Uses of Package
com.quantfinlib.alpha
Packages that use com.quantfinlib.alpha
Package
Description
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.
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Classes in com.quantfinlib.alpha used by com.quantfinlib.alphaClassDescriptionNet/gross curves plus the cumulative fraction of equity each cost component consumed — the cost autopsy.Builds target weights from raw scores at a rebalance (the construction hook).The research dataset an alpha factor operates on: an index-aligned panel of price series over a fixed symbol order, with optional fundamentals.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: per-bar residual alpha, factor betas, and fit quality.IC per horizon plus the interpolated half-life of the shortest-horizon IC.Per-block ICs with their dispersion — consistency across regimes.One walk-forward fold: what was chosen, and how it did out of sample.Observed mean IC against its permutation null distribution.IC across the sweep plus the worst adjacent-parameter drop.All folds plus the aggregate in-sample vs out-of-sample comparison.Per-day-of-week profile, indexed Monday = 0 … Sunday = 6.The turn-of-month split, with a Welch t-stat on the difference.Mean forward return per score quantile — the picture behind the IC:
meanReturns()[0]is the average forward return of the lowest-scored names, the last entry of the highest-scored, andSignalEvaluator.QuantileReport.spread()is the top-minus-bottom long/short return per period.The evaluation scorecard;SignalEvaluator.Report.format()renders it for humans.