Uses of Record Class
com.quantfinlib.backtest.PerformanceMetrics

Packages that use PerformanceMetrics
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.
com.quantfinlib.backtest
Bar-based backtesting.
com.quantfinlib.backtest.portfolio
Multi-asset backtesting: PortfolioBacktester rebalances long/short weight targets from a PortfolioStrategy with commission and turnover tracking (input series must be index-aligned — see data.SeriesAligner), and in its survivorship-aware overload consumes a data.PointInTimeUniverse (delistings terminate at the delisting return, mergers convert at deal terms, index drops force sales) plus explicit ex-date cash dividends; CrossSectionalMomentum is the built-in point-in-time factor strategy (12-1 momentum ranked over the members alive at each rebalance); PositionSizing supplies Kelly, fixed-fractional risk, inverse-volatility weights and vol-target leverage.
com.quantfinlib.backtest.tick
Event-driven tick-level backtesting — the level below bars, where queue position decides whether a passive order actually trades: TickBacktester replays QFLT tick files through a TickStrategy with microstructure-aware fills (market orders pay half the spread; limit orders fill on trade-through or earn fills print-by-print against a simulated queue; optional tick-size grid for real-world prices; no fills against the triggering print).
com.quantfinlib.backtest.validation
The defense against overfit backtests: ParameterGrid + GridSearchOptimizer enumerate and rank parameter combinations; WalkForwardAnalyzer optimizes on rolling train windows and evaluates on unseen test windows, stitching out-of-sample equity (capital carries across folds) and reporting the walk-forward efficiency ratio; SharpeValidation applies the Bailey/López de Prado probabilistic and deflated Sharpe — the multiple-testing haircut for grid-picked winners — plus the minimum track record length (how many periods before the record MEANS something, in closed form); BlockBootstrap hands the backtest Sharpe its sampling DISTRIBUTION (stationary Politis-Romano blocks — an iid resample destroys the autocorrelation and understates the uncertainty, the classic route to false confidence): the honest question is whether the 5th percentile is still positive, not whether 1.2 is a good number.
com.quantfinlib.report
Professional report generation, all writers hand-rolled on the JDK: ReportGenerator assembles portfolio, performance, risk, allocation, trade, Monte Carlo and technical sections into a Report, exported as HTML (with inline SvgCharts equity/drawdown charts), CSV, PDF, or XLSX.