kingchenc
fcb221ec03
Adds 19 streaming indicators so an external trading-bot feature extractor can replace its hand-built features with native, batch/streaming-equivalent ones. Each is a real gap (verified against the existing catalogue), production-only, with full Python/Node/WASM bindings, fuzz drivers, and tests. Five commits, one per family group; counter 377 -> 396. ## What's added **Price Statistics (6)** — `LogReturn`, `RealizedVolatility` (raw quadratic variation, the un-annualised counterpart to `HistoricalVolatility`), `RollingQuantile`, `RollingIqr`, `RollingPercentileRank`, `SpreadAr1Coefficient` (pairwise AR(1) rho of the spread; complements `OuHalfLife`). **Price Action (4)** — `CloseVsOpen`, `BodySizePct`, `WickRatio`, `HighLowRange` (stateless per-bar OHLC transforms). **Regime / Trend / Jump labels (3)** — `TrendLabel` (sign of the rolling OLS slope), `JumpIndicator` (return outliers vs trailing volatility, measured as deviation from the trailing mean so steady drift is not flagged), `RegimeLabel` (volatility-quantile regime split). **Risk / Performance (2)** — `WinRate`, `Expectancy` (R-multiple). **Microstructure (4)** — `OrderFlowImbalance` (Cont-Kukanov-Stoikov OFI), `Vpin`, `AmihudIlliquidity`, `RollMeasure`. These reuse the existing `OrderBook` / `Trade` inputs (no new input type). ## Intentionally NOT added (already present, would be duplicates) - **Population skew / kurtosis** — `skewness.rs` / `kurtosis.rs` are already population moments (divisor n). - **Hurst R/S** — `hurst_exponent.rs` already uses rescaled-range (R/S) analysis. - **Queue Imbalance** — exactly `OrderBookImbalanceTop1` ((bidSize - askSize) / (bidSize + askSize)). ## Verification `cargo test -p wickra-core` (lib 3187 + doc 354), `cargo clippy --workspace --all-targets --all-features -D warnings` clean, node `npm run build && npm test` (471), python `pytest` (784). Counter consistent across `mod.rs`, lib block, README, and docs/README at 396.
83 lines
3.3 KiB
Rust
83 lines
3.3 KiB
Rust
#![no_main]
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//! Fuzz two-input `Indicator<(f64, f64)>` implementations with arbitrary
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//! `(asset, benchmark)` return pairs.
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//!
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//! Each iteration consumes a byte stream and interprets it as a sequence of
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//! `(f64, f64)` pairs (8 bytes per `f64`), then drives every two-series
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//! indicator over the sequence both streaming and as a batch. No path may
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//! panic.
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use libfuzzer_sys::fuzz_target;
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use wickra_core::{Alpha, BatchExt, BetaNeutralSpread, Cointegration, DistanceSsd, GrangerCausality, Indicator, InformationRatio, KalmanHedgeRatio, LeadLagCrossCorrelation, OuHalfLife, PairSpreadZScore, PairwiseBeta, RelativeStrengthAB, RollingCorrelation, RollingCovariance, SpreadAr1Coefficient, SpreadBollingerBands, SpreadHurst, TreynorRatio, VarianceRatio};
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#[inline(never)]
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fn drive<I>(make: impl Fn() -> I, data: &[(f64, f64)])
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where
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I: Indicator<Input = (f64, f64), Output = f64> + BatchExt,
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{
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let mut streaming = make();
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for &x in data {
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let _ = streaming.update(x);
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}
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let _ = make().batch(data);
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}
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fuzz_target!(|data: &[u8]| {
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// Pack two consecutive 8-byte chunks into one `(f64, f64)` pair.
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let pairs: Vec<(f64, f64)> = data
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.chunks_exact(16)
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.map(|c| {
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let a = f64::from_le_bytes(c[..8].try_into().expect("8 bytes"));
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let b = f64::from_le_bytes(c[8..].try_into().expect("8 bytes"));
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(a, b)
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})
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.collect();
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drive(|| TreynorRatio::new(10, 0.0).unwrap(), &pairs);
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drive(|| InformationRatio::new(10).unwrap(), &pairs);
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drive(|| Alpha::new(10, 0.0).unwrap(), &pairs);
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drive(|| PairwiseBeta::new(10).unwrap(), &pairs);
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drive(|| PairSpreadZScore::new(10, 10).unwrap(), &pairs);
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drive(|| RollingCorrelation::new(20).unwrap(), &pairs);
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drive(|| RollingCovariance::new(20).unwrap(), &pairs);
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drive(|| OuHalfLife::new(60).unwrap(), &pairs);
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drive(|| SpreadHurst::new(60).unwrap(), &pairs);
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drive(|| DistanceSsd::new(20).unwrap(), &pairs);
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drive(|| BetaNeutralSpread::new(20).unwrap(), &pairs);
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drive(|| VarianceRatio::new(60, 2).unwrap(), &pairs);
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drive(|| GrangerCausality::new(60, 1).unwrap(), &pairs);
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drive(|| SpreadAr1Coefficient::new(40).unwrap(), &pairs);
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// Struct-output pair indicator: drive update + batch directly (the generic
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// `drive` above only covers `Output = f64`).
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let mut ll = LeadLagCrossCorrelation::new(8, 3).unwrap();
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for &x in &pairs {
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let _ = ll.update(x);
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}
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let _ = LeadLagCrossCorrelation::new(8, 3).unwrap().batch(&pairs);
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let mut co = Cointegration::new(12, 1).unwrap();
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for &x in &pairs {
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let _ = co.update(x);
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}
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let _ = Cointegration::new(12, 1).unwrap().batch(&pairs);
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let mut rs = RelativeStrengthAB::new(10, 14).unwrap();
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for &x in &pairs {
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let _ = rs.update(x);
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}
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let _ = RelativeStrengthAB::new(10, 14).unwrap().batch(&pairs);
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let mut kalman_hedge_ratio = KalmanHedgeRatio::new(0.001, 0.001).unwrap();
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for &x in &pairs {
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let _ = kalman_hedge_ratio.update(x);
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}
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let _ = KalmanHedgeRatio::new(0.001, 0.001).unwrap().batch(&pairs);
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let mut spread_bollinger_bands = SpreadBollingerBands::new(20, 2.0).unwrap();
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for &x in &pairs {
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let _ = spread_bollinger_bands.update(x);
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}
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let _ = SpreadBollingerBands::new(20, 2.0).unwrap().batch(&pairs);
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});
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