Uses of Class
com.quantfinlib.backtest.validation.ParameterGrid
Packages that use ParameterGrid
Package
Description
The defense against overfit backtests:
ParameterGrid +
GridSearchOptimizer enumerate
and rank parameter combinations;
WalkForwardAnalyzer optimizes
on rolling train windows and evaluates on unseen test windows, stitching
out-of-sample equity (capital carries across folds) and reporting the
walk-forward efficiency ratio;
SharpeValidation applies the
Bailey/López de Prado probabilistic and deflated Sharpe — the
multiple-testing haircut for grid-picked winners — plus the minimum
track record length (how many periods before the record MEANS
something, in closed form);
BlockBootstrap hands the
backtest Sharpe its sampling DISTRIBUTION (stationary Politis-Romano
blocks — an iid resample destroys the autocorrelation and understates
the uncertainty, the classic route to false confidence): the honest
question is whether the 5th percentile is still positive, not whether
1.2 is a good number.-
Uses of ParameterGrid in com.quantfinlib.backtest.validation
Methods in com.quantfinlib.backtest.validation that return ParameterGridMethods in com.quantfinlib.backtest.validation with parameters of type ParameterGridModifier and TypeMethodDescriptionWalkForwardAnalyzer.analyze(BarSeries series, ParameterGrid grid, StrategyFactory factory, BacktestConfig config, int trainBars, int testBars, ToDoubleFunction<PerformanceMetrics> objective) GridSearchOptimizer.best(ParameterGrid grid, StrategyFactory factory, BarSeries series, BacktestConfig config, ToDoubleFunction<PerformanceMetrics> objective) The winning parameter set only.static List<GridSearchOptimizer.Candidate> GridSearchOptimizer.search(ParameterGrid grid, StrategyFactory factory, BarSeries series, BacktestConfig config, ToDoubleFunction<PerformanceMetrics> objective)