid, StrategyFactory factory, BarSeries series, BacktestConfig config, ToDoubleFunction<PerformanceMetrics> objective)	The winning parameter set only.
static double	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 ranked.size() zero-skill trials would have scored anyway (SharpeValidation.deflatedSharpe(double, double[], int, double, double)).
static List<GridSearchOptimizer.Candidate>	search(ParameterGrid grid, StrategyFactory factory, BarSeries series, BacktestConfig config, ToDoubleFunction<PerformanceMetrics> objective)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details

search

public static List<GridSearchOptimizer.Candidate> search(ParameterGrid grid,
 StrategyFactory factory,
 BarSeries series,
 BacktestConfig config,
 ToDoubleFunction<PerformanceMetrics> objective)

best

public static GridSearchOptimizer.Candidate best(ParameterGrid grid,
 StrategyFactory factory,
 BarSeries series,
 BacktestConfig config,
 ToDoubleFunction<PerformanceMetrics> objective)

The winning parameter set only.

deflatedSharpeOfWinner

public static double 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 ranked.size() zero-skill trials would have scored anyway (SharpeValidation.deflatedSharpe(double, double[], int, double, double)). A grid search computes a Sharpe for every trial and then quietly reports only the maximum — this is the one number that makes that selection honest. Values near 1 mean the winner survives its own search; below ~0.95 the "best" parameter set is indistinguishable from picking the luckiest of N random ones.

Parameters:

ranked - result of search(com.quantfinlib.backtest.validation.ParameterGrid, com.quantfinlib.backtest.validation.StrategyFactory, com.quantfinlib.core.BarSeries, com.quantfinlib.backtest.BacktestConfig, java.util.function.ToDoubleFunction<com.quantfinlib.backtest.PerformanceMetrics>) (uses every trial's Sharpe as the null distribution), ≥ 2 trials

winnerReturns - the winner's per-period returns (derive from its equity curve), ≥ 4 observations

periodsPerYear - the annualization used by the backtest metrics