Uses of Interface
com.quantfinlib.alpha.AlphaFactor

Packages that use AlphaFactor

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 AlphaFactor in com.quantfinlib.alpha

Classes in com.quantfinlib.alpha that implement AlphaFactor

Modifier and Type	Class	Description
static final class	AlphaCombiner.Combination	Fitted weights and the combined factor.

Methods in com.quantfinlib.alpha that return AlphaFactor

Modifier and Type	Method	Description
static AlphaFactor	Factors.bollinger(int period, double stdDevs)	Bollinger mean reversion: −(close − SMA) / (k·σ) — the negative band position, +1 at the lower band, −1 at the upper.
static AlphaFactor	Factors.lowVolatility(int lookback)	Low-volatility anomaly: −σ(returns) over the lookback, calm names scoring high.
static AlphaFactor	Factors.macd(int fast, int slow, int signal)	MACD histogram normalized by price: (macdLine − signalLine) / close.
static AlphaFactor	Factors.meanReversion(int lookback)	Plain mean reversion: −(close / SMA − 1) — how far the price sits below its own average, as a fraction.
static AlphaFactor	Factors.momentum(int lookback, int skip)	Cross-sectional momentum: close[i−skip] / close[i−lookback] − 1.
static AlphaFactor	Factors.movingAverageCrossover(int fast, int slow)	Moving-average crossover: (SMA_fast − SMA_slow) / SMA_slow.
static AlphaFactor	Factors.prepared(AlphaFactor factor, double p, Map<String,String> sectorBySymbol)	The standard preparation chain: Factors.winsorized(AlphaFactor, double) at p, then Factors.standardized(AlphaFactor), then Factors.sectorNeutralized(AlphaFactor, Map) (skipped when sectorBySymbol is null).
static AlphaFactor	Factors.quality()	Quality composite: profitability minus leverage — ROE − 0.1 × debt/equity.
static AlphaFactor	Factors.rsi(int period)	Contrarian RSI: (50 − RSI) / 50, in [−1, +1].
static AlphaFactor	Factors.sectorNeutralized(AlphaFactor factor, Map<String,String> sectorBySymbol)	Demeans scores within each sector per date, so the factor ranks names against their sector peers.
static AlphaFactor	Factors.standardized(AlphaFactor factor)	Cross-sectional z-score per date: (s − mean)/sd over the scored names (population sd); all-zero when the scores have no dispersion.
static AlphaFactor	Factors.value()	Value composite: the average of earnings yield and book yield (1/PB).
static AlphaFactor	Factors.winsorized(AlphaFactor factor, double p)	Clips each date's scores to the cross-sectional p and 1 − p quantiles (linear interpolation between order statistics), NaN entries untouched.

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

Modifier and Type	Method	Description
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 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 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 AlphaFactor	Factors.prepared(AlphaFactor factor, double p, Map<String,String> sectorBySymbol)	The standard preparation chain: Factors.winsorized(AlphaFactor, double) at p, then Factors.standardized(AlphaFactor), then Factors.sectorNeutralized(AlphaFactor, Map) (skipped when sectorBySymbol is null).
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.
static AlphaFactor	Factors.sectorNeutralized(AlphaFactor factor, Map<String,String> sectorBySymbol)	Demeans scores within each sector per date, so the factor ranks names against their sector peers.
static AlphaFactor	Factors.standardized(AlphaFactor factor)	Cross-sectional z-score per date: (s − mean)/sd over the scored names (population sd); all-zero when the scores have no dispersion.
static AlphaFactor	Factors.winsorized(AlphaFactor factor, double p)	Clips each date's scores to the cross-sectional p and 1 − p quantiles (linear interpolation between order statistics), NaN entries untouched.

Method parameters in com.quantfinlib.alpha with type arguments of type AlphaFactor

Modifier and Type	Method	Description
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 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 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.