Uses of Package
com.quantfinlib.alpha
Packages that use com.quantfinlib.alpha
Package
Description
Cross-sectional alpha research: signal generation, evaluation,
validation, execution-aware backtesting, portfolio construction and
reporting, each a separate step.
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Classes in com.quantfinlib.alpha used by com.quantfinlib.alphaClassDescriptionBacktest settings: decision schedule, costs and capital.Net and gross curves (one point per close from
startIndex), the cumulative fraction of equity each cost consumed, and turnover.Risk-model-driven construction forAlphaBacktester.run(AlphaContext, AlphaFactor, Config, RiskOptions): at each rebalance aFactorRiskModelis rebuilt from the panel up to the decision bar (AlphaRiskModels) and the book comes fromTurnoverAwareOptimizerstarted at the drifted book actually held.A run with its per-bar trace: the book held over each close-to-close bar, each name's P&L, the cost and the financing, all as fractions of equity at the start of the bar.Builds target weights from raw scores at a rebalance (the construction hook).Fitted weights and the combined factor.Combination settings.The research dataset an alpha factor operates on: an index-aligned panel of price series over a fixed symbol order, with optional fundamentals and an optional point-in-time universe.A cross-sectional alpha factor: at a bar index, one raw score per symbol, where higher = more attractive to own (buy high scores, sell low).OLS attribution with standard errors.Mean IC per cumulative horizon, plus the horizon where it first falls below half the shortest-horizon value.Lagged-return IC profile with its exponential fit.Risk model specification.Per-block ICs with their dispersion: consistency across regimes.One walk-forward fold.Which link the null distribution destroys.Observed mean IC against its null distribution.IC across the sweep.All folds plus the aggregate in-sample vs out-of-sample comparison.P&L attribution of a traced backtest over time: factor, specific, trading, cost and financing components per bar, with long/short leg and sector splits.Per-day-of-week profile, indexed Monday = 0 … Sunday = 6.The turn-of-month split.Strategy capacity: net Sharpe ratio against book size.Point-in-time fundamentals: every record is keyed by the time it became public, andFundamentalsHistory.asOf(String, long)returns only what was published by then.One published record.A constructed book with what it actually achieved.Mean forward return per score quantile:meanReturns()[0]is the lowest-scored names' average forward return, the last entry the highest-scored.The evaluation scorecard;SignalEvaluator.Report.format()renders it.Optimiser settings.