kingchenc
e385734275
## B15 Microstructure — three new indicators (485 → 488) | Indicator | Input | Output | Notes | |-----------|-------|--------|-------| | `TradeSignAutocorrelation` | `Trade` | `f64` ∈ [-1,1] | lag-1 autocorrelation of the signed aggressor (order-flow persistence) | | `Pin` | `Trade` | `f64` ∈ [0,1] | probability of informed trading from rolling buy/sell imbalance (EKOP single-window estimator); `name()` = `"PIN"` | | `HasbrouckInformationShare` | `(f64, f64)` | `f64` ∈ [0,1] | variance-ratio proxy for each venue's share of price discovery | ### Wiring - Core structs + full unit tests (every branch). - Hand-written Python/Node/WASM bindings for the two `Trade`-input indicators (precedent `TradeImbalance`); `node_pair_indicator!` / `wasm_pair_indicator!` macro bindings + hand Python pyclass for the pairwise Hasbrouck (precedent `RollingCorrelation`). - Fuzz drives added to `indicator_update_trade.rs` and `indicator_update_pair.rs`. - Dedicated Python + Node streaming-vs-batch and reference tests; Hasbrouck in the `PAIR` registry. - README counter (3 spots) + `docs/README.md` + `FAMILIES` assert bumped to 488. ### Verify (all green, local) - `cargo test -p wickra-core --lib`: 3991 passed - `cargo test -p wickra-core --doc`: 438 passed - `cargo clippy --workspace --all-targets --all-features -- -D warnings`: clean - node: 561 passed · pytest: 926 passed
85 lines
3.4 KiB
Rust
85 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, HasbrouckInformationShare, 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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drive(|| HasbrouckInformationShare::new(2).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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