Uses of Class
com.quantfinlib.alpha.AlphaContext

Packages that use AlphaContext

Package

Description

com.quantfinlib.alpha

The alpha research pipeline — signal to evaluated, validated, cost-aware, constructed, reported strategy, with each stage a separate, composable step: Signal generation — Factors: nine standard factors (MA crossover, contrarian RSI, MACD, Bollinger reversion, mean reversion, 12-1 momentum, value, quality, low volatility) producing raw cross-sectional scores over an AlphaContext panel; Evaluation — SignalEvaluator: rank IC, IR, t-stat, hit rate, turnover, cross-factor exposure — the cheap filter before any backtest; Validation — AlphaValidation: walk-forward selection with OOS efficiency, blocked k-fold consistency, Monte Carlo permutation p-values, parameter sensitivity — the overfitting defense; Execution-aware backtest — AlphaBacktester: commission, bid-ask spread, slippage and square-root market impact (microstructure.MarketImpactModel), with gross-vs-net cost decomposition; Portfolio construction — PortfolioConstruction: z-score sizing with caps, inverse-vol risk budgeting, sector and beta neutralization, mean-variance tilt; Reporting — AlphaReport: alpha decay with half-life, OLS factor attribution, drawdown curves, rolling Sharpe, and the shared ratio set from backtest.PerformanceAnalytics.

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 fundamentals snapshot for value/quality factors.
AlphaContext	AlphaContext.withUniverse(PointInTimeUniverse universe)	The same panel with a point-in-time universe attached: built-in factors then score non-members as NaN per bar (see the class doc).

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

Modifier and Type	Method	Description
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)	Evaluates the factor's mean IC at each horizon.
static SignalEvaluator.Report	SignalEvaluator.evaluate(AlphaContext ctx, AlphaFactor factor, int startIndex, int horizon)	Evaluates a factor over [startIndex, ctx.bars() − horizon), stepping by horizon (non-overlapping forward windows).
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 AlphaValidation.RobustnessResult	AlphaValidation.monteCarloRobustness(AlphaContext ctx, AlphaFactor factor, int horizon, int startIndex, int trials, long seed)	Permutation test on the score/return pairing: per trial, scores from date tᵢ are paired with forward returns from a shuffled date tⱼ, destroying any true predictive link while preserving both marginal distributions.
static AlphaValidation.SensitivityResult	AlphaValidation.parameterSensitivity(AlphaContext ctx, List<AlphaFactor> sweep, int horizon, int startIndex)	Evaluates each candidate (an ORDERED parameter sweep — neighbors in the list must be neighbors in parameter space) and reports the worst IC drop between adjacent candidates.
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 the forward returns per group, over the same non-overlapping date grid as SignalEvaluator.evaluate(AlphaContext, AlphaFactor, int, int): dates step by horizon from startIndex, a NaN score or NaN forward return drops that (symbol, date) pair, and a date with fewer complete pairs than quantiles contributes to no bucket at all.
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.WeightBuilder builder)	Runs with a caller-supplied construction pipeline.
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 of each symbol against the equal-weight universe return — the in-panel market proxy when no index series is supplied.
static double[]	PortfolioConstruction.trailingVols(AlphaContext ctx, int index, int lookback)	Trailing return volatilities per symbol at index — the standard input to PortfolioConstruction.inverseVolBudget(double[], double[], double) (per-bar σ; the common scale cancels in the renormalization).
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 unseen bars.
double[]	AlphaBacktester.WeightBuilder.weights(AlphaContext ctx, double[] scores, int index)