Uses of Class
com.quantfinlib.alpha.AlphaContext

Packages that use AlphaContext

Package	Description
com.quantfinlib.alpha	Cross-sectional alpha research: signal generation, evaluation, validation, execution-aware backtesting, portfolio construction and reporting, each a separate step.

Uses of AlphaContext in com.quantfinlib.alpha

Methods in com.quantfinlib.alpha that return AlphaContext

Modifier and Type	Method	Description
static AlphaContext	AlphaContext.of(Map<String, BarSeries> data)	Panel without fundamentals (technical factors only).
static AlphaContext	AlphaContext.of(Map<String, BarSeries> data, Map<String, Fundamentals> fundamentals)	Panel with a static fundamentals snapshot.
AlphaContext	AlphaContext.withFundamentalsHistory(FundamentalsHistory history)	The same panel with point-in-time fundamentals attached.
AlphaContext	AlphaContext.withUniverse(PointInTimeUniverse universe)	The same panel with a point-in-time universe attached (see the class doc).

Methods in com.quantfinlib.alpha with parameters of type AlphaContext

Modifier and Type	Method	Description
static BacktestAttribution	BacktestAttribution.attribute(AlphaContext ctx, AlphaBacktester.Traced traced, String[] sectors, AlphaRiskModels.Spec spec)	Attributes a traced run.
static CapacityCurve	CapacityCurve.compute(AlphaContext ctx, AlphaFactor factor, AlphaBacktester.Config base, AlphaBacktester.RiskOptions options, double[] capitals, double maxParticipation)	As CapacityCurve.compute(AlphaContext, AlphaFactor, AlphaBacktester.Config, AlphaBacktester.WeightBuilder, double[], double) with the risk-model construction.
static CapacityCurve	CapacityCurve.compute(AlphaContext ctx, AlphaFactor factor, AlphaBacktester.Config base, AlphaBacktester.WeightBuilder builder, double[] capitals, double maxParticipation)	Runs the backtest at each capital (ascending, each > 0) with the given construction.
static AlphaValidation.CrossValidationResult	AlphaValidation.crossValidate(AlphaContext ctx, AlphaFactor factor, int horizon, int startIndex, int k)	Splits the evaluation range into k contiguous blocks and recomputes the mean IC inside each.
static AlphaReport.Decay	AlphaReport.decayProfile(AlphaContext ctx, AlphaFactor factor, int startIndex, int[] horizons)	Mean IC against cumulative forward returns (t, t+h] at each horizon (non-overlapping grid per horizon).
static FactorRiskModel	AlphaRiskModels.estimate(AlphaContext ctx, int index, String[] sectors, AlphaRiskModels.Spec spec)	Estimates the model on the spec.window() periods ending at index, forecasting the period after it.
static SignalEvaluator.Report	SignalEvaluator.evaluate(AlphaContext ctx, AlphaFactor factor, int startIndex, int horizon)	Evaluates a factor at every bar in [startIndex, ctx.bars() − horizon) against overlapping horizon-bar forward returns (see the class doc for each metric).
static double	SignalEvaluator.factorExposure(AlphaContext ctx, AlphaFactor a, AlphaFactor b, int startIndex, int step)	Mean cross-sectional rank correlation between two factors' scores — how much of factor B is already inside factor A.
static AlphaCombiner.Combination	AlphaCombiner.fit(AlphaContext ctx, List<AlphaFactor> factors, int fromIndex, int toIndex, AlphaCombiner.Settings settings)	Fits the combination on IC dates fromIndex, fromIndex + step, … up to toIndex − horizon.
static String	AlphaReport.formatIcTable(AlphaContext ctx, List<AlphaFactor> factors, int startIndex, int[] horizons)	AlphaReport.icTable(AlphaContext, List, int, int[]) as plain text, one row per factor.
static double[][]	AlphaReport.icTable(AlphaContext ctx, List<AlphaFactor> factors, int startIndex, int[] horizons)	Mean IC of each factor at each forward horizon ([factor][horizon]), the same statistic as AlphaReport.decayProfile(AlphaContext, AlphaFactor, int, int[]) for one factor.
static AlphaReport.LagDecay	AlphaReport.lagIcProfile(AlphaContext ctx, AlphaFactor factor, int startIndex, int maxLag)	IC of the scores at t against the one-bar return over (t+h−1, t+h] for h = 1..maxLag, averaged over every bar t from startIndex with the whole window in sample, then fitted by least squares to IC(h) = IC₀·exp(−(h−1)/τ) (for fixed τ the optimal IC₀ is closed-form; τ is found by golden-section search on log τ over [0.05, 10⁵] bars, and reported as +∞ when the best fit sits at the upper bound).
static double[]	FactorReturns.longShort(AlphaContext ctx, AlphaFactor factor, int startIndex, int quantiles, int rebalanceEvery)	Per-bar long-short factor returns over (t, t+1] for t = startIndex .. bars−2, so the result has bars − 1 − startIndex entries.
static AlphaValidation.RobustnessResult	AlphaValidation.monteCarloRobustness(AlphaContext ctx, AlphaFactor factor, int horizon, int startIndex, int trials, long seed)	AlphaValidation.monteCarloRobustness(AlphaContext, AlphaFactor, int, int, int, long, NullModel) with the cross-sectional null.
static AlphaValidation.RobustnessResult	AlphaValidation.monteCarloRobustness(AlphaContext ctx, AlphaFactor factor, int horizon, int startIndex, int trials, long seed, AlphaValidation.NullModel model)	Permutation test of the mean rank IC on the non-overlapping grid (startIndex, stepping by horizon).
static AlphaValidation.SensitivityResult	AlphaValidation.parameterSensitivity(AlphaContext ctx, List<AlphaFactor> sweep, int horizon, int startIndex)	Evaluates each candidate of an ordered sweep (neighbours in the list are neighbours in parameter space) and reports how far the IC falls when the chosen parameter moves one step.
static SignalEvaluator.QuantileReport	SignalEvaluator.quantileReturns(AlphaContext ctx, AlphaFactor factor, int startIndex, int horizon, int quantiles)	Buckets each evaluation date's cross-section into quantiles score-ranked groups and averages forward returns per group.
static AlphaBacktester.Result	AlphaBacktester.run(AlphaContext ctx, AlphaFactor factor, AlphaBacktester.Config config)	Runs with the standard z-score construction (gross 1.0, 5% name cap).
static AlphaBacktester.Result	AlphaBacktester.run(AlphaContext ctx, AlphaFactor factor, AlphaBacktester.Config config, AlphaBacktester.RiskOptions options)	Runs with the risk-model-driven turnover-aware optimiser as the construction (see AlphaBacktester.RiskOptions).
static AlphaBacktester.Result	AlphaBacktester.run(AlphaContext ctx, AlphaFactor factor, AlphaBacktester.Config config, AlphaBacktester.WeightBuilder builder)	Runs with a caller-supplied construction pipeline.
static AlphaBacktester.Traced	AlphaBacktester.runTraced(AlphaContext ctx, AlphaFactor factor, AlphaBacktester.Config config, AlphaBacktester.RiskOptions options)	AlphaBacktester.run(AlphaContext, AlphaFactor, Config, RiskOptions) with the per-bar trace.
static AlphaBacktester.Traced	AlphaBacktester.runTraced(AlphaContext ctx, AlphaFactor factor, AlphaBacktester.Config config, AlphaBacktester.WeightBuilder builder)	AlphaBacktester.run(AlphaContext, AlphaFactor, Config, WeightBuilder) with the per-bar trace.
double[]	AlphaCombiner.Combination.scores(AlphaContext ctx, int index)	Combined score: Σ w_k z'_k with the same per-date orthogonalisation as the fit; NaN where any factor has no score.
double[]	AlphaFactor.scores(AlphaContext ctx, int index)	Raw scores at index, aligned with the context's symbols.
static double[]	PortfolioConstruction.sectorNeutralize(AlphaContext ctx, double[] weights, Map<String,String> sectorBySymbol)	PortfolioConstruction.sectorNeutralize(double[], String[]) with alignment by construction: sector labels come as a map keyed by symbol and are resolved against the context's frozen (sorted!)
static double[]	PortfolioConstruction.trailingBetas(AlphaContext ctx, int index, int lookback)	Trailing OLS betas against a leave-one-out equal-weight market: for symbol i the market return is the mean of the other names' returns.
static double[]	PortfolioConstruction.trailingBetas(AlphaContext ctx, int index, int lookback, BarSeries market)	Trailing OLS betas against an external market series aligned with the panel (same bar count and timestamps), for example an index.
static double[]	PortfolioConstruction.trailingVols(AlphaContext ctx, int index, int lookback)	Trailing return volatilities per symbol at index (sample sd of the close-to-close returns ending at bars index−lookback+1 .. index), the standard input to PortfolioConstruction.inverseVolBudget(double[], double[], double).
static AlphaValidation.WalkForwardResult	AlphaValidation.walkForward(AlphaContext ctx, List<AlphaFactor> candidates, int horizon, int startIndex, int trainBars, int testBars)	Rolls a train/test split across the sample: each fold picks the candidate with the best training-window mean IC and scores it on the next testBars bars.
double[]	AlphaBacktester.WeightBuilder.weights(AlphaContext ctx, double[] scores, int index)