kingchenc
389200f855
Deepens the **Price Statistics** family (B9) with five rolling-statistics indicators (447 -> 452): - **ShannonEntropy** — Shannon entropy of a binned rolling value distribution. - **SampleEntropy** — Richman-Moorman sample entropy (regularity/complexity of a window). - **KendallTau** — Kendall rank correlation (tau-b) over paired observations (pairwise; distinct from Pearson/Spearman). - **JarqueBera** — Jarque-Bera normality test statistic over a rolling window. - **RollingMinMaxScaler** — maps the latest value to 0..1 over a rolling window. All scalar f64 input except KendallTau (pairwise). Multi-arg scalars (Shannon/Sample entropy) use hand-written Python/Node bindings + the variadic wasm macro; KendallTau uses the pair macros. Verified locally: 3668 core lib + 410 doc tests, clippy clean, 527 node tests, 871 pytest, counter 452.
84 lines
3.4 KiB
Rust
84 lines
3.4 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, KendallTau, 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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drive(|| KendallTau::new(20).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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