Uses of Package
com.quantfinlib.data

Packages that use com.quantfinlib.data
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.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.data
Data in, out, and preparation — the bridge between real-world files/feeds and the analytics stack: CsvBarLoader (RFC-4180-tolerant CSV bars), HttpBarFetcher (CSV over HTTP), TickFileWriter/TickFileReader (QFLT binary tick format with as-fast-as-possible or paced replay), TickCapture (record the live bus for deterministic replay), SeriesAligner (timestamp intersection / union+forward-fill for ragged multi-asset data) CorporateActions (split/dividend back-adjustment) and PointInTimeUniverse (as-of membership + delisting/merger terminal events — the engine half of survivorship-bias-free backtesting, consumed by the universe-aware PortfolioBacktester overload and StockScreener.membersAsOf) with UniverseCsvLoader as its documented CSV interchange format for user-supplied membership/lifecycle data.
com.quantfinlib.screener
Stock screening: StockScreener applies composable ScreenFilters — TechnicalFilters (RSI, moving averages, MACD, ADX, VWAP, SuperTrend, Bollinger, Ichimoku, breakouts, volume spikes, gaps, 52-week levels) and FundamentalFilters (market cap, P/E, P/B, EPS, ROE, dividend yield, leverage) — then ranks survivors with the weighted min-max RankingEngine and exports to CSV.