Add 10 pairwise stat-arb indicators to Price Statistics (#154)

Adds ten pairwise `(f64, f64)` indicators to the **Price Statistics** family, completing the A1 stat-arb expansion block. ## Indicators **Scalar output:** - **RollingCorrelation** — rolling Pearson correlation of period-over-period *returns* (distinct from level-based `PearsonCorrelation`). - **RollingCovariance** — rolling covariance of returns. - **OuHalfLife** — Ornstein–Uhlenbeck half-life of mean reversion of the spread `a − b`. - **SpreadHurst** — Hurst exponent of the spread (variance-of-lagged-differences fit) for regime detection. - **DistanceSsd** — Gatev sum-of-squared-deviations between two start-normalised series. - **BetaNeutralSpread** — rolling OLS regression residual `a − (α + β·b)`. - **VarianceRatio** — Lo–MacKinlay variance-ratio test on the spread (two params: `period`, `q`). - **GrangerCausality** — F-statistic for whether `b` predicts `a` (two params: `period`, `lag`). **Struct output (custom bindings):** - **KalmanHedgeRatio** — dynamic hedge ratio via a Kalman filter → `{ hedgeRatio, intercept, spread }`. - **SpreadBollingerBands** — Bollinger bands on the spread → `{ middle, upper, lower, percentB }`. ## Notes - No new traits or input families: all use the native `Indicator<Input = (f64, f64)>` (precedent `Beta`, `Cointegration`). - Adds `Error::InvalidParameter` for floating-point constructor parameters (Kalman `delta`/`observation_var`, `num_std`). - Full Python/Node/WASM bindings; the two struct-output indicators are hand-written, the rest use the pair macros. - Indicator count 315 → 325; README, family rows, `__init__`, fuzz target, and CHANGELOG updated. ## Verification - `cargo test --workspace --all-features` — green (2676 core lib + 308 doc). - `cargo clippy --workspace --all-targets --all-features -- -D warnings` — clean. - Node: `npm run build && npm test` — 410 passing (`index.d.ts`/`index.js` regenerated). - Python: `pytest` — 684 passing.
2026-06-03 15:39:55 +02:00
parent 53941b7b07
commit a3a1ae4dba
25 changed files with 4313 additions and 51 deletions
@@ -531,6 +531,24 @@ wasm_pair_indicator!(
    "SpearmanCorrelation",
    wc::SpearmanCorrelation
 );
+wasm_pair_indicator!(
+    WasmRollingCorrelation,
+    "RollingCorrelation",
+    wc::RollingCorrelation
+);
+wasm_pair_indicator!(
+    WasmRollingCovariance,
+    "RollingCovariance",
+    wc::RollingCovariance
+);
+wasm_pair_indicator!(WasmOuHalfLife, "OuHalfLife", wc::OuHalfLife);
+wasm_pair_indicator!(WasmSpreadHurst, "SpreadHurst", wc::SpreadHurst);
+wasm_pair_indicator!(WasmDistanceSsd, "DistanceSsd", wc::DistanceSsd);
+wasm_pair_indicator!(
+    WasmBetaNeutralSpread,
+    "BetaNeutralSpread",
+    wc::BetaNeutralSpread
+);

 // ---------- PairSpreadZScore (two params) ----------

@@ -748,6 +766,210 @@ impl WasmRelativeStrengthAb {
    }
 }

+// ---------- VarianceRatio (two params) ----------
+
+#[wasm_bindgen(js_name = "VarianceRatio")]
+pub struct WasmVarianceRatio {
+    inner: wc::VarianceRatio,
+}
+
+#[wasm_bindgen(js_class = "VarianceRatio")]
+impl WasmVarianceRatio {
+    #[wasm_bindgen(constructor)]
+    pub fn new(period: usize, q: usize) -> Result<WasmVarianceRatio, JsError> {
+        Ok(Self {
+            inner: wc::VarianceRatio::new(period, q).map_err(map_err)?,
+        })
+    }
+    pub fn update(&mut self, a: f64, b: f64) -> Option<f64> {
+        self.inner.update((a, b))
+    }
+    /// Batch over two equally-sized arrays of prices. Returns one `f64` per
+    /// input position (`NaN` during warmup).
+    pub fn batch(&mut self, a: &[f64], b: &[f64]) -> Result<Float64Array, JsError> {
+        if a.len() != b.len() {
+            return Err(JsError::new("a and b must be equal length"));
+        }
+        let mut out = Vec::with_capacity(a.len());
+        for i in 0..a.len() {
+            out.push(self.inner.update((a[i], b[i])).unwrap_or(f64::NAN));
+        }
+        Ok(Float64Array::from(out.as_slice()))
+    }
+    pub fn reset(&mut self) {
+        self.inner.reset();
+    }
+    #[wasm_bindgen(js_name = isReady)]
+    pub fn is_ready(&self) -> bool {
+        self.inner.is_ready()
+    }
+    #[wasm_bindgen(js_name = warmupPeriod)]
+    pub fn warmup_period(&self) -> usize {
+        self.inner.warmup_period()
+    }
+}
+
+// ---------- GrangerCausality (two params) ----------
+
+#[wasm_bindgen(js_name = "GrangerCausality")]
+pub struct WasmGrangerCausality {
+    inner: wc::GrangerCausality,
+}
+
+#[wasm_bindgen(js_class = "GrangerCausality")]
+impl WasmGrangerCausality {
+    #[wasm_bindgen(constructor)]
+    pub fn new(period: usize, lag: usize) -> Result<WasmGrangerCausality, JsError> {
+        Ok(Self {
+            inner: wc::GrangerCausality::new(period, lag).map_err(map_err)?,
+        })
+    }
+    pub fn update(&mut self, a: f64, b: f64) -> Option<f64> {
+        self.inner.update((a, b))
+    }
+    /// Batch over two equally-sized arrays of prices. Returns one `f64` per
+    /// input position (`NaN` during warmup).
+    pub fn batch(&mut self, a: &[f64], b: &[f64]) -> Result<Float64Array, JsError> {
+        if a.len() != b.len() {
+            return Err(JsError::new("a and b must be equal length"));
+        }
+        let mut out = Vec::with_capacity(a.len());
+        for i in 0..a.len() {
+            out.push(self.inner.update((a[i], b[i])).unwrap_or(f64::NAN));
+        }
+        Ok(Float64Array::from(out.as_slice()))
+    }
+    pub fn reset(&mut self) {
+        self.inner.reset();
+    }
+    #[wasm_bindgen(js_name = isReady)]
+    pub fn is_ready(&self) -> bool {
+        self.inner.is_ready()
+    }
+    #[wasm_bindgen(js_name = warmupPeriod)]
+    pub fn warmup_period(&self) -> usize {
+        self.inner.warmup_period()
+    }
+}
+
+// ---------- KalmanHedgeRatio (two params, object output) ----------
+
+#[wasm_bindgen(js_name = "KalmanHedgeRatio")]
+pub struct WasmKalmanHedgeRatio {
+    inner: wc::KalmanHedgeRatio,
+}
+
+#[wasm_bindgen(js_class = "KalmanHedgeRatio")]
+impl WasmKalmanHedgeRatio {
+    #[wasm_bindgen(constructor)]
+    pub fn new(delta: f64, observation_var: f64) -> Result<WasmKalmanHedgeRatio, JsError> {
+        Ok(Self {
+            inner: wc::KalmanHedgeRatio::new(delta, observation_var).map_err(map_err)?,
+        })
+    }
+    /// Returns `{ hedgeRatio, intercept, spread }`, or `null` during warmup.
+    pub fn update(&mut self, a: f64, b: f64) -> JsValue {
+        match self.inner.update((a, b)) {
+            Some(o) => {
+                let obj = Object::new();
+                Reflect::set(&obj, &"hedgeRatio".into(), &o.hedge_ratio.into()).ok();
+                Reflect::set(&obj, &"intercept".into(), &o.intercept.into()).ok();
+                Reflect::set(&obj, &"spread".into(), &o.spread.into()).ok();
+                obj.into()
+            }
+            None => JsValue::NULL,
+        }
+    }
+    /// Flat `Float64Array` of length `3 * n`:
+    /// `[hedgeRatio0, intercept0, spread0, hedgeRatio1, ...]`. Warmup rows are NaN.
+    pub fn batch(&mut self, a: &[f64], b: &[f64]) -> Result<Float64Array, JsError> {
+        if a.len() != b.len() {
+            return Err(JsError::new("a and b must be equal length"));
+        }
+        let n = a.len();
+        let mut out = vec![f64::NAN; n * 3];
+        for i in 0..n {
+            if let Some(o) = self.inner.update((a[i], b[i])) {
+                out[i * 3] = o.hedge_ratio;
+                out[i * 3 + 1] = o.intercept;
+                out[i * 3 + 2] = o.spread;
+            }
+        }
+        Ok(Float64Array::from(out.as_slice()))
+    }
+    pub fn reset(&mut self) {
+        self.inner.reset();
+    }
+    #[wasm_bindgen(js_name = isReady)]
+    pub fn is_ready(&self) -> bool {
+        self.inner.is_ready()
+    }
+    #[wasm_bindgen(js_name = warmupPeriod)]
+    pub fn warmup_period(&self) -> usize {
+        self.inner.warmup_period()
+    }
+}
+
+// ---------- SpreadBollingerBands (two params, object output) ----------
+
+#[wasm_bindgen(js_name = "SpreadBollingerBands")]
+pub struct WasmSpreadBollingerBands {
+    inner: wc::SpreadBollingerBands,
+}
+
+#[wasm_bindgen(js_class = "SpreadBollingerBands")]
+impl WasmSpreadBollingerBands {
+    #[wasm_bindgen(constructor)]
+    pub fn new(period: usize, num_std: f64) -> Result<WasmSpreadBollingerBands, JsError> {
+        Ok(Self {
+            inner: wc::SpreadBollingerBands::new(period, num_std).map_err(map_err)?,
+        })
+    }
+    /// Returns `{ middle, upper, lower, percentB }`, or `null` during warmup.
+    pub fn update(&mut self, a: f64, b: f64) -> JsValue {
+        match self.inner.update((a, b)) {
+            Some(o) => {
+                let obj = Object::new();
+                Reflect::set(&obj, &"middle".into(), &o.middle.into()).ok();
+                Reflect::set(&obj, &"upper".into(), &o.upper.into()).ok();
+                Reflect::set(&obj, &"lower".into(), &o.lower.into()).ok();
+                Reflect::set(&obj, &"percentB".into(), &o.percent_b.into()).ok();
+                obj.into()
+            }
+            None => JsValue::NULL,
+        }
+    }
+    /// Flat `Float64Array` of length `4 * n`:
+    /// `[middle0, upper0, lower0, percentB0, middle1, ...]`. Warmup rows are NaN.
+    pub fn batch(&mut self, a: &[f64], b: &[f64]) -> Result<Float64Array, JsError> {
+        if a.len() != b.len() {
+            return Err(JsError::new("a and b must be equal length"));
+        }
+        let n = a.len();
+        let mut out = vec![f64::NAN; n * 4];
+        for i in 0..n {
+            if let Some(o) = self.inner.update((a[i], b[i])) {
+                out[i * 4] = o.middle;
+                out[i * 4 + 1] = o.upper;
+                out[i * 4 + 2] = o.lower;
+                out[i * 4 + 3] = o.percent_b;
+            }
+        }
+        Ok(Float64Array::from(out.as_slice()))
+    }
+    pub fn reset(&mut self) {
+        self.inner.reset();
+    }
+    #[wasm_bindgen(js_name = isReady)]
+    pub fn is_ready(&self) -> bool {
+        self.inner.is_ready()
+    }
+    #[wasm_bindgen(js_name = warmupPeriod)]
+    pub fn warmup_period(&self) -> usize {
+        self.inner.warmup_period()
+    }
+}
+
 // ---------- KAMA (three params) ----------

 #[wasm_bindgen(js_name = KAMA)]